Search Results (309)

Automated cherry harvesting presents a significant opportunity to overcome the high costs and inefficiencies of manual labor in modern agriculture. However, robotic harvesting in dense canopies requires sophisticated path planning to navigate cluttered branches and selectively pick target fruits. This paper introduces a complete robotic harvesting solution centered on a novel path-planning algorithm: the Multi-Strategy Integrated RRT for Continuous Harvesting Path (MSI-RRTCHP) algorithm. Our system first employs a machine vision system to identify and locate mature cherries, distinguishing them from unripe fruits, leaves, and branches, which are treated as obstacles. Based on this visual data, the MSI-RRTCHP algorithm generates an optimal picking trajectory. Its core innovation is a synergistic strategy that enables intelligent navigation by combining probability-guided exploration, goal-oriented sampling, and adaptive step size adjustments based on the obstacle’s density. To optimize the picking sequence for multiple targets, we introduce an enhanced traversal algorithm ($\sigma$-TSP) that accounts for obstacle interference. Field experiments demonstrate that our integrated system achieved a 90% picking success rate. Compared with established algorithms, the MSI-RRTCHP algorithm reduced the path length by up to 25.47% and the planning time by up to 39.06%. This work provides a practical and efficient framework for robotic cherry harvesting, showcasing a significant step toward intelligent agricultural automation. Full article

The integration of a real-time image processing system using multiple webcams with a variable rate spraying system mounted on the back of an unmanned tractor presents an effective solution to the labor shortage in agriculture. This research aims to design and fabricate a low-cost, variable-rate, remote-controlled sprayer specifically for use in sugarcane fields. The primary method involves the modification of a 15-horsepower tractor, which will be equipped with a remote-control system to manage both the driving and steering functions. A foldable remote-controlled spraying arm is installed at the rear of the unmanned tractor. The system operates by using a webcam mounted on the spraying arm to capture high-angle images above the sugarcane canopy. These images are recorded and processed, and the data is relayed to the spraying control system. As a result, chemicals can be sprayed on the sugarcane accurately and efficiently based on the insights gained from image processing. Tests were conducted at various nozzle heights of 0.25 m, 0.5 m, and 0.75 m. The average system efficiency was found to be 85.30% at a pressure of 1 bar, with a chemical spraying rate of 36 L per hour and a working capacity of 0.975 hectares per hour. The energy consumption recorded was 0.161 kWh, while fuel consumption was measured at 6.807 L per hour. In conclusion, the development of the remote-controlled variable rate sprayer mounted on an unmanned tractor enables immediate and precise chemical application through remote control. This results in high-precision spraying and uniform distribution, ultimately leading to cost savings, particularly by allowing for adjustments in nozzle height from a minimum of 0.25 m to a maximum of 0.75 m from the target. Full article

Direct seeding has gained prominence as a labor-efficient and environmentally sustainable alternative to conventional transplanting in rice cultivation. In direct seeding systems, early-stage management is crucial for stable seedling establishment, with sowing uniformity measured by seed counts being a critical indicator of success. However, conventional manual seed counting methods are time-consuming, prone to human error, and impractical for large-scale or repetitive tasks, necessitating advanced automated solutions. Recent advances in computer vision technologies and precision agriculture tools, offer the potential to automate seed counting tasks. Nevertheless, challenges such as domain discrepancies and limited labeled data restrict robust real-world deployment. To address these issues, we propose a density estimation-based seed counting framework integrating semi-supervised learning and background augmentation. This framework includes a cost-effective data acquisition system enabling diverse domain data collection through indoor background augmentation, combined with semi-supervised learning to utilize augmented data effectively while minimizing labeling costs. The experimental results on field data from unknown domains show that our approach reduces seed counting errors by up to 58.5% compared to conventional methods, highlighting its potential as a scalable and effective solution for agricultural applications in real-world environments. Full article

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. Full article

This study presents a cost-effective, carbon-negative construction material for affordable housing, developed entirely from locally available agricultural wastes: rice husk ash, wood ash, and rice straw—materials often problematic to dispose of in many African regions. Rice husk ash provides high amorphous silica, acting as a strong pozzolanic agent. Wood ash contributes calcium oxide and alkalis to serve as a reactive binder, while rice straw functions as a lightweight organic filler, enhancing thermal insulation and indoor climate comfort. These materials undergo natural pozzolanic reactions with water, eliminating the need for Portland cement—a major global source of anthropogenic CO₂ emissions (~900 kg CO₂/ton cement). This process is inherently carbon-negative, not only avoiding emissions from cement production but also capturing atmospheric CO₂ during lime carbonation in the hardening phase. Field trials in Kenya confirmed the composite’s sufficient structural strength for low-cost housing, with added benefits including termite resistance and suitability for unskilled laborers. In a collaboration between the University of Tartu and Kenyatta University, a semi-automatic mixing and casting system was developed, enabling fast, low-labor construction of full-scale houses. This innovation aligns with Kenya’s Big Four development agenda and supports sustainable rural development, post-disaster reconstruction, and climate mitigation through scalable, eco-friendly building solutions. Full article

The identification and detection of planting holes, combined with UAV technology, provides an effective solution to the challenges posed by manual counting, high labor costs, and low efficiency in large-scale planting operations. However, existing target detection algorithms face difficulties in identifying planting holes based on their edge features, particularly in complex environments. To address this issue, a target detection network named YOLO-PH was designed to efficiently and rapidly detect planting holes in complex environments. Compared to the YOLOv8 network, the proposed YOLO-PH network incorporates the C2f_DyGhostConv module as a replacement for the original C2f module in both the backbone network and neck network. Furthermore, the ATSS label allocation method is employed to optimize sample allocation and enhance detection effectiveness. Lastly, our proposed Siblings Detection Head reduces computational burden while significantly improving detection performance. Ablation experiments demonstrate that compared to baseline models, YOLO-PH exhibits notable improvements of 1.3% in mAP50 and 1.1% in mAP50:95 while simultaneously achieving a reduction of 48.8% in FLOPs and an impressive increase of 26.8 FPS (frames per second) in detection speed. In practical applications for detecting indistinct boundary planting holes within complex scenarios, our algorithm consistently outperforms other detection networks with exceptional precision (F1-score = 0.95), low computational cost, rapid detection speed, and robustness, thus laying a solid foundation for advancing precision agriculture. Full article

Apple harvesting faces increasing challenges owing to rising labor costs and the limited seasonal workforce availability, highlighting the need for robotic harvesting solutions in precision agriculture. This study presents a 6-DOF robotic arm system designed for harvesting in dwarf hedge-planted orchards, featuring a lightweight perception module, a task-adaptive motion planner, and an adaptive soft gripper. A lightweight approach was introduced by integrating the Faster module within the C2f module of the You Only Look Once (YOLO) v8n architecture to optimize the real-time apple detection efficiency. For motion planning, a Dynamic Temperature Simplified Transition Adaptive Cost Bidirectional Transition-Based Rapidly Exploring Random Tree (DSA-BiTRRT) algorithm was developed, demonstrating significant improvements in the path planning performance. The adaptive soft gripper was evaluated for its detachment and load-bearing capacities. Field experiments revealed that the direct-pull method at 150 mN·m torque outperformed the rotation-pull method at both 100 mN·m and 150 mN·m. A custom control system integrating all components was validated in partially controlled orchards, where obstacle clearance and thinning were conducted to ensure operation safety. Tests conducted on 80 apples showed a 52.5% detachment success rate and a 47.5% overall harvesting success rate, with average detachment and full-cycle times of 7.7 s and 15.3 s per apple, respectively. These results highlight the system’s potential for advancing robotic fruit harvesting and contribute to the ongoing development of autonomous agricultural technologies. Full article

Insect pests remain a major threat to agricultural productivity, particularly in open-field cropping systems where conventional monitoring methods are labor-intensive and lack scalability. This study presents the design, implementation, and field evaluation of a neural network-guided smart trap specifically developed to monitor and selectively capture nocturnal insect pests under real agricultural conditions. The proposed trap integrates light and rain sensors, servo-controlled mechanical gates, and a single-layer perceptron neural network deployed on an ATmega-2560 microcontroller by Microchip Technology Inc. (Chandler, AZ, USA). The perceptron processes normalized sensor inputs to autonomously decide, in real time, whether to open or close the gate, thereby enhancing the selectivity of insect capture. The system features a removable tray containing a food-based attractant and yellow and green LEDs designed to lure target species such as moths and flies from the orders Lepidoptera and Diptera. Field trials were conducted between June and August 2023 in La Barca, Jalisco, Mexico, under diverse environmental conditions. Captured insects were analyzed and classified using the iNaturalist platform, with the successful identification of key pest species including Tetanolita floridiana, Synchlora spp., Estigmene acrea, Sphingomorpha chlorea, Gymnoscelis rufifasciata, and Musca domestica, while minimizing the capture of non-target organisms such as Carpophilus spp., Hexagenia limbata, and Chrysoperla spp. Statistical analysis using the Kruskal–Wallis test confirmed significant differences in capture rates across environmental conditions. The results highlight the potential of this low-cost device to improve pest monitoring accuracy, and lay the groundwork for the future integration of more advanced AI-based classification and species recognition systems targeting nocturnal Lepidoptera and other pest insects. Full article

The agricultural products trade network is essentially a complex adaptive system formed by nonlinear interactions between countries. Based on the complex system theory, this study reveals the dynamic self-organization law of the RCEP regional agricultural products trade network by using the panel data of RCEP agricultural products export trade from 2000 to 2023, combining social network analysis (SNA) and the temporal exponential random graph model (TERGM). The results show the following: (1) The RCEP agricultural products trade network presents a “core-edge” hierarchical structure, with China as the core hub to drive regional resource integration and ASEAN countries developing into secondary core nodes to deepen collaborative dependence. (2) The “China-ASEAN-Japan-Korea “riangle trade structure is formed under the RCEP framework, and the network has the characteristics of a “small world”. The leading mode of South–South trade promotes the regional economic order to shift from the traditional vertical division of labor to multiple coordination. (3) The evolution of trade network system is driven by multiple factors: endogenous reciprocity and network expansion are the core structural driving forces; synergistic optimization of supply and demand matching between economic and financial development to promote system upgrading; geographical proximity and cultural convergence effectively reduce transaction costs and enhance system connectivity, but geographical distance is still the key system constraint that restricts the integration of marginal countries. This study provides a systematic and scientific analytical framework for understanding the resilience mechanism and structural evolution of regional agricultural trade networks under global shocks. Full article

Water resources have become a critical factor limiting agricultural development and ecological health in arid regions. The ecological efficiency of agricultural water use (EEAWU) serves as an indicator of the sustainable utilization of agricultural water resources, taking into account both economic output and environmental impact. This paper, grounded in the social–ecological system (SES) framework, integrates multidimensional variables related to social behavior, economic decision-making, and ecological constraints to construct an analytical system that examines the impact mechanism of farmers’ part-time employment on the EEAWU. Utilizing survey data from 448 farmers in the western Tarim River Basin, and employing the super-efficiency SBM model alongside Tobit regression for empirical analysis, the study reveals the following findings: (1) the degree of farmers’ part-time employment is significantly negatively correlated with EEAWU (β = −0.041, p < 0.05); (2) as the extent of part-time employment increases, farmers adversely affect EEAWU by altering agricultural labor allocation, adjusting crop structures, and inadequately adopting water-saving measures; (3) farm size plays a negative moderating role in the relationship between farmers’ part-time engagement and the EEAWU, where scale expansion can alleviate the EEAWU losses associated with part-time employment through cost-sharing and factor substitution mechanisms. Based on these findings, it is recommended to enhance the land transfer mechanism, promote agricultural social services, implement tiered water pricing and water-saving subsidy policies, optimize crop structures, and strengthen environmental regulations to improve EEAWU in arid regions. Full article

The impact of urbanization on the agricultural economy has long attracted scholarly attention. Taking China as a case, this study investigates the relationship between urbanization and agricultural development under the dual progress of urbanization and the rural revitalization strategy. Based on panel data from 31 mainland provinces, this paper measures agricultural economic efficiency using the global slack-based measure (SBM) model and employs quantile regression to systematically analyze the influence of various urbanization factors across different levels of agricultural efficiency. A Tobit regression model is further adopted for robustness checks. The results show that representative urbanization factors, such as the proportion of urban population and the prevalence of higher education, exert significant negative impacts on agricultural efficiency, particularly in regions with higher efficiency levels. Freight volume has a significantly negative effect in regions with medium and low efficiency, while freight turnover negatively impacts medium- to high-efficiency areas. In contrast, improvements in healthcare services and digital infrastructure are found to consistently enhance agricultural efficiency. Although the corporatization of agriculture is often regarded as a key outcome of urbanization, its efficiency-improving effect is not statistically significant in most models and is mainly concentrated in high-efficiency regions. Overall, the improvement in China’s agricultural economic efficiency relies more on direct support from the rural revitalization strategy, while rapid urbanization has failed to bring substantial benefits and has even led to structural negative effects. These adverse outcomes may stem from the rapid occupation of suburban farmland, increased logistics costs due to the relocation of agricultural activities, and the ineffective absorption of surplus rural labor. This study highlights the need for future urbanization policies in China to pay greater attention to the coordinated development of the agricultural economy. The methods and findings of this research also provide reference value for other developing regions facing similar urbanization-agriculture dynamics. Full article

Continuing improvements in our understanding of ancient climate change renders it necessary to expand our toolkit for exploring human responses to climatic shifts. Currently, archaeological methods for exploring the resilience of ancient human agricultural systems—in addition to strategies for managing risk and/or uncertainty—are frustratingly limited in comparison to the rich ethnographic record of how humans have navigated climatic stressors. This article proposes that legumes might provide a new, albeit woefully understudied, vector for potential analyses, especially given their central role in traditional agricultural systems as a buffer against environmental stress. The peculiar agronomic character of legumes, especially among the widely cultivated varieties that are toxic in their unrefined state, could allow for robust hypotheses about agricultural strategies to be tested against our paleoclimate record. Importantly, these hypotheses could be tested against a wide variety of models of human–plant and human–environment interaction, as they could be based on labor costs rather than assumptions of ancient cultural preference. Legumes, however, present particular difficulties as objects of analyses, and therefore some methodological cautions are in order. Consequently, instead of proposing and testing hypotheses, this article seeks instead to inspire future research in relation to our constantly improving data. Full article

This study explores the potential of freely available tools for collecting, processing, and applying information in the management of mechanized fieldwork. A hierarchical approach was developed, integrating operational, logistical, and strategic levels of decision-making based on crop type, land conditions, machinery, labor, and time constraints. Various technological and technical solutions were evaluated through simulations and manual data processing. The proposed methodology was applied to a real-world case in Kalipetrovo, Bulgaria. The results include a 3.5-fold reduction in required tractors and a 50% decrease in tractor driver needs, achieved through extended working hours and shift scheduling. Additional benefits were identified from replacing conventional tillage with deep tillage, resulting in higher fuel consumption but improved soil preparation. Detailed resource schedules were created for machinery, labor, and fuel, highlighting seasonal peaks and optimization opportunities. The approach relies on spreadsheets and free AI-assisted platforms, proving to be a low-cost, accessible solution for mid-sized farms lacking advanced digital infrastructure. The findings demonstrate that structured information integration can support the effective renewal and utilization of tractor and machinery fleets while offering a scalable basis for decision support systems in agricultural engineering. Full article

Rice is one of the three primary staple crops worldwide. The accurate monitoring of its key growth stages is crucial for agricultural management, disaster early warning, and ensuring food security. The effective collection of ground reference data is a critical step for monitoring rice growth stages using satellite imagery, traditionally achieved through labor-intensive field surveys. Here, we propose utilizing UAVs as an alternative means to collect spatially continuous ground reference data across larger areas, thereby enhancing the efficiency and scalability of training and validation processes for rice growth stage mapping products. The UAV data collection involved the Nanchuan, Yongchuan, Tongnan, and Kaizhou districts of Chongqing City, encompassing a total area of 377.5 hectares. After visual interpretation, centimeter-level high-resolution labels of the key rice growth stages were constructed. These labels were then mapped to Sentinel-2 imagery through spatiotemporal matching and scale conversion, resulting in a reference dataset of Sentinel 2 data that covered growth stages such as jointing and heading. Furthermore, we employed 30 vegetation index calculation methods to explore 48,600 spectral band combinations derived from 10 Sentinel-2 spectral bands, thereby constructing a series of novel vegetation indices. Based on the maximum relevance minimum redundancy (mRMR) algorithm, we identified an optimal subset of features that were both highly correlated with rice growth stages and mutually complementary. The results demonstrate that multi-feature modeling significantly enhanced classification performance. The optimal model, incorporating 300 features, achieved an F1 score of 0.864, representing a 2.5% improvement over models based on original spectral bands and a 38.8% improvement over models using a single feature. Notably, a model utilizing only 12 features maintained a high classification accuracy (F1 = 0.855) while substantially reducing computational costs. Compared with existing methods, this study constructed a large-scale ground-truth reference dataset for satellite imagery based on UAV observations, demonstrating its potential as an effective technical framework and providing an effective technical framework for the large-scale mapping of rice growth stages using satellite data. Full article

Automation in agricultural machinery, underpinned by the integration of advanced technologies, is revolutionizing sustainable farming practices. Key enabling technologies include multi-source positioning fusion (e.g., RTK-GNSS/LiDAR), intelligent perception systems utilizing multispectral imaging and deep learning algorithms, adaptive control through modular robotic systems and bio-inspired algorithms, and AI-driven data analytics for resource optimization. These technological advancements manifest in significant applications: autonomous field machinery achieving lateral navigation errors below 6 cm, UAVs enabling targeted agrochemical application, reducing pesticide usage by 40%, and smart greenhouses regulating microclimates with ±0.1 °C precision. Collectively, these innovations enhance productivity, optimize resource utilization (water, fertilizers, energy), and mitigate critical labor shortages. However, persistent challenges include technological heterogeneity across diverse agricultural environments, high implementation costs, limitations in adaptability to dynamic field conditions, and adoption barriers, particularly in developing regions. Future progress necessitates prioritizing the development of lightweight edge computing solutions, multi-energy complementary systems (integrating solar, wind, hydropower), distributed collaborative control frameworks, and AI-optimized swarm operations. To democratize these technologies globally, this review synthesizes the evolution of technology and interdisciplinary synergies, concluding with prioritized strategies for advancing agricultural intelligence to align with the Sustainable Development Goals (SDGs) for zero hunger and responsible production. Full article