Technology Advancements and the Needs of Farmers: Mapping Gaps and Opportunities in Row Crop Farming

Abstract

Increased food production demands, labor shortages, and environmental concerns are driving the need for innovative agricultural technologies. However, effective adoption depends critically on aligning robot innovations with the needs of farmers. This paper examines the alignment between the needs of farmers and the robotic systems used in row crop farming. We review current commercial agricultural robots and research, and map these to the needs of farmers, as expressed in the literature, to identify the key issues holding back large-scale adoption. From initial pool of 184 research articles, 19 survey articles, and 82 commercial robotic solutions, we selected 38 peer-reviewed academic studies, 12 survey articles, and 18 commercially available robots for in-depth review and analysis for this study. We identify the key challenges faced by farmers and map them directly to the current and emerging capabilities of agricultural robots. We supplement the data gathered from the literature review of surveys and case studies with in-depth interviews with nine farmers to obtain deeper insights into the needs and day-to-day operations. Farmers reported mixed reactions to current technologies, acknowledging efficiency improvements but highlighting barriers such as capital costs, technical complexity, and inadequate support systems. There is a notable demand for technologies for improved plant health monitoring, soil condition assessment, and enhanced climate resilience. We then review state-of-the-art robotic solutions for row crop farming and map these technological capabilities to the farmers’ needs. Only technologies with field validation or operational deployment are included, to ensure practical relevance. These mappings generate insights that underscore the need for lightweight and modular robot technologies that can be adapted to diverse farming practices, as well as the need for farmers’ education and simpler interfaces to robotic operations and data analysis that are actionable for farmers. We conclude with recommendations for future research, emphasizing the importance of co-creation with the farming community to ensure the adoption and sustained use of agricultural robotic solutions.

1. Introduction

The global agricultural sector faces mounting pressure to sustainably increase food production due to rapid population growth, projected to reach nearly 10 billion by 2050 [1]. Concurrently, farmers grapple with escalating labor shortages, erratic weather patterns driven by climate change, and significant environmental challenges, including soil erosion and declining soil fertility [2,3]. Traditional intensive farming practices contribute significantly to soil degradation, with excessive tillage leading to erosion and diminished long-term productivity, while heavy reliance on chemical inputs exacerbates soil fertility issues and negatively impacts surrounding ecosystems [4]. These environmental pressures, coupled with shifting weather patterns characterized by increased frequency of droughts, floods, and unpredictable rainfall, further destabilize traditional agricultural practices and necessitate the adoption of innovative solutions [5].

Robotics and automation have become pivotal in transforming agriculture into a more sustainable and efficient sector. By automating repetitive tasks and precisely applying agricultural inputs, robotics reduces labor demands and chemical usage. These advancements underpin precision agriculture, enabling targeted monitoring and tailored interventions at the plant and soil level, thereby improving soil fertility, reducing erosion, and minimizing environmental impacts [6,7,8]. Row crop farming, characterized by structured cultivation of staple crops like corn, soybeans, cotton, and wheat, offers ideal conditions for robotic automation due to its repetitive operations [9]. Integrating robotics in this context optimizes resource use, lowers operational costs, and enhances soil and crop monitoring, thus significantly improving resilience to pests, resource inefficiencies, and climate variability [8].

Several farmer operational challenges stand to benefit directly from robotic intervention [8]. Despite technological advances, significant gaps persist between robotic capabilities and farmers’ operational needs. Previous research provides critical insights into the adoption dynamics, yet often remains fragmented. Economic feasibility, including perceived costs and returns on investment, substantially influences farmer acceptance, with startups highlighting economic justification as crucial for adoption [10]. Farmer acceptance is closely tied to the perceived economic benefits and practical utility of robotics, as demonstrated by multi-stakeholder perspectives [11]. Conversely, policymakers and researchers prioritize sustainability, employment implications, and data privacy, illustrating differing stakeholder perspectives. Technological bottlenecks, notably in robot navigation systems, remain critical economic and operational barriers to broader implementation, demanding robust and economically viable solutions [12]. Ethical and policy dimensions further complicate adoption, as societal acceptance, ethical considerations, and long-term sustainability implications emerge prominently in discussions around agricultural robotics [13].

Technical reviews consistently underline capabilities across various robotic components, including perception systems [14], autonomous navigation [15,16], cooperative multi-robot systems [17,18,19], harvesting automation [20,21], and human–robot interaction [22]. Collectively, these analyses highlight the potential of robotics to significantly enhance precision agriculture and sustainability [7,23]. However, despite these technological advancements, limited attention has been paid to systematically aligning robotic capabilities with specific, operationally grounded farmer requirements. This gap underscores a critical disconnect between technological development and practical farm integration.

This article aims to provide a systematic mapping between farmer-identified needs in row crop agriculture and current robotic technological capabilities by combining insights from surveys and literature reviews with qualitative interviews conducted to contextualize practical farm operations. We categorize and prioritize key farmers’ needs and then systematically review the state-of-the-art commercial and academic robotic technologies, mapping their capabilities to these needs. Through this analysis, we identify areas of alignment, clarify persistent gaps, and highlight emerging opportunities for future innovation. Our aims are to guide collaborative research and innovation, ensuring that robotic technologies are practically relevant, effectively adopted, and sustainably integrated into agricultural systems. Figure 1 shows the framework followed for this paper. This paper addresses several key gaps in the existing literature. First, it provides a farmer-centered view of agricultural robotics by connecting user needs to system capabilities. Second, it combines both commercial and academic solutions within one comparative framework, which is not commonly conducted. Third, it uses a structured ability-level assessment to show the mismatches between technology maturity and field needs. These contributions create a more practical and adoption-focused overview compared to previous technically focused reviews.

The paper is structured as follows: Section 2 provides an overview of row crop farming, highlighting key practices and challenges. Section 3 provides detailed metholodogy for the selection of articles and commercial products. Section 4 summarizes the farmer’s needs from the literature supplemented with in-depth interviews. Section 5 reviews current robotic technologies used in row crop farming. Section 6 maps farmers’ needs to robotic capabilities, highlighting alignments and limitations. Section 7 provides the key findings and priority actions based on highlighted limitations from the previous section. Finally, Section 8 summarizes the findings, discusses limitations, and provides recommendations for future research.

2. Overview of Row Crop Farming

Row crop farming involves cultivating crops such as corn, soybeans, cotton, wheat, barley, rye, and oats in evenly spaced rows, encompassing tasks such as planting, irrigation, pest management, nutrient application, and harvesting. These crops are economically significant, essential to the global food supply, animal feed, and various industrial processes. Row crops are particularly suitable for robotic automation due to their structured cultivation methods, which include homogeneous planting of crops in rows, even row spacing, and involvement of repetitive tasks for crop management like sowing, spraying, and harvesting. These structured cultivation principles are also applied to many vegetable crops, such as lettuce, broccoli, tomatoes, and carrots, which are planted similarly in uniform rows to increase efficiency in mechanized operations such as spraying and harvesting. However, vegetables require more delicate harvesting and post-harvest handling, which pose additional automation challenges. Research progress in soft robotic grippers and selective harvesting tools is addressing these constraints in both staple and high-value row crops [20,21].

During the planting season, farmers face various operational challenges, including labor shortages, unpredictable weather patterns, pests, weed competition, soil compaction, and inadequate nutrient management. Climate change further accelerates these problems, introducing more variability and uncertainty in farming operations [24]. During peak periods, such as harvesting and planting, labor shortages lead to increased costs and reduced productivity, thereby increasing the need for innovative solutions [25]. Previously, farmers managed these challenges through manual labor, chemical use, crop rotation, and mechanical tillage. Recently, advanced approaches such as integrated pest management (IPM) [26], precision agriculture, including GPS-guided machinery, targeted nutrient application, and sustainable practices like polyculture, cover cropping, no-till farming, and regenerative agriculture, are being used to address these challenges more effectively. These approaches improve resource efficiency, sustainability, and resilience to environmental changes. Additionally, advancements in robotic systems help these approaches by facilitating precise weeding, real-time and continuous monitoring of pests and crop health, selective spraying of nutrients, and navigation with minimal soil compaction. Autonomous and regular field data collection also helps farmers respond to unexpected crop health issues and weather changes, making farming more resilient and sustainable.

Agricultural robotics presents significant opportunities to address labor constraints, increase precision, and improve environmental sustainability. However, successful adoption depends critically on aligning robotic innovations with farmers’ practical needs and operational contexts. The following sections systematically explore these alignments, identify gaps, and highlight opportunities for future technology development.

3. Methodology

This study employs a multi-step approach to examine how agricultural robotic technologies address the needs of row crop farmers. The methodology includes three parts: (1) a systematic review of the literature of academic publications, (2) a technical evaluation of commercial robotic solutions, and (3) a qualitative analysis of existing farmer survey studies in the literature complemented by in-depth interviews with farmers. We used predefined inclusion criteria to ensure rigor, relevance, and practical value for each part. The methodology, along with inclusion and exclusion criteria for each part, is described in detail in the following subsections.

3.1. Literature and Survey Review

We conducted a structured review of the literature to analyze technological advancements in agricultural robotics for row crops and to assess the needs of farmers highlighted in previous survey studies. To ensure a rigorous and comprehensive analysis, we conducted a systematic review of the literature covering publications from 2020 through April 2025. The review included peer-reviewed journal articles, conference proceedings, and commercial product documentation sourced from databases such as Google Scholar, ScienceDirect, IEEE Xplore, SpringerLink, Scopus, and commercial solutions from the Google search engine, online articles, and blogs. Targeted keywords guiding the search included “precision agriculture robots”, “row crop automation”, “robotic weeding”, “crop monitoring robots”, and “machine learning and AI in agriculture”. For the review of farmer perspectives, we selected case studies, survey papers, and stakeholder reports that focused on the use of agricultural robotics and smart farming technologies. The following inclusion criteria were used:

• For technological studies: focus specifically on robotic technologies with demonstrated applicability to row crop systems. This encompassed advances in sensor integration (e.g., multi-spectral imaging, LiDAR), AI and machine learning algorithms for decision support and automation, autonomous navigation strategies suitable for field environments, and robotic hardware optimized for energy efficiency, adaptability, and robustness in outdoor field conditions. Studies were also selected based on evidence of field deployment, operational efficiency, and sustainability benefits.
• For surveys and case studies: studies targeting row crop farmers or mixed-farm contexts, providing quantitative or qualitative data on barriers, motivations for adoption, or performance feedback, and presenting a clear methodology (sample size, geographic scope, and question framing).
• Exclusion criteria included conceptual-only research, greenhouse-specific technologies, studies focused solely on livestock, or surveys with unclear methodology or small sample sizes.

We initially retrieved 184 papers. After removing duplicates and screening abstracts, we evaluated full papers. Applying the inclusion/exclusion criteria and further assessing relevance, we selected a final sample of 38 peer-reviewed research articles and 12 farmer-focused surveys/case studies. These were used in Section 5.2 and Section 4.1, respectively. Figure 2 provides a PRISMA diagram that details the systematic selection of the literature.

3.2. Commercial Product Identification and Filtering

Similarly, we conducted a structured search to identify commercial robotic products suitable for row crop farming. We initially identified over 80 commercial robotic solutions through manufacturer websites, product launch reports, agricultural robotics blogs, and agricultural robotics events [27]. We used the following inclusion criteria:

• Proven applicability to row crop farming operations (e.g., seeding, weeding, spraying, monitoring).
• Autonomous or semi-autonomous capability.
• Field deployment or commercial availability (not just concepts or prototypes).
• Availability of technical data on sensors, capabilities, and operational features.

We excluded products designed solely for greenhouse environments, tree crops, or animal husbandry, as well as those lacking sufficient documentation of their features or use cases. After applying these criteria, we selected 18 commercial robotic platforms for detailed comparison and analysis. Figure 2 includes a flow diagram for the selection process.

3.3. Farmer Interview Methodology

To complement the existing literature and gain practical insights into the challenges and expectations surrounding technology, we conducted semi-structured interviews with nine farmers between February 2024 and April 2024. We selected participants using purposive sampling to represent different farm sizes, crop types, and levels of technology adoption across Ireland. We based selection on willingness to participate, operational relevance to row crop farming, and diversity in experience with agricultural technologies. We recruited participants through local extension networks and farming cooperatives. Interviews continued until we reached thematic saturation, meaning that no new themes emerged from additional participants. Each interview lasted 45–60 min and covered operational challenges, views on current robotic tools, and desired improvements.

We applied thematic coding using an inductive approach. We manually coded transcripts and grouped them under themes like “usability”, “cost concerns”, “technical limitations”, and “desired capabilities”. We then analyzed the data using the Value Proposition Canvas framework by Osterwalder et al. [28] to interpret the pains and gains. These themes helped classify needs in Section 4.2 and supported the pains and gains matrix.

4. The Needs of Farmers in Row Crop Farming

This section examines the particular needs of farmers involved in row crop farming, emphasizing the results obtained from literature surveys and qualitative analysis from in-depth interviews.

4.1. Insights from Surveys and Case Studies

Digital integration in agriculture is increasingly recognized as essential for improving productivity, resource efficiency, and decision making. However, many farmers struggle to extract actionable insights from existing technologies. Kernecker et al. [29] found that while farmers appreciated the potential benefits of smart machines, including improved income, productivity, and reduced workload, adoption was hindered by factors such as high costs, complexity, poor interoperability, and limited useful feedback from collected data. These findings highlight the need for more intuitive systems, such as digital twins [30,31] and interfaces powered by large language models (LLMs), that can translate sensor data into meaningful recommendations. Moreover, farmer-to-farmer communication was identified as a key adoption driver, reinforcing the importance of socially integrated, user-friendly solutions.

Robotic solutions must be adaptable to different farm sizes and production types. Spykman et al. [32] found that large-scale commercial farmers preferred high-capacity autonomous systems for efficiency, whereas smallholder and organic farmers favored lightweight robots that minimize soil compaction and environmental disruption. Rather than outright purchases, many farmers showed interest in Robot-as-a-Service (RaaS) models [33] to reduce upfront costs. This is supported by Caffaro and Cavallo [34], who observed that small and medium farms often show higher willingness to adopt new technologies, provided they are scalable, economically feasible, and environmentally compatible.

Customization of robotic systems to individual farm needs remains critical but also contributes to high development and operational costs. Labor constraints and sustainability concerns are shared across farm types, but one-size-fits-all solutions often fall short. Marinoudi et al. [35] and Monteiro [36] emphasized the potential of task-specific robots, such as precision weeders, to address labor and input challenges, yet high costs and slow operation speeds limit scalability. Studies by Tamirat et al. [11] and Gil et al. [12] highlight how business models, like subscription-based services, could make robotics more accessible. While Lampridi et al. [37] found that robotic farming currently incurs higher operational costs, Lowenberg-DeBoer et al. [38] showed that lower labor expenses can offset these costs, making robotic systems economically viable in many scenarios.

Field-level evidence further supports the value of adaptable robotic systems. Ørum et al. [39] studied a 200-hectare Dutch farm using autonomous robots alongside conventional equipment and reported a 30% reduction in labor hours, as well as improved fuel efficiency and lower greenhouse gas emissions. These findings align with Maritan et al. [40], who emphasized that fleet management and regulatory support are essential for the economic success of agricultural robots. Broader adoption also depends on farmer awareness and education. Rübcke von Veltheim [41] and Osrof et al. [42] identified age, training, and awareness as influential factors, recommending more inclusive development processes and better access to vendor-neutral educational materials. This raises broader questions about whether agricultural curricula and advisory services are adequately incorporating automation, robotics, and data literacy.

Collectively, these studies underscore that successful adoption of agricultural robotics depends not only on technical performance but also on business models, farmer education, and system integration. Farmers prioritize practical value, operational speed, and economic return on investment (ROI). However, many also recognize the long-term environmental value of robotics, particularly if this value can be monetized through subsidies, emissions data reporting (e.g., Scope 3 carbon credits), or market premiums for sustainable production.

In summary, survey and case study findings reveal several consistent themes: (1) farmers demand actionable, easy-to-use technology; (2) customization to farm size and crop type is necessary but cost-intensive; (3) RaaS and shared-service models can reduce adoption barriers; and (4) economic viability depends on both direct returns and external incentives. These insights inform the next subsection, which presents findings from qualitative interviews with farmers, offering additional operational context and validating survey results (see

Table 1. Comprehensive overview of issues and insights from various farmer surveys and case studies on agricultural technology covered in this paper.

	Issue/Theme	Articles	Common Findings	Concerns
Technological	Scalability and suitability for different farm sizes	[12,32,35,37,43]	Technologies often favor large-scale operations, making them less accessible for small farms.	Small farms struggle with technology scale and relevance to their specific needs.
	Interoperability and compatibility with existing systems	[12,32,35,37]	Difficulty integrating new technologies with existing systems.	Compatibility issues lead to inefficiency and frustration.
	Market readiness and availability of technology	[12,32,35,37]	Variability in technology availability and market readiness.	Inconsistent access to new technologies across different regions.
	Technological complexity and usability	[12,34,35,36,37,40,42,43]	Complex technologies require significant training; usability issues limit adoption.	Integration difficulties with existing systems, leading to frustration and underutilization.
	Adoption and research needs	[12,37,38,39,42]	Adoption rates are slow but show potential for growth.	Hesitation due to lack of localized research and proven results.
Economic	Return on Investment (ROI) uncertainty	[12,29,37,38,43]	Uncertainty about long-term ROI limits adoption.	Doubts about whether the investment will pay off in the long run.
	High costs of adoption	[11,12,29,32,34,35,37,38,39,41,43]	High initial costs are a major barrier, particularly for small and medium farms.	Difficulty in justifying high upfront costs and uncertain ROI.
	Labor implications and job displacement	[11,12,35]	Automation raises concerns about labor displacement.	Fear of job loss and social impact on rural communities.
Data Management	Complexity in managing data	[11,41,42]	Managing and analyzing data generated by smart farming technologies is challenging.	Farmers struggle with the volume and complexity of data.
	Data privacy and security concerns	[11,12,35,41,43]	Growing concerns as technologies become more data-driven.	Worries about data misuse, ownership, and breaches.
Environmental	Environmental and sustainability benefits	[11,12,32,35,36,37,38]	Environmental sustainability is a strong driver for adoption.	Skepticism about the actual environmental impact and long-term sustainability.
Educational	Farmer training and education	[34,37,38,40,41,42,43]	Training and education are essential to overcome adoption barriers.	Lack of training leads to underuse and frustration among farmers.
Social/Cultural	Skepticism and resistance	[29,32,34,35,41,43]	Widespread scepticism about benefits, particularly among older and small-scale farmers.	Fear of failure, unclear benefits, and privacy concerns.
	Social and cultural acceptance	[29,32,35,41,43]	Social and cultural factors influence adoption.	Traditional practices and norms resist technological changes.
Regulatory	Policy and regulatory support	[11,12,34,35,38,43]	Policy support is crucial for technology adoption.	Lack of clear policies related to data, safety, labor displacement, and compliance.

).

4.2. Results of In-Depth Interviews with Farmers

To better contextualize the surveys and case studies, we conducted in-depth interviews with nine farmers from temperate regions (USDA hardiness zones 8 and 9), including Ireland, the UK, and Atlantic-influenced continental Europe. These regions are characterized by moderate temperatures, increasingly wetter winters, and fragmented land parcels, often resulting in small- to medium-scale farming operations [44,45]. The interviewed farmers had diverse operations, including sole crop production, mixed cropping, forestry, and dairy farming. The practices included crop rotation, intercropping, mixed cropping, and cultivation of both winter and spring crops, although climate shifts increasingly favor spring crops due to operational risks associated with wetter winter months.

Farmers consistently identified several key tasks as automation priorities, including precision seeding, sowing, mechanical weeding, crop monitoring, and selective harvesting. These priorities were inferred from two primary challenges: continuing labor shortages and unpredictable weather patterns. Farmers reported that erratic rainfall and temperature fluctuations delay planting, affect spraying and harvesting times, and lead to lower crop yields. Although they view automation as a means to enhance farming operations, they remain concerned about the cost of technology, the shortage of qualified operators, and the reliability of robotic systems during critical periods, such as planting and harvesting.

It was evident from discussions on return on investment (ROI) that farmers sought to boost productivity, improve crop quality, and adopt sustainable practices. They envisioned robots that could reduce manual labor and chemical use while preserving soil health through low-compaction designs. They frequently emphasized the necessity of modular and affordable robotic solutions that could be integrated with their existing equipment to minimize the capital cost. Solutions that provide actionable data insights and enable narrow-row mechanical weeding, spot spraying, and precision seeding were given high priority. Farmers showed high interest in interconnected digital solutions that could evaluate data from soil and crop sensors to respond quickly through autonomous or semi-autonomous operations. For long-term use of advanced robotic solutions, access to maintenance and repair services was considered crucial.

These detailed interviews with farmers added critical operational depth to the broader findings from the literature review of surveys and case studies. Direct discussions with farmers highlighted the specifics of their daily farming operations, seasonal stressors, and concrete design preferences, while findings from the literature review of surveys provided broader technology perceptions and barriers. Interestingly, farmers indicated that they prefer the flexibility of robotic systems over their scale, favoring adaptable and modular robotic solutions over large, multifunctional machines.

Table 2. Pains and gains analysis of farmers’ interviews in Ireland using Value Proposition Canvas framework by Osterwalder et al. [28].

Jobs	Pains	Gains	Concerns
Seeding and planting	High cost of advanced equipment	Reduced labor costs	Integration with conventional farming tools
Mechanical weeding	Manual labor and less efficient weeders	Minimized chemical use through precision weeding	Robustness of system
Yield estimation	Environmental and climate change impact on yields	Accurate yield predictions data	Actionable data
Crop monitoring	Insufficient funds for sustainable practices	Real-time health monitoring for crops and soil	Reliability of monitoring data
Precision spraying and watering	Availability of trained labor and cost	Increase efficiency and resource management	Precision and consistency
Selective harvesting of ripe crops	Short harvest windows due to weather variability	Higher crop quality and less waste	Maintenance and uptime of equipment
Soil microbiology and nutrient analysis	Information to perform actions on specific areas	Enhanced soil health insights	Availability of actionable data from the analysis
Automated pest control	Inconsistent pest control methods	Effective and targeted pest control	Durability in varying conditions
Plant health and nutrients analysis	High cost and time of advanced analysis	Improved plant health and optimized nutrient use	Precision and accuracy of nutrient analysis
Minimize soil compaction	Heavy machinery causing soil damage	Reduced soil damage and better root growth	Adaptability of small and lightweight machines to different farms
Environmental sustainability	Complex regulations and compliance requirements	Improved environmental impact and reduced carbon footprint	Sustainability of farming practices

analyzes the insights from interviews through the lens of the Value Proposition Canvas framework by Osterwalder et al. [28], mapping farmers’ identified tasks (“jobs”) against associated pain points and desired gains. This approach clarifies the functional, emotional, and economic values that farming automation must deliver to achieve relevance and adoption.

Overall, the interviews refined our understanding of the value of on-farm robotic solutions. Section 6 categorizes and prioritizes these needs. Understanding the needs of farmers set the foundation for the next section, which examines how current robotic technologies align with or fall short of these expectations.

5. Robotic Technologies in Agriculture for Row Crops

Robotic technologies have transformed agriculture by improving efficiency, reducing labor costs, and enhancing crop yields. These innovations span both commercial solutions and academic research, addressing key areas such as sensing and imaging, artificial intelligence, autonomous navigation, and robotic hardware. The methodology for identifying and selecting relevant robotic technologies is described in Section 3. This section is divided into two main parts. First, we review commercial robotic solutions, analyzing technical specifications and practical applications. Next, we explore academic and technological innovations, focusing on field-validated solutions. This structured review bridges commercial advancements and academic research, identifying key gaps and opportunities for integrating robotic solutions into real-world row crop farming.

5.1. Commercial Solutions for Row Crops

Commercial robotic solutions have significantly impacted row crop farming by addressing labor shortages, rising production costs, and sustainability challenges. These solutions range widely, including autonomous tractors, unmanned aerial vehicles (UAVs), and AI-driven machines designed for precision, efficiency, and consistent performance. Key applications include robotic manipulators for harvesting [46,47,48,49], UAVs specialized in aerial spraying [50,51,52], and autonomous ground vehicles (UGVs) used for fertilization [53,54], crop monitoring [55,56], seeding [54,57], and precision weeding [58,59,60,61,62]. These robots employ specialized tools and locomotion systems optimized for varied tasks and terrains.

The process for commercial product selection and evaluation is described in the Methodology Section 3. Notably, the availability and quality of technical information varied significantly across products. Some vendors provide detailed specifications, performance metrics, and user manuals publicly, facilitating rigorous evaluation. Others offer limited technical transparency, often constrained by proprietary concerns or early market stage, which complicates independent assessment of capabilities and limits comprehensive comparisons. The selected commercial robots are grouped by primary function: seeding, weeding, crop monitoring, and spraying, which are application-specific solutions and multi-purpose platforms performing multiple tasks during the production cycle with different tools. Table 3 summarizes their technological features, including battery life, sensing modalities, task specialization, weight, and kinematics, highlighting both strengths and current limitations. Table 3 also includes robot costs or Robots-as-a-Service (RaaS) models, providing context on economic feasibility and how pricing influences farm adoption.

5.1.1. Application-Specific Commercial Robotic Solutions

Application-specific robotic solutions aim to automate particular tasks in row crop farming. These can reduce labor in labor-intensive tasks and minimize chemical use while increasing overall efficiency. However, their adaptability and versatility are limited by their focus on particular farming operations. They are, therefore, more of an addition to already existing farming equipment than a standalone solution for various tasks on the farm.

The FarmDroid FD20 [57] is a notable example of an application-specific commercial solution, a solar-powered, autonomous system designed for precision seeding and weeding tasks. Due to its mechanical weeding capabilities coupled with autonomous navigation of 8 mm accuracy using RTK-GPS, it minimizes the manual labor and chemical use in the field. It can cover 6.5 hectares per day and weighs around 900 kg, which helps reduce soil compaction while performing continuous farming operations with solar power. It is slower than a conventional tractor and has limited adaptability, as it is only suitable for seeding and weeding operations on specific crops, despite being able to scale to accommodate different farm and row sizes. A similar robot designed for corn fields is the Rowbot [54], which provides monitoring, fertilizing, and canopy crop seeding. Although its multiple operations increase productivity, a significant limitation is its capacity to adapt to different crops.

Precision spraying is the focus of another specialized category of robots. A solar-powered robot for targeted spraying and high-precision monitoring of grain crops is Solinftec’s Solix robot [53]. Up to two million plants can be analyzed with its high-resolution cameras, and with integrated AI capabilities, it can reduce herbicide use by up to 95%. Its ability to continuously operate for three days without sunlight further enhances its sustainability, although it is still restricted to its primary task. Another interesting solution for precision spraying is the DJI MG-1P [50], an autonomous aerial robot that can carry a payload of 10 kg. Its terrain-following radar, combined with intelligent flight control, provides even coverage of spray across the field. However, its operating time is limited by the need for frequent recharging, and high wind can affect the performance of spraying. Furthermore, Oscar by Osiris Agriculture [63] is an autonomous robot for precise fertilizing and irrigation in large fields. It features a 40 m boom, allowing it to spray this length simultaneously with precision. With integrated cameras to monitor plant growth combined with soil moisture information, it can deliver customized water and fertilizers to the individual plants. Although its design is primarily suitable for larger fields, it improves efficiency across 8 to 20 hectares per day.

For monitoring and data collection, autonomous robots are becoming more important. The TerraSentia [55] is a compact, 13 kg robot equipped with RGB and LiDAR sensors. It can perform high-resolution 3D phenotyping and yield prediction. It navigates fields on its own to gather crucial crop data but cannot take direct action. Meanwhile, the Tom robot by Small Robot Company [64] provides detailed assessments of crop health and detects weeds, capturing high-resolution images (0.28 mm/px). Its modular design allows for spraying and weeding. However, its complexity might create challenges for farmers. The AIGRO UP robot [65] is an autonomous tool carrier that mows, performs mechanical weeding, and scouts. It comes in 2WD and 4WD models with a 24 h battery life. It reduces soil compaction but is limited to its assigned tasks.

Weeding robots also play a crucial role in reducing reliance on herbicides. The Aigen Element robot [59] integrates precision weeding, crop monitoring, and data collection, running entirely on solar power to enhance sustainability. Despite its compact design and minimal soil impact, it lacks modular attachments, making it less flexible than multi-purpose robots. Similarly, the WeedSpider [61], weighing 800 kg, can cover 11.3 hectares per day, using AI-driven mechanical weeding arms for high-precision weed removal. Additionally, MYCE Agriculture [60] developed a solar-powered weeding robot that uses lasers and cameras, but limited information is available on navigation and crop identification. The Farming GT by Farming Revolution [66] is an advanced weeding robot with deep-learning-powered vision, capable of identifying over 80 plant species and removing weeds with high precision, even in dense or overlapping crops. Finally, Earth Rover’s CLAWS [62] combines computer vision with short-pulse light technology to remove weeds while reducing soil disturbance. However, we still do not know how well it works in dense crops and different terrains.

While these robots provide useful solutions for certain tasks, they also have drawbacks. High costs, limited operations, and challenges with integration may slow down their widespread use. Figure 3 shows key commercial robots designed for specific applications in agriculture.

5.1.2. Multi-Purpose Commercial Robotic Solutions

Multi-purpose robotic platforms are changing agricultural automation by combining multiple functions into one system. Their modular design and flexibility reduce the need for several machines, which improves efficiency and cuts costs. However, high upfront costs, complex integration, and scalability challenges remain significant obstacles to widespread use. Naïo Technologies’ Oz [67] is a compact autonomous weeding and hoeing robot designed for organic and small farms. While it effectively reduces labor, its slow speed and limited scalability mean that larger farms need to deploy a fleet. Similarly, Farm-ng’s Amiga [68] is built for selective harvesting, planting, and crop monitoring. Its modular tool attachments, remote operation, and open API support boost flexibility and help it integrate smoothly into farming systems.

Saga Robotics’ Thorvald [69] is a versatile platform for light treatment, spraying, and data collection, suitable for both indoor and outdoor farming [70,71]. It can operate independently or through remote control, but integrating extra tools can still be difficult. Naïo Technologies’ Orio [58] is a high-precision weeding robot that uses sustainable mechanical weeding instead of herbicides. Weighing 1450 kg, it works at speeds between 3 and 5.5 km/h, using RTK-GPS, LiDAR, and cameras to tell crops apart from weeds. With a battery life of 12 h, it can support various attachments, such as hoe blades, seeders, and mowers, but its price tag makes it hard to access.

Table 3. Technological capabilities of autonomous commercial agricultural robots discussed in this paper. N/R: not reported—no details available nor disclosed by the manufacturer. N/A: not applicable. *: Solar-powered and battery-supported. **: Hovering time without payload on single battery. ***: With diesel range extender. RaaS: Robots-as-a-Service model.

Robot	Applications	Price [€]	Battery Life [h]	Weight [kg]	Sensors	Kinematics	Features
FD20 [57]	Seeding, weeding	101–150 k	18–24 *	900	RTK-GPS, proximity sensors	Wheeled skid-steer	CO2 neutral, uses high-precision GPS for seeding and weeding
Rowbot [54]	Fertilizing, seeding	RaaS	20	N/R	GPS, cameras, LiDAR	Tracked skid-steer	Precision fertilization, works under crop canopy, optimized for nitrogen application
Solix [53]	Monitoring, spraying	RaaS	N/R *	500	Cameras, RTK-GPS	Two-wheel drive	Upto 95% herbicide reduction, targeted spraying boom
DJI MG-1P [50]	Monitoring, spraying	15 k	0.33 **	13.7	Radar, RTK-GPS, FPV camera	Multirotor drone	Terrain-adaptive spraying, can operate in fleets
Oscar [63]	Irrigation	201–300 k	N/R	12,000	RTK-GPS, RGB cameras	Four-wheel drive	Precision irrigation, upto 30% resource savings and 10% yield improvement
TerraSentia [55]	Monitoring, phenotyping	RaaS	3	13	LiDAR, RGB cameras, GPS	Wheeled skid-steer	Under-canopy field data and trait analysis, compact and rugged design
Tom v4 [64]	Monitoring, weed detection	RaaS	4	350	RTK-GPS, cameras	Four-wheel drive & steer	Plant-level data collection, modular design, scalable for various farm sizes
AIGRO UP [65]	Mowing, harrowing	21–50 k	8	100	RTK-GPS, cameras	Four-wheel drive	Compact and lightweight design, can be used in various farms
Element [59]	Weeding, crops analysis	50–80 k	N/R *	N/R	Cameras, temperature and moisture sensors	Wheeled skid-steer	Chemical-free weed control, reduces soil compaction, supports fleet coordination and control
WeedSpider [61]	Weeding, thinning, spraying	RaaS	10 *	800	LiDAR, GPS, cameras	Wheeled skid-steer	95% labor cost reduction, sub-inch accuracy, 3D mechanical weeding
MYCE [60]	Monitoring, weeding	RaaS	20 *	100	GPS, cameras	Four-wheel drive & steer	High precision in vegetables, compact design
Farming GT [66]	Precision weeding	101–200 k	30 ***	1500	RTK-GPS, multispectral camera	Four-wheel drive & steer	High precision weeding, adaptability across terrains and crops
CLAWS [62]	Monitoring, weeding	RaaS	N/R *	N/R	Cameras, RTK-GPS	Two-Wheel drive	Mechanical weeding, chemical-free operation, data collection for crop health
Oz [67]	Multi-purpose	21–50 k	8	150	RTK-GPS, stereo cameras	Four-wheel skid-steer drive	Multiple attachments for different tasks, ideal for small to medium farms
Amiga [68]	Multi-purpose	21–50 k	8	200	RTK-GPS, cameras	Four-wheel skid-steer drive	Modular design, customizable for various tasks, 3-point hitch support
Thorvald II [69]	Multi-purpose	RaaS	10	180	LiDAR, cameras, GPS	Four-wheel drive & steer	Versatile, modular, suitable for various crops and terrains
Orio [58]	Multi-purpose	201–300 k	8–12	1450	RTK-GPS, LiDAR, safety bumpers	Four-wheel drive	Versatile tool carrier, supports various attachments, 3-point hitch support
Robotti [72]	Multi-purpose	101–200 k	N/A	3000	RTK-GPS, cameras	Four-wheel drive	Dual diesel engines (144 hp), 3-point hitch and PTO support, supports various attachments

Table 3. Technological capabilities of autonomous commercial agricultural robots discussed in this paper. N/R: not reported—no details available nor disclosed by the manufacturer. N/A: not applicable. *: Solar-powered and battery-supported. **: Hovering time without payload on single battery. ***: With diesel range extender. RaaS: Robots-as-a-Service model.

Robot	Applications	Price [€]	Battery Life [h]	Weight [kg]	Sensors	Kinematics	Features
FD20 [57]	Seeding, weeding	101–150 k	18–24 *	900	RTK-GPS, proximity sensors	Wheeled skid-steer	CO2 neutral, uses high-precision GPS for seeding and weeding
Rowbot [54]	Fertilizing, seeding	RaaS	20	N/R	GPS, cameras, LiDAR	Tracked skid-steer	Precision fertilization, works under crop canopy, optimized for nitrogen application
Solix [53]	Monitoring, spraying	RaaS	N/R *	500	Cameras, RTK-GPS	Two-wheel drive	Upto 95% herbicide reduction, targeted spraying boom
DJI MG-1P [50]	Monitoring, spraying	15 k	0.33 **	13.7	Radar, RTK-GPS, FPV camera	Multirotor drone	Terrain-adaptive spraying, can operate in fleets
Oscar [63]	Irrigation	201–300 k	N/R	12,000	RTK-GPS, RGB cameras	Four-wheel drive	Precision irrigation, upto 30% resource savings and 10% yield improvement
TerraSentia [55]	Monitoring, phenotyping	RaaS	3	13	LiDAR, RGB cameras, GPS	Wheeled skid-steer	Under-canopy field data and trait analysis, compact and rugged design
Tom v4 [64]	Monitoring, weed detection	RaaS	4	350	RTK-GPS, cameras	Four-wheel drive & steer	Plant-level data collection, modular design, scalable for various farm sizes
AIGRO UP [65]	Mowing, harrowing	21–50 k	8	100	RTK-GPS, cameras	Four-wheel drive	Compact and lightweight design, can be used in various farms
Element [59]	Weeding, crops analysis	50–80 k	N/R *	N/R	Cameras, temperature and moisture sensors	Wheeled skid-steer	Chemical-free weed control, reduces soil compaction, supports fleet coordination and control
WeedSpider [61]	Weeding, thinning, spraying	RaaS	10 *	800	LiDAR, GPS, cameras	Wheeled skid-steer	95% labor cost reduction, sub-inch accuracy, 3D mechanical weeding
MYCE [60]	Monitoring, weeding	RaaS	20 *	100	GPS, cameras	Four-wheel drive & steer	High precision in vegetables, compact design
Farming GT [66]	Precision weeding	101–200 k	30 ***	1500	RTK-GPS, multispectral camera	Four-wheel drive & steer	High precision weeding, adaptability across terrains and crops
CLAWS [62]	Monitoring, weeding	RaaS	N/R *	N/R	Cameras, RTK-GPS	Two-Wheel drive	Mechanical weeding, chemical-free operation, data collection for crop health
Oz [67]	Multi-purpose	21–50 k	8	150	RTK-GPS, stereo cameras	Four-wheel skid-steer drive	Multiple attachments for different tasks, ideal for small to medium farms
Amiga [68]	Multi-purpose	21–50 k	8	200	RTK-GPS, cameras	Four-wheel skid-steer drive	Modular design, customizable for various tasks, 3-point hitch support
Thorvald II [69]	Multi-purpose	RaaS	10	180	LiDAR, cameras, GPS	Four-wheel drive & steer	Versatile, modular, suitable for various crops and terrains
Orio [58]	Multi-purpose	201–300 k	8–12	1450	RTK-GPS, LiDAR, safety bumpers	Four-wheel drive	Versatile tool carrier, supports various attachments, 3-point hitch support
Robotti [72]	Multi-purpose	101–200 k	N/A	3000	RTK-GPS, cameras	Four-wheel drive	Dual diesel engines (144 hp), 3-point hitch and PTO support, supports various attachments

Meanwhile, Agrointelli’s ROBOTTI [72] is an autonomous field robot designed for various field operations using standard tractor attachments. Unlike electric robots, it runs on dual diesel engines, allowing continuous 24/7 operation with RTK-GPS support for precision. Its configurable row widths and implementation of different sizes enhance versatility, though high upfront costs remain a barrier. As agricultural robotics continue evolving, new designs and robotic concepts are emerging rapidly [73,74,75,76]. Figure 4 highlights key multi-purpose robots in row crop farming. By integrating multiple tasks into a single platform, multi-purpose robots reduce labor, improve efficiency, and enhance farm data collection. Their ability to perform planting, weeding, harvesting, and phenotyping makes them a valuable investment in long-term productivity and sustainability.

Table 3 compares the technological capabilities of various commercial autonomous agricultural robots mentioned in this section. These solutions differ widely in their application focus, sensor setups, mobility systems, and pricing models, such as direct purchase versus Robots-as-a-Service. Robots that use advanced sensing technologies like LiDAR, multispectral cameras, and high-precision RTK-GPS usually provide better autonomy and accuracy for navigation and task execution, especially in complicated or changing field conditions. However, these high-performance sensors can greatly raise the total system cost. For instance, LiDAR-equipped platforms like WeedSpider, Thorvald II, and Orio provide sub-inch-level spatial awareness and obstacle avoidance, but their hardware costs can be too high for small and medium-sized farms unless they are available through service-based models. RTK-GPS also improves positioning precision, but it needs subscription-based corrections, which add to both hardware and operational costs. As robotic platforms continue to develop, finding a balance between sensor features, cost, and scalability is important for supporting wider adoption, particularly in small to medium markets that are sensitive to costs.

5.2. Field-Validated Technological Innovations in Academic Research

Recent agricultural robotics research in academia has moved beyond theoretical and simulation work, demonstrating successful field deployments and hardware integration across sensing, AI, navigation, and various tools. These real-world implementations significantly enhance precision, efficiency, and sustainability in row crop farming. Figure 5 illustrates the integration of key technological subsystems that make autonomous agricultural robots capable of performing various tasks in field conditions. These core subsystems include sensing and imaging, machine learning and AI, autonomous navigation, control system, and robotic hardware. The sensing and imaging subsystem comprises all the sensing and imaging modules, including cameras, LiDAR, and proximity sensors, which are used to detect crops, identify weeds, collect plant health information, and detect obstacles and paths for navigation. Navigation data from sensing and imaging is passed to autonomous navigation subsystem, which comprises sensor fusion and navigation algorithms to support precise navigation across crop rows and avoid obstacles for robots utilizing RTK-GPS and IMU-based localization. Weeds and crops-related data are used by machine learning and AI for crop classification, predictive analysis, and task planning. This enables the robot to quickly react to changing field conditions and make cognitive, data-driven decisions. Sensor feedback and AI-driven decisions are utilized by the control system to regulate robot motion and control tool operations. Finally, robotic hardware encompasses chassis designs, locomotion systems, tools, and manipulators specifically designed for various agricultural tasks. Field-ready agricultural robots rely on these interconnected core technologies that affect their efficiency, accuracy, and autonomy in practical applications.

This section examines field-validated innovations for row crops drawn from recent academic research. We specifically focus on robotic solutions that have been evaluated in real agricultural environments rather than remaining theoretical or limited to simulation. Field-validated technologies were selected through structured criteria, as outlined in Section 3. This focus on field-proven systems offers insight into the real-world potential of robotics in advancing row crop farming. The purpose here is to analyze the innovations, rather than to critically review the technical work conducted by the authors.

Vision and spectral sensors have been tested extensively in outdoor crop environments to improve weed detection, crop health assessment, and precise input application. Quan et al. [77] developed a robotic weeding platform tested in maize fields that enabled precise intra-row weeding through real-time crop and weed detection using RGB cameras and thermal imaging as shown in Figure 6a. Liu et al. [78] demonstrated a LiDAR-based navigation system in soybean and corn fields, delivering a crop row detection algorithm with 2.98 cm accuracy to enable autonomous over-canopy navigation, though gaps in canopy remain a challenge. Valero et al. [79] integrated multispectral cameras and LiDAR sensors on a robotic arm for targeted single-plant fertilization in organic cropping fields, significantly reducing fertilizer use by distinguishing plant types in real time. Additionally, Cubero et al. [80] developed the RobHortic platform, a remote-controlled robot combining RGB, multispectral, and hyperspectral sensors for early detection of bacterial infections in carrot crops, achieving around 60% detection accuracy under field conditions. Vasconcelos et al. [81] introduced DARob, a low-cost autonomous robot for agricultural image acquisition, providing scalable datasets for computer vision applications and supporting precision agriculture efforts in resource-constrained settings.

Thermal imaging and spectral sensors have also been deployed for crop stress and irrigation management. Ma et al. [82] utilized infrared thermal imaging to diagnose water deficits in winter wheat, optimizing irrigation scheduling and conserving water. Electrochemical soil sensors, developed by Dhamu et al. [83], were used for real-time monitoring of soil organic carbon, providing farmers with actionable data for precise fertilization. Portable IoT-based weather stations [84] and soil moisture sensors [85] have been field-tested for climate-responsive farm decision making, enabling efficient irrigation management and resource conservation. GPS and IMU sensor fusion demonstrated by Galati et al. [86] achieved decimeter-level accuracy in vineyard navigation, crucial for autonomous operations in narrow crop rows. Autonomous spot-spraying with RTK-GNSS and dual cameras was validated by Wijesundara et al. [87] in structured crop fields, enabling precise agrochemical application and reducing waste. Field-tested sensing solutions are proving essential for precision management, offering accurate, real-time insights to improve crop care and resource efficiency.

AI and machine learning models have been integrated with robotic hardware and validated in the field, improving weed control, crop monitoring, and harvesting. Visentin et al. [88] introduced a robotic platform with RGB-D cameras shown in Figure 6b. It achieved up to 98% accuracy in detecting weeds and caused minimal damage to crops, less than 4%, in actual greenhouse settings. This was especially true when integrating prior knowledge, such as intra-row spacing. However, detection accuracy can vary with different lighting or terrain conditions, which is a common issue in agricultural robotics. The SPARROW system by Balasingham et al. [89] combined YOLOv8-based weed detection with optimized herbicide spraying in field tests, significantly reducing chemical use while maintaining weed control. Azghadi et al. [90] demonstrated a deep-learning-powered robotic spot sprayer in sugarcane farms, achieving a 65% reduction in herbicide application with 97% weed control efficiency. UAV-based AI models developed by Ndlovu et al. [91] used multispectral imagery to estimate maize water stress indicators in smallholder farms, enhancing irrigation decision making. For selective harvesting, Chen et al. [92] implemented an AIoT-enabled mobile robot using SLAM and YOLOv3-tiny for pitaya harvesting in outdoor orchards, achieving 96.7% fruit recognition accuracy under challenging field conditions. AI-based pest detection systems with acoustic and infrared sensors, as developed by Thomas et al. [93], provided real-time alerts in operational crop fields, enabling timely interventions and reducing losses. Yang et al. [94] designed a Residual-like Soft Actor Critic (R-SAC) reinforcement-learning-based path-planning system for agricultural robots, improving obstacle avoidance and adaptive task performance under changing field conditions. Field-ready AI solutions are unlocking precision harvesting and pest management, directly improving yield and reducing production risks.

Figure 6. Innovative agricultural robots in research for row crops. (a) Intelligent intra-row weeding robotic platform [77]. (b) Inter-row and intra-row weeding platform [88]. (c) Modular agricultural robotic systems (MARS) [95].

Figure 6. Innovative agricultural robots in research for row crops. (a) Intelligent intra-row weeding robotic platform [77]. (b) Inter-row and intra-row weeding platform [88]. (c) Modular agricultural robotic systems (MARS) [95].

Advanced navigation systems combining multiple sensors have been tested in diverse crop environments to ensure reliable autonomous operation. Velasquez et al. [96] fused LiDAR and IMU data via extended Kalman filtering to improve under-canopy navigation in cornfields, maintaining accuracy despite environmental occlusions. Du et al. [97] developed a low-cost tracked robot using vision-based navigation and IMU fusion for autonomous weed control in flax and canola fields, proving effective in narrow rows, though limited by herbicide dependency. Winterhalter et al. [98] combined GNSS data with crop row mapping for accurate pose estimation and field traversal in vegetable crops. Sulistijono et al. [99] enhanced ground robot trajectory planning using drone-generated terrain models, optimizing navigation on uneven farmlands. Vision-based crop row detection using deep learning architectures like U-Net was validated by De Silva et al. [100] in sugar beet fields, demonstrating robustness across varying weed density and lighting conditions. Wei et al. [101] applied lightweight convolutional neural networks for early-stage corn row following, effectively filtering non-crop elements and improving navigation precision. Monocular RGB vision systems extended autonomous under-canopy navigation runtimes in dense crop environments, as shown by Sivakumar et al. [102], with follow-up improvements in large-scale deployments [103]. Baltazar et al. [104] developed a machine-vision-guided robotic sprayer using SVM-based leaf density estimation to dynamically adjust air and spray flow, significantly improving precision in heterogeneous vineyard environments. Field tests confirm that advanced computer vision and sensor fusion approaches are boosting navigation reliability and crop compatibility.

Field-proven robotic platforms emphasize adaptability and durability to perform complex tasks across various terrains. Guri et al. [105] developed a modular wheeled robot platform, shown in Figure 6c, offering high-accuracy navigation and tool interchangeability for multiple crop operations. Yang et al. [106] engineered a heavy-duty hexapod robot with adaptive fuzzy impedance control to maintain stability on rugged agricultural landscapes. Modular autonomous systems like MARS [95] facilitate diverse precision farming tasks, while weatherproof unmanned ground vehicles (UGVs) designed by Kyberd et al. [107] provide reliable long-term operation in harsh outdoor conditions. Solar-powered UGVs such as Agri.q [108] extend operational duration with onboard manipulators for precise field tasks. Wang et al. [109] investigated optimal laser parameters in precision weeding robots, identifying strategies to minimize targeting errors and enhance cutting efficiency. Khadatkar et al. [110] designed the WeeRo, a low-cost remotely controlled mechanical weeder achieving 82% efficiency on raised beds, addressing labor challenges in developing regions. Ju et al. [111] created a YOLOv5-enhanced adaptive cruise weeding robot for paddy fields, achieving 90% precision in rice seedling detection and high weeding efficiency. Jiang et al.’s [112] hybrid intra-row weeding robot that uses deep learning and multiple weeding knives to reduce herbicide use in maize fields while minimizing crop damage. Terra et al. [113] developed a low-cost autonomous sprayer retrofitted on conventional boom sprayers, incorporating machine vision and microcontroller-based nozzle control to precisely target pesticide application on multiple crop types, including onion, soybean, and corn. Javidan and Mohamadzamani [114] built a solar-powered ultrasonic sensor-guided seeder robot, achieving precise row planting trajectories at low operational cost. Robust, modular hardware designs are key enablers of versatile, long-lasting field performance across diverse crop systems, while specific application-based robotic solutions validated in the field are demonstrating substantial reductions in input use and improving operational targeting.

Table 4. Overview of selected field-validated agricultural robotics innovations for row crops automation.

Reference	Technology/Methodology	Key Contributions	Crop	Challenges	Application Context
Quan et al., 2022 [77]	RGB and thermal imaging, custom weeding tool	Real-time crop and weed detection enabling precise intra-row mechanical weeding	Maize	Detection accuracy in varying field conditions	Precision weeding in maize cultivation
Liu et al., 2024 [78]	LiDAR-Based Navigation System	2.98 cm accuracy in crop row detection for autonomous navigation	Soybean, corn	High cost of sensors, complexity in varying field conditions	Over-canopy navigation for precision farming
Valero et al., 2022 [79]	Multispectral camera, LiDAR, robotic arm	Targeted single-plant fertilization reducing fertilizer use	Organic vegetable fields	Distinguishing plant types, optimizing fertilization	Precision fertilization in organic farming
Cubero et al., 2020 [80]	Multispectral, hyperspectral, thermal cameras	Detected bacterial infections in carrots,  60% accuracy	Carrot fields	Asymptomatic stage detection	Field-based early disease sensing
Vasconcelos et al., 2023 [81]	Low-cost autonomous RGB/IR robot	Developed affordable robot for scalable image datasets	Beans	Lighting variation, low-cost components	Research-oriented agricultural image collection
Ma et al., 2024 [82]	Infrared thermal imaging	Diagnosed water deficit via canopy temperature	Winter wheat	Environmental factors affecting readings	Precision irrigation management
Dhamu et al., 2024 [83]	Electrochemical sensors	Real-time soil organic carbon measurement	Various soil types	Sensor calibration	Real-time soil carbon monitoring and Soil health assessment
Dubey et al., 2023 [84]	IoT-based portable station	Real-time weather data for farm decision-making	Various crops	Sensitivity to environmental factors	Climate-responsive precision agriculture
Bryant et al., 2023 [85]	Soil moisture sensors	Water use reduction while maintaining yields	Various crops	Sensor placement, calibration	Precision irrigation scheduling
Galati et al., 2022 [86]	GPS, IMU fusion	Decimeter-level navigation accuracy in vineyards	Vineyards, narrow rows	GNSS signal loss	Autonomous navigation in vineyard and similar narrow-row crops
Wijesundara et al., 2023 [87]	RTK-GPS, machine vision	Precise agro-chemical application with autonomous spraying	Structured crop fields	GPS reliability, vision accuracy	Autonomous precision spraying in structured crop fields
Visentin et al., 2023 [88]	Deep learning with RGB-D camera	98% weed detection with minimal crop damage	Multiple row crops	System integration	Precise intra-row and inter-row weeding
Balasingham et al., 2024 [89]	YOLOv8, vision-guided spraying	Autonomous weed detection and herbicide spot spraying	Row crops	Vision system accuracy	Autonomous weed detection and herbicide reduction in field
Azghadi et al., 2024 [90]	Deep learning-based robotic sprayer	65% herbicide reduction with 97% weed control	Sugarcane farms	Annotation and integration	Reducing herbicide usage and improving water quality
Ndlovu et al., 2021 [91]	UAV multispectral data, ML	UAV-derived model for maize water content estimation for irrigation	Maize	Data integration	Precision irrigation and drought monitoring
Chen et al., 2023 [92]	AIoT, SLAM, YOLOv3-tiny	96.7% fruit recognition in pitaya orchards	Pitaya	Occlusion, lighting	Autonomous selective fruit harvesting
Thomas et al., 2023 [93]	AI, IoT acoustic and IR sensors	Real-time pest alerts for early intervention	Various crops	Sensor fusion accuracy	AI-based Real-time pest management
Yang et al., 2022 [94]	R-SAC reinforcement learning	Adaptive, obstacle-aware robot navigation	Mixed crops	Complex training setup	Learning-based autonomous path planning
Velasquez et al., 2022 [96]	LiDAR and IMU fusion	Improved under-canopy navigation accuracy	Corn crops	Sensor occlusion	Autonomous row following
Du et al., 2021 [97]	Vision-based navigation	Offered a low-cost solution for autonomous weed control with vision-based navigation	Flax, canola	Battery and computation	Autonomous weed control in narrow row crops
Winterhalter et al., 2021 [98]	GNSS and crop row detection	GNSS-referenced crop row map for reliable pose estimation and headland turns	Vegetable fields	GNSS accuracy at field edges	Fully autonomous field traversal
Sulistijono et al., 2020 [99]	UAV aerial mapping and grid-based path planning	Optimized ground robot paths in uneven farmland through drone maps	Varied crops	Map accuracy	Precision navigation planning
De Silva et al., 2023 [100]	Deep learning (U-Net)	Applied deep learning for robust crop row detection using low-cost cameras	Sugar beet fields	Dense weed coverage	Vision-based autonomous navigation in challenging field conditions
Wei et al., 2022 [101]	Lightweight CNN	Effective early-stage corn row navigation	Corn	Unstructured environment variability	Early-stage crop row-following and navigation
Sivakumar et al., 2021, 2024 [102,103]	Monocular RGB vision, semantic keypoint detection	Extended autonomous runtime in dense crops	Corn, soybean	Occlusions, visual clutter	Autonomous under-canopy operation
Baltazar et al., [104]	SVM-based machine vision	Dynamic adjustment of air/spray flow, 80–85% accuracy	Vineyards, steep slopes	Heterogeneous canopy, slope handling	Smart precision spraying on complex terrain
Guri et al., 2024 [105]	Modular reconfigurable mobile Robot	Modular design for tool reconfiguration for diverse tasks	Various crops	Integration challenges	Multi-purpose robotic platform
Yang et al., 2023 [106]	Heavy-duty hexapod robot	Adaptive fuzzy impedance control for stability of hexapod	Multiple terrains	Force tracking errors	Navigation in rugged agricultural environments
Xu and Li, 2022 [95]	Modular Agricultural Robotic System (MARS)	Affordable, versatile platform for precision farming	Various crops	Modularity challenges	High-throughput phenotyping and precision farming tasks
Kyberd et al., 2023 [107]	Robust sensor suite and safety systems	Long-term outdoor autonomous operation	Various crops	Weatherproofing	Long-term autonomous operations in unstructured harsh field
Quaglia et al., 2020 [108]	Agri.q (Solar-Powered UGV)	Solar-powered UGV with a 7-DOF manipulator for extended autonomous operation	Vineyards	Power management	Precision agriculture tasks in large farms
Wang et al., 2025 [109]	Laser tuning (power, angle, trajectory)	Reduced targeting error, better cutting efficiency	Multiple weed species	Laser accuracy, environmental variability	Precision laser weeding optimization
Khadatkar et al., 2025 [110]	Remote-controlled sweeps, low-cost robot	Achieved 82% weeding efficiency, minimal crop damage	Raised bed fields	Sweep precision, labor interface	Affordable mechanical weeding solution
Ju et al. [111]	YOLOv5-based adaptive cruise robot	90% rice seedling detection, 82% weed control rate	Paddy rice fields	Waterlogged conditions	Vision-guided paddy field weed control
Jiang et al., 2023 [112]	Deep learning, multiple knives	Targeted weed removal reducing herbicides	Maize fields	Targeting precision	Intra-row robotic weeding
Terra et al., 2021 [113]	Low-cost vision and nozzle control	Retrofitted sprayer for precision pesticide application	Onion, soybean, corn	Component integration	Autonomous spraying on multiple crops
Javidan et al., 2021 [114]	Solar, ultrasonic	Precise low-cost planting in small farm	Mixed crops	Power management	Small farm seeder

provides an overview of field-validated robotic solutions for row crops automation. Between 2020 and April 2025, these academic innovations have shown significant progress in moving from lab prototypes to practical field applications. The studies reveal several trends: (1) many robotic systems are still experimental and often tested on small-scale or very specific tasks; (2) how well technology works depends on crop type and field environment; and (3) sensor fusion, like combining LiDAR, RGB, and multispectral imaging, is increasingly used to address perception limits. Challenges such as sensor calibration, environmental variability, and system integration remain across many studies. These findings suggest that while the progress is encouraging, broader scalability and general use are still limited. Focusing on modularity, strength, and cost efficiency is important to bridge the gap between academic innovation and farm use. Prioritizing solutions with proven field performance ensures that research insights translate into tangible benefits for farmers and the agricultural sector.

6. Aligning the Needs of Farmers with Robotic Capabilities

A detailed classification of the needs of farmers, categorized into operational, technological, economic, data management, environmental, educational, regulatory, and health and safety, highlights the potential of robotic capabilities to improve agricultural productivity, efficiency, and sustainability.

Table 5. Mapping the identified needs of farmers against the core system abilities of reviewed agricultural solutions. Ability levels (0–8) are assigned using the SPARC Robotics Roadmap framework [115] (see Figure 7), where each level reflects increasing system autonomy.

	Farmers’ Needs	Priority	Ability Level (0–8)									Remarks
			Configurability	Adaptability	Interaction Ability	Dependability	Motion Ability	Manipulation Ability	Perception Ability	Decisional Autonomy	Cognitive Ability
Operational	Precision seeding	High	3–4	3	2	3	6	4	5	3–4	3	Focus remains on physical precision while decision making and interaction abilities are limited.
	Weed control	High	3–4	4	2	3	6–7	5–6	6–7	5–6	3–4	Shows high precision and motion abilities but limited scalability and cognition for various crops.
	Crop and soil monitoring	High	3	3	2	3	6	4–5	6–7	5–6	4	Accurate data collection but limited ability to make informed, real-time decisions based on collected data.
	Plant health analysis	High	3	3	2	3	6	4–5	6–7	5–6	4	Accurate data collection but limited ability to make informed, real-time decisions based on collected data.
	Spot spraying	Medium	3–4	3	2	3	6	3	5–6	4–5	4	Support precise spot spraying but limited adaptability and scalability for various scenarios.
	Selective harvesting	Medium	3–4	3	5	2	7	6–7	6–7	5–6	4–5	Available solutions have high manipulation and perception abilities but lack in speed due to limited adaptability and cognition.
	Pest control	Medium	3	3	2	3	5–6	3–4	5–6	4	4	Precise pest control are commercially available but limited adaptability and scalability for various scenarios.
Technological	Tools integration	High	4	4	3	2	3–4	2–3	3–4	3–4	2	Some commercial robots support integration with existing tools, but context-aware autonomous integration is yet to be seen.
	System robustness	Medium			3	3–4	5–6	3	4–5	3–4	2–3	Current robots are reasonably robust in farming operations but innovation is required to enhance adaptability in extreme conditions.
	Equipment maintenance	Medium				2–3						Limited software maintenance to handle specific task or mission errors but complex solution to perform hardware maintenance.
	System scalability	Medium	2	3	3	2					1	Few robots can adapt to and scale with farming operations to some extent but limited reliability and cognitive reasoning.
Economic	Field mapping and yield estimation	High	3–4	3	2	4	6	4–5	6–7	4–5	3–4	Well-supported by existing solution in terms of perception and motion ability while cognitive and interaction abilities should improve for advanced decision making.
	Low cost solutions	High	2	2	2	2	5	3	3–4	3–4	2–3	Low-cost robots provide basic configurability, and perception with sufficient motion ability but are limited in more demanding situations.
	Low cost labor work	Medium										Current robots, with limited adaptability and cognitive abilities, are better suited for basic, repetitive tasks than complex labor-intensive work.
Data Management	Actionable insights	High				2–3				2–3	2–3	Current solutions struggle to provide actionable insights due to low decisional autonomy, and cognitive abilities, limiting their effectiveness in autonomous data interpretation.
	Data collection & Management	Medium	3–4	3	3	2–3	6	4	5–6	3–4	3–4	Precise in data collection, but still require human oversight for complex data management tasks due to limited decisional autonomy and cognitive processing.
Environmental	Accurate nutrient analysis	High	2	3	2–3	2–3			5–6	4–5	3–4	Solutions aid in nutrient analysis but are limited by moderate interaction and cognitive abilities, restricting full autonomy and accuracy.
	Soil compaction reduction	High					5–6		3–4	3–4	3–4	Some small lightweight solutions are available but limited to specific operations.
	Sustainable farming solutions	Medium	3–4	4	4	4	6	4	5	5	4–5	Current robots support sustainable farming but improved configurability is needed for flexible application of sustainable practices.
Educational	Farmer training	High										Limited training and awareness programs are available to educate farmers about the capabilities of smart solution in agriculture.
	Adoption of new technologies	Medium										Limited on-field testing and pilot programs are available to facilitate the adoption of smart technologies in agriculture.
Regulatory	Compliance with regulations	Medium				2–3						Required concise and clear regulations for integrating smart robotic solutions into agriculture and to positively facilitate the adoption of these technologies.
Safety	Worker safety	High			2–3	2–3						Clear Safety regulations are required for robotic solutions in agriculture to ensure the safe deployment and operation of robots.

provides a structured comparison of the needs of farmers and current robotic technology capabilities, outlining key strengths, gaps, and limitations. The analysis uses the core system abilities and the corresponding ability levels outlined in the H2020 Robotics Multi-Annual Roadmap for Agricultural Robotics [115]. Figure 7 shows these nine main ability domains: configurability, adaptability, perception, manipulation, motion, dependability, decisional autonomy, cognitive abilities, and interaction abilities. These abilities are rated on a standardized scale from 0 to 8. Each level indicates a higher capability, starting with basic (0) and extending up to fully autonomous and context-aware/cognitively enabled (8). This framework was chosen for its thoroughness, relevance to agricultural environments, and its fit with robotics evaluation standards in European research programs. The needs of farmers identified in Section 4 are matched to these main system abilities.

The ability levels in Table 5 are assigned based on a comparison of the technological capabilities of commercial robotic solutions (Table 3) and findings from field studies (Table 4). Each need of a farmer was linked to one or more system abilities defined in the SPARC Robotics Roadmap (Figure 7). A corresponding ability level from 0 to 8 was assigned based on the observed effectiveness and real-world use. These levels reflect the current state of deployed or deployable technologies, not theoretical possibilities. When multiple systems met the same need with different capabilities, an average or conservative rating was used to determine the ability level. Contextual notes were added in the remarks column to interpret the findings.

Farmers rely on various time-sensitive and repetitive tasks, such as seeding, weeding, pest control, soil health monitoring, and selective harvesting, that require high precision, efficiency, and adaptability. After reviewing the commercial solutions and technological innovations in academia, we can observe that robotic solutions demonstrate relatively strong motion (6–7) and perception abilities (5–7) in these areas, enabling accurate field navigation and data collection. For example, weed control systems score 6–7 in perception and motion, close to fully autonomous motion and perception, reflecting their ability to detect and mechanically remove weeds precisely. However, decisional autonomy and cognitive abilities generally remain moderate (3–6), indicating that most systems rely heavily on human oversight for real-time decision making and adaptive responses. Selective harvesting robots exhibit higher manipulation (6–7) and interaction abilities (5), aligning with the delicate handling requirements, but suffer from limited speed and adaptability, reducing operational efficiency compared to manual labor. Similarly, pest control robots have demonstrated effective detection and intervention strategies, yet they lack scalability across different crop types due to their task-specific designs. Despite these advancements, most operational robotic systems are built for specific applications rather than adaptable multi-tasking, increasing the overall cost and complexity for farmers. These operational gaps underscore a critical need for enhanced AI-driven autonomy and multi-functionality to accommodate diverse crop types and dynamic field conditions.

Our interview data and the literature highlight that for farmers, the viability of agricultural robotics depends on system robustness, scalability, and ease of integration with existing farming operations. While motion and perception technologies have seen substantial improvements, issues related to maintenance, interoperability, and adaptability continue to limit widespread adoption. While configurability and adaptability scores hover around 3–4, indicating moderate ability to adjust to varying contexts, interaction and cognitive skills lag (mostly below 3–4). Robotic systems designed for agriculture often require regular maintenance and recalibration, which can be challenging for farmers with limited technical expertise. This reflects current limitations in tool integration and issues in system maintenance due to complex engineering designs. Farmers express concern about the complexity of system upkeep and interoperability with existing equipment. Many robotic solutions lack interoperability with existing farm equipment, making it difficult for farmers to integrate them into their current workflows. Particularly for small and medium-sized farms with varying operational requirements, the limited adaptability and modularity of current robotic solutions restrict their wider adoption. Although there are some solutions, scalability remains a significant challenge. Most of the robots are designed to target specific farm sizes and crop types, which limits their applicability in various agricultural scenarios. The robustness score (3–4) is also considerably low, indicating a need to enhance robot capabilities in dealing with extreme weather conditions.

The high capital cost of robotic solutions is the primary deterrent to adoption for small and medium-sized farms. The initial high cost, combined with continuing maintenance and software upgrades, discourages smaller farms from adopting these solutions, despite being aware of increased productivity and lower labor costs. There are few low-cost robots available, but their limited configurability (2) and cognitive abilities (2–3) make them useful for simple, repetitive tasks rather than autonomous farming operations. On the other hand, high-end, AI-powered robotic solutions offer precision and autonomy, but are often too costly for small to medium-sized farms. Economic hurdles are exacerbated by the absence of cooperative ownership and Robot-as-a-Service (RaaS) models. The data from surveys and farmer responses highlight that investment in robotic solutions is hampered by high capital costs, ongoing service costs, and uncertainty on return on investment, especially in the absence of subsidies and leasing options.

Advanced sensors, imaging systems, and IoT connectivity capabilities are being increasingly applied to agricultural robots to enhance their operational efficiency and enable them to collect extensive data on environmental changes, crop health, and soil conditions. However, the challenge is turning these data into useful insights without requiring constant human oversight. While current robots have strong perception and data collection capabilities (4–6), their decision-making skills and cognitive abilities (2–3) are still developing. This limits their ability to independently analyze and act on data in real time. Farmers need AI-driven systems that can turn raw data into practical recommendations, like when and where to irrigate, fertilize, or apply pesticides, without needing human interpretation. Right now, most robots depend on cloud-based dashboards or operator analysis, which delays responses and lowers the value of real-time sensing. To close this gap, we need to integrate better machine learning models and onboard AI that can make smart, autonomous decisions based on context.

Sustainability is becoming more important in agriculture, and robots are essential for cutting down on chemical use, reducing soil damage, and making better use of resources. Robotic systems help sustainability by applying nutrients accurately, reducing soil compaction, and supporting eco-friendly practices. However, their moderate configurability and interaction abilities show the need for improved system flexibility to handle different environmental conditions. Lightweight robotic solutions can help reduce soil compaction, but they struggle with scalability. Energy efficiency and power management are ongoing issues because of battery life limitations, which researchers are addressing through solar-powered and hybrid energy systems. Survey data consistently points to a lack of farmer training and limited exposure to automation as major obstacles to adoption. Despite their potential, robotic solutions are not widely adopted by farmers due to a lack of knowledge, limited training opportunities, and difficulties in integrating automation into their existing farming operations. Farmers believe that training provided by manufacturers does not sufficiently build their confidence in using and maintaining robotic solutions. Furthermore, human–robot interaction capabilities are rated as low, which complicates collaboration and interaction with robots.

The adoption of robotic solutions in agriculture also relies on regulations, safety standards, and risk management. Farmers emphasize the importance of clear regulations and safety guidelines to ensure the safe use of robots in shared spaces. Interaction and reliability abilities receive low ratings, indicating that safety features are still in development. Without clear regulations and robust safety measures, liability concerns could hinder the adoption of these technologies. The data-driven mapping reveals a clear pattern: current agricultural robots are highly precise and perceptive in basic automation but fall short in areas such as full autonomy, adaptability, cognitive decision making, and human interaction. Economic and educational challenges make these technological gaps even wider. This highlights the necessity for more progress in AI-driven automation, modular robotic systems, and flexible solutions. Addressing these challenges is crucial to integrating robotic capabilities with the practical needs of farming.

7. Discussion and Conclusions

Building on the data-driven insights presented in Section 6, this section highlights the significant findings that could lead to the advancement of agricultural robotic solutions more suited to the operational needs of farmers. This study presents a multi-step framework for aligning the needs of farmers with current robotic advancements by evaluating commercial solutions and field-tested academic solutions against the identified needs of farmers, as determined through a qualitative analysis of the survey literature and in-depth interviews with farmers. This study fills a significant gap by systematically aligning the robotic core abilities to the real-world farm requirements. This includes identifying areas where existing solutions are lacking in terms of functionality, usability, adaptability, adoption readiness, and integration with current farming operations.

Although robotics has undoubtedly increased accuracy and productivity in various agricultural tasks, such as seeding, weeding, and monitoring, its efficacy is still diminished by its limited decision making, flexibility, and context awareness. This paper highlights that research and developments are more focused on autonomous navigation and perception capabilities, with less progress in cognitive abilities and decision making. This imbalance in abilities limits the robot’s capability to make autonomous cognitive decisions in the field or provide quick responses to unforeseen situations, which were persistent issues noted in both case studies and farmer interviews. In order to address these challenges, we need to shift toward modular, multi-purpose robotic solutions with adjustable tools that facilitate a range of diverse tasks. This modularity reduces the need for multiple specialized machines for different tasks, which in turn lowers costs and encourages adoption. Crucially, for making cognitive decisions in the field and achieving context awareness, along with actionable insights, it is essential to incorporate powerful and user-friendly AI capabilities.

From an economic perspective, high capital costs remain a major barrier, particularly for small and medium farms. As this review shows, most available solutions are priced above practical levels, and many lack scalable financial models. Innovative ownership strategies like Robot-as-a-Service (RaaS), cooperative leasing, and targeted government subsidies will be crucial for broad adoption. In terms of data management, this paper shows a gap between the impressive sensing abilities of robots and their capacity to interpret data independently. Farmers have expressed frustration over data that is not fully utilized or that requires technical skills to understand. This highlights the need for smooth AI-driven data systems that link robotic sensors to farm management decisions through user-friendly interfaces and immediate, actionable suggestions. Sustainability was a recurring theme in both farmer responses and reviews of surveys and case studies. Lightweight designs, renewable power systems like solar energy, and protective movement patterns for soil are still uncommon. Investing in energy-efficient, low-compaction robotic designs can help maintain soil health, reduce chemical use, and enhance resilience to climate change.

Regarding farmer education and user support, this study emphasizes that successful adoption depends on more than just technical ability; it requires trust, training, and simplicity. Most current training materials are either too technical or overly generic. There is a need for localized, hands-on programs that empower farmers not only to operate but also to adapt and maintain these systems. Finally, changing regulations and safety standards will be vital for expanding robotic use. Many farmers mentioned legal uncertainties and liability issues as barriers to adoption. Clear operational guidelines, certification processes, and cross-border standards can help build the trust needed for widespread acceptance.

The main contribution of this paper is to make the connection between robotic capability and farmer needs clear, structured, and measurable. It links previously disconnected areas, technological advancements, field validation, and user expectations to guide future development. By aligning technological maturity with the actual needs of end users, this work can benefit researchers, manufacturers, policymakers, and agricultural extension networks.

Table 6. Summary of key findings, current development status, and priority actions for advancing agricultural robotics in row crop systems.

Key Domain	Current Development Status	Recommended Strategic Actions
Motion and perception abilities	High (6–7)	Maintain and expand strengths in navigation, weed detection, and imaging; adapt sensing modules to diverse field conditions and under-canopy environments.
Decisional autonomy and cognitive reasoning	Low to Moderate (2–5)	Develop explainable AI for real-time, in-field decision-making; integrate adaptive learning systems that respond to environmental variability without human input.
Manipulation and human–robot interaction	Moderate (3–6)	Design flexible, crop-sensitive manipulators for harvesting and tool use; improve user interfaces and co-working capabilities for collaborative tasks.
System modularity, scalability, and robustness	Moderate (2–4)	Engineer modular toolkits and configurable platforms; design for durability in multi-crop, multi-terrain operations across small and large farms.
Economic accessibility and business models	Limited	Encourage Robot-as-a-Service (RaaS) models, cooperative ownership, and public funding programs; reduce hardware costs via open-source and localized manufacturing.
Data management and actionable analytics	Underutilized	Link sensing data to real-time recommendations using cloud-based and edge-AI; improve integration with farm management systems and support intuitive dashboards.
Environmental impact and sustainability	Promising but fragmented	Prioritize energy-efficient, low-emission robots; promote chemical-free solutions like mechanical weeding and targeted spraying with minimal soil compaction.
Farmer education and skills support	Insufficient	Provide localized, scenario-based training and decision-support tools; invest in farmer co-design programs and accessible learning platforms.
Policy, regulation, and safety standards	Emerging but unclear	Develop internationally aligned safety standards and certification processes; define liability and data-sharing regulations for robotic systems.

offers an overview of the current development status and priority actions to promote the design, accessibility, and adoption of agricultural robots.

8. Summary, Limitations, and Future Research Directions

This study examines the alignment between the needs of farmers and robotic capabilities, highlighting both strengths and key technological gaps in commercial solutions, field-validated academic research, and user feedback. By mapping robotic functions to real-world priorities using a 0–8 ability scale, this paper provides a clear framework for identifying where current systems meet expectations and where they do not. It also connects different areas, such as technology development, farmer usability, and policy considerations. This offers insights that are relevant to researchers, manufacturers, and stakeholders working toward sustainable automation in agriculture.

While motion, perception, and automation technologies have improved significantly, allowing robots to perform tasks like precision seeding, weeding, and crop monitoring, important limitations still exist. Issues with decision-making autonomy, adaptability, and affordability continue to hinder real-world deployment. Many robotic systems still require human intervention for critical decision making, limiting their ability to operate independently in dynamic farm environments. Additionally, most agricultural robots remain task-specific, increasing costs and reducing flexibility. Economic barriers, particularly high initial investment costs, further slow adoption, making robotic solutions less accessible to small and mid-sized farms. Environmental concerns, including energy efficiency and soil impact, also require further innovation to ensure that robotic solutions contribute positively to sustainable farming practices.

Current agricultural robotic systems face significant technological constraints that hinder their broader adoption and effectiveness. Key limitations include insufficient autonomous decision-making capabilities, with AI algorithms often unable to process complex, real-time environmental variability such as sudden weather changes, pest outbreaks, or soil heterogeneity. Manipulation and adaptability are limited; most robots are designed for specific, narrowly defined tasks, lacking the flexibility to handle multiple crop types or unexpected obstacles. Integration challenges persist due to poor interoperability between robotic platforms and existing farm machinery or management software. Additionally, energy efficiency remains suboptimal, as battery life and power management limit operational durations, especially in large-scale farming. The lack of robust, self-maintenance features increases downtime and reliance on technical support, which is problematic for farmers with limited technical expertise. Finally, sensor limitations constrain perception in adverse environmental conditions (e.g., dust, low light), reducing reliability. These technological gaps emphasize the need for advances in AI, modular design, energy systems, and user-friendly interfaces to unlock the full potential of agricultural robotics.

Apart from these technical challenges, some broader challenges such as socio-economic barriers, ethical and environmental concerns, and interdisciplinary collaboration gaps need to be addressed. Farmers frequently lack access to scalable ownership options, such as leasing and Robot-as-a-Service (RaaS) options, and are concerned about their return on investment, as the robotic solutions are not yet fully mature compared to traditional machinery. Adoption is also significantly hampered by speculations around environmental effects from the use of autonomous robots, as well as ethical concerns about worker displacement. Furthermore, the collaboration gap between researchers, engineers, ergonomists, and end-users often results in technically sophisticated solutions that fail to meet the real-world farm requirements and the needs of farmers. To increase usability, trust, and long-term success in the field, user-centered and collaboratively produced solutions are essential.

For farmers, robotic solutions offer opportunities to increase efficiency, reduce labor dependency, and optimize resource use; however, concerns about cost, usability, and adaptability remain obstacles. Farmers require systems that are affordable, easy to integrate, and adaptable across different crops and terrains. Meanwhile, robotic developers must focus on designing modular, multi-functional platforms that allow farmers to switch between various farming tasks without the need for multiple specialized machines. Additionally, AI-driven decision making must evolve to enable robots to autonomously adjust operations based on real-time field conditions, reducing the need for constant human oversight. Manufacturers should prioritize user-centered design, versatile tools, adaptable solutions for different farm sizes and crops, and tested autonomous decision making. They should also focus on interoperability with existing farm equipment to minimize costs and invest in sturdy, low-maintenance designs suitable for various field conditions. Providing scalable, multi-functional solutions and flexible service plans will enhance accessibility and support the long-term use of these solutions. Policymakers and industry stakeholders must also work toward expanding financial incentives, cooperative leasing models, and shared robotic services to make automation more economically viable for all farm sizes.

Looking ahead, future research should prioritize enhancing the decision-making autonomy, adaptability, affordability, and sustainability of robotic solutions. AI advancements will be crucial in transitioning robots from simple automation tools to intelligent, adaptive farm assistants capable of making real-time decisions based on environmental and crop conditions. Lowering costs through scalable manufacturing, open-source software, and government-backed subsidies will further drive adoption, particularly in regions where financial constraints limit technological investments. Additionally, ensuring that robots integrate seamlessly with existing precision agriculture technologies will help maximize their impact. By addressing these key challenges, agricultural robotics can become a transformative tool for sustainable, scalable, and cost-effective farming, supporting long-term food security and economic growth in the agricultural sector.