Search Results (605)

Nitrous oxide (${\mathrm{N}}_2\mathrm{O}$) emissions from agriculture are rising due to increased fertilizer use and intensive farming, posing a major challenge for climate mitigation. This study introduces a novel reinforcement learning (RL) framework to optimize farm management strategies that balance crop productivity with environmental impact, particularly ${\mathrm{N}}_2\mathrm{O}$ emissions. By modeling agricultural decision-making as a partially observable Markov decision process (POMDP), the framework accounts for uncertainties in environmental conditions and observational data. The approach integrates deep Q-learning with recurrent neural networks (RNNs) to train adaptive agents within a simulated farming environment. A Probabilistic Deep Learning (PDL) model was developed to estimate ${\mathrm{N}}_2\mathrm{O}$ emissions, achieving a high Prediction Interval Coverage Probability (PICP) of 0.937 within a 95% confidence interval on the available dataset. While the PDL model’s generalizability is currently constrained by the limited observational data, the RL framework itself is designed for broad applicability, capable of extending to diverse agricultural practices and environmental conditions. Results demonstrate that RL agents reduce ${\mathrm{N}}_2\mathrm{O}$ emissions without compromising yields, even under climatic variability. The framework’s flexibility allows for future integration of expanded datasets or alternative emission models, ensuring scalability as more field data becomes available. This work highlights the potential of artificial intelligence to advance climate-smart agriculture by simultaneously addressing productivity and sustainability goals in dynamic real-world settings. 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

The Solar Agro Savior (SAS) is an innovative solution that is assisted by drones for the sustainable utilization of water and plant disease observation in the agriculture sector. This system integrates an alerting mechanism for humidity, moisture, and temperature variations, which affect the plants’ health and optimization in water utilization, which enhances plant yield productivity. A significant feature of the system is the efficient monitoring system in a larger region through drones’ high-resolution cameras, which enables real-time, efficient response and alerting for environmental fluctuations to the authorities. The machine learning algorithm, particularly recurrent neural networks, which is a pioneer with agriculture and pest control, is incorporated for intelligent monitoring systems. The proposed system incorporates a specialized form of a recurrent neural network, Long Short-Term Memory (LSTM), which effectively addresses the vanishing gradient problem. It also utilizes an attention-based mechanism that enables the model to assign meaningful weights to the most important parts of the data sequence. This algorithm not only enhances water utilization efficiency but also boosts plant yield and strengthens pest control mechanisms. This system also provides sustainability through the re-utilization of water and the elimination of electric energy through solar panel systems for powering the inbuilt irrigation system. A comparative analysis of variant algorithms in the agriculture sector with a machine learning approach was also illustrated, and the proposed system yielded 99% yield accuracy, a 97.8% precision value, 98.4% recall, and a 98.4% F1 score value. By encompassing solar irrigation and artificial intelligence-driven analysis, the proposed algorithm, Solar Argo Savior, established a sustainable framework in the latest agricultural sectors and promoted sustainability to protect our environment and community. Full article

In the era of smart agriculture, accurate identification of plant species is critical for effective crop management, biodiversity monitoring, and the sustainable use of medicinal resources. However, existing deep learning approaches often underperform when applied to fine-grained plant classification tasks due to the lack of domain-specific, high-quality datasets and the limited representational capacity of traditional architectures. This study addresses these challenges by introducing a novel, well-curated leaf image dataset consisting of 39 classes of medicinal and aromatic plants collected from the Aromatic and Medicinal Plant Research Station in Odakkali, Kerala, India. To overcome performance bottlenecks observed with a baseline Convolutional Neural Network (CNN) that achieved only 44.94% accuracy, we progressively enhanced model performance through a series of architectural innovations. These included the use of a pre-trained VGG16 network, data augmentation techniques, and fine-tuning of deeper convolutional layers, followed by the integration of Squeeze-and-Excitation (SE) attention blocks. Ultimately, we propose a hybrid deep learning architecture that combines VGG16 with Batch Normalization, Gated Recurrent Units (GRUs), Transformer modules, and Dilated Convolutions. This final model achieved a peak validation accuracy of 95.24%, significantly outperforming several baseline models, such as custom CNN (44.94%), VGG-19 (59.49%), VGG-16 before augmentation (71.52%), Xception (85.44%), Inception v3 (87.97%), VGG-16 after data augumentation (89.24%), VGG-16 after fine-tuning (90.51%), MobileNetV2 (93.67), and VGG16 with SE block (94.94%). These results demonstrate superior capability in capturing both local textures and global morphological features. The proposed solution not only advances the state of the art in plant classification but also contributes a valuable dataset to the research community. Its real-world applicability spans field-based plant identification, biodiversity conservation, and precision agriculture, offering a scalable tool for automated plant recognition in complex ecological and agricultural environments. Full article

Accurate identification of predator–pest relationships is essential for implementing effective and sustainable biological control in agriculture. However, existing image-based methods struggle to recognize insect co-occurrence under complex field conditions, limiting their ecological applicability. To address this challenge, we propose a hybrid deep learning framework that integrates convolutional neural networks (CNNs) and Transformer architectures for multi-label recognition of predator–pest combinations. The model leverages a novel co-occurrence attention mechanism to capture semantic relationships between insect categories and employs a pairwise label matching loss to enhance ecological pairing accuracy. Evaluated on a field-constructed dataset of 5,037 images across eight categories, the model achieved an F1-score of 86.5%, mAP50 of 85.1%, and demonstrated strong generalization to unseen predator–pest pairs with an average F1-score of 79.6%. These results outperform several strong baselines, including ResNet-50, YOLOv8, and Vision Transformer. This work contributes a robust, interpretable approach for multi-object ecological detection and offers practical potential for deployment in smart farming systems, UAV-based monitoring, and precision pest management. Full article

Agriculture is the foundational industry for human survival, profoundly impacting economic, ecological, and social dimensions. In the face of global challenges such as rapid population growth, resource scarcity, and climate change, achieving technological innovation in agriculture and advancing smart farming have become increasingly critical. In recent years, deep learning and computer vision have developed rapidly. Key areas in computer vision—such as deep learning-based image processing, object detection, and multimodal fusion—are rapidly transforming traditional agricultural practices. Processes in agriculture, including planting planning, growth management, harvesting, and post-harvest handling, are shifting from experience-driven methods to digital and intelligent approaches. This paper systematically reviews applications of deep learning and computer vision in agricultural growth management over the past decade, categorizing them into four key areas: crop identification, grading and classification, disease monitoring, and weed detection. Additionally, we introduce classic methods and models in computer vision and deep learning, discussing approaches that utilize different types of visual information. Finally, we summarize current challenges and limitations of existing methods, providing insights for future research and promoting technological innovation in agriculture. Full article

Smart farming has emerged as a promising solution to address challenges such as climate change, population growth, and limited agricultural infrastructure. To enhance the operational efficiency of smart farms, this paper proposes an integrated system that combines Recurrent Neural Networks (RNNs) and Unmanned Aerial Vehicles (UAVs). The proposed framework forecasts future resource shortages using an RNN model and recent environmental data collected from the field. Based on these forecasts, the system schedules a resource supply plan and determines the UAV path by considering both dynamic energy consumption and priority levels, aiming to maximize the efficiency of the resource supply. Experimental results show that the proposed integrated smart farm framework achieves an average reduction of 81.08% in the supply miss rate. This paper demonstrates the potential of an integrated AI- and UAV-based smart farm management system in achieving both environmental responsiveness and operational optimization. Full article

The integration of artificial intelligence into sustainable agriculture holds significant potential to transform traditional agricultural practices. This transformation of agricultural practices through AI directly intersects with several critical sustainable development goals, such as Climate Action (SDG13), Life Below Water (SDG 14), and Life on Land (SDG 15). However, such implementations are fraught with multifaceted challenges. This study explores the technological, organizational, and environmental challenges confronting top management in the agricultural sector utilizing the technological–organizational–environmental framework. As interest in AI-enabled sustainable initiatives continues to rise globally, this exploration is timely and relevant. The study employs an interpretive case study approach, drawing insights from a carbon sequestration project within the agricultural sector where AI technologies have been integrated to support sustainability goals. The findings reveal six key challenges: sustainable policy inconsistency, AI experts lacking farming knowledge, farmers’ resistance to change, limited knowledge and expertise to deploy AI, missing links in the existing system, and transition costs, which often hinder the achievement of long-term sustainability outcomes. This study emphasizes the importance of field realities and cross-disciplinary collaboration to optimize the role of AI in sustainability efforts. Full article

Agriculture 5.0 represents a shift towards a more sustainable agricultural model, integrating Artificial Intelligence (AI), the Internet of Things (IoT), robotics, and blockchain technologies to enhance productivity and resource management, with an emphasis on social and environmental resilience. This article explores how the evolution of wireless technologies to sixth-generation networks (6G) can support innovation in Colombia’s agricultural sector and foster rural advancement. The study follows three main phases: search, analysis, and selection of information. In the search phase, key government policies, spectrum management strategies, and the relevant literature from 2020 to 2025 were reviewed. The analysis phase addresses challenges such as spectrum regulation and infrastructure deployment within the context of a developing country. Finally, the selection phase evaluates technological readiness and policy frameworks. Findings suggest that 6G could revolutionize Colombian agriculture by improving connectivity, enabling real-time monitoring, and facilitating precision farming, especially in rural areas with limited infrastructure. Successful 6G deployment could boost agricultural productivity, reduce socioeconomic disparities, and foster sustainable rural development, contingent on aligned public policies, infrastructure investments, and human capital development. Full article

Winter wheat yield prediction is critical for optimizing field management plans and guiding agricultural production. To address the limitations of conventional manual yield estimation methods, including low efficiency and poor interpretability, this study innovatively proposes an intelligent yield estimation method based on a convolutional neural network (CNN). A comprehensive two-factor (fertilization × irrigation) controlled field experiment was designed to thoroughly validate the applicability and effectiveness of this method. The experimental design comprised two irrigation treatments, sufficient irrigation (C) at 750 m³ ha⁻¹ and deficit irrigation (M) at 450 m³ ha⁻¹, along with five fertilization treatments (at a rate of 180 kg N ha⁻¹): (1) organic fertilizer alone, (2) organic–inorganic fertilizer blend at a 7:3 ratio, (3) organic–inorganic fertilizer blend at a 3:7 ratio, (4) inorganic fertilizer alone, and (5) no fertilizer control. The experimental protocol employed a DJI M300 RTK unmanned aerial vehicle (UAV) equipped with a multispectral sensor to systematically acquire high-resolution growth imagery of winter wheat across critical phenological stages, from heading to maturity. The acquired multispectral imagery was meticulously annotated using the Labelme professional annotation tool to construct a comprehensive experimental dataset comprising over 2000 labeled images. These annotated data were subsequently employed to train an enhanced CNN model based on ResNet50 architecture, which achieved automated generation of panicle density maps and precise panicle counting, thereby realizing yield prediction. Field experimental results demonstrated significant yield variations among fertilization treatments under sufficient irrigation, with the 3:7 organic–inorganic blend achieving the highest actual yield (9363.38 ± 468.17 kg ha⁻¹) significantly outperforming other treatments (p < 0.05), confirming the synergistic effects of optimized nitrogen and water management. The enhanced CNN model exhibited superior performance, with an average accuracy of 89.0–92.1%, representing a 3.0% improvement over YOLOv8. Notably, model accuracy showed significant correlation with yield levels (p < 0.05), suggesting more distinct panicle morphological features in high-yield plots that facilitated model identification. The CNN’s yield predictions demonstrated strong agreement with the measured values, maintaining mean relative errors below 10%. Particularly outstanding performance was observed for the organic fertilizer with full irrigation (5.5% error) and the 7:3 organic-inorganic blend with sufficient irrigation (8.0% error), indicating that the CNN network is more suitable for these management regimes. These findings provide a robust technical foundation for precision farming applications in winter wheat production. Future research will focus on integrating this technology into smart agricultural management systems to enable real-time, data-driven decision making at the farm scale. Full article

Livestock farming is a vital component of global food security, yet it remains a major contributor to greenhouse gas (GHG) emissions, particularly methane (CH₄), which has a global warming potential 28 times greater than carbon dioxide (CO₂). This review provides a comprehensive synthesis of current knowledge surrounding the sources, biological mechanisms, and mitigation strategies related to CH₄ emissions from ruminant livestock. We first explore the process of methanogenesis within the rumen, detailing the role of methanogenic archaea and the environmental factors influencing CH₄ production. A thorough assessment of both direct and indirect methods used to quantify CH₄ emissions is presented, including in vitro techniques (e.g., syringe method, batch culture, RUSITEC), in vivo techniques (e.g., respiration chambers, Greenfeed, laser CH₄ detectors), and statistical modeling approaches. The advantages and limitations of each method are critically analyzed in terms of accuracy, cost, feasibility, and applicability to different farming systems. We then examine a wide range of mitigation strategies, organized into four core pillars: (1) animal and feed management (e.g., genetic selection, pasture quality improvement), (2) diet formulation (e.g., feed additives such as oils, tannins, saponins, and seaweed), (3) rumen manipulation (e.g., probiotics, ionophores, defaunation, vaccination), and (4) manure management practices and policy-level interventions. These strategies are evaluated not only for their environmental impact but also for their economic and practical viability in diverse livestock systems. By integrating technological innovations with sustainable agricultural practices, this review highlights pathways to reduce CH₄ emissions while maintaining animal productivity. It aims to support decision-makers, researchers, and livestock producers in the global effort to transition toward climate-smart, low-emission livestock farming. Full article

The European Union’s developing rural development plan places digital and precision agriculture at the centre of agricultural modernisation and economic development. This article examines how agricultural practices in rural EU regions are being influenced by smart technology, such as drones, IoT sensors, satellite-based research, and AI-driven platforms, through an analysis of recent data from sources across the European Union. This study applies a mixed-methods approach, combining quantitative analysis of strategic policy documents and EU databases, to evaluate the ways in which precision agriculture reduces input consumption, increases productivity, reduces labour shortages and rural area depopulation, and improves sustainability. By investing in infrastructure, developing communities for data exchange, and organising training for farmers, European policies such as the Strategic Plans of the Common Agricultural Policy (CAP), the SmartAgriHubs initiative, and the AgData program actively encourage the transition to digital agriculture. Cyprus is analysed as a case study to show how targeted investments and initiatives supported by the EU can help smaller countries, with limited natural resources, to realise the benefits of digital transformation in agriculture. A special focus is placed on how solutions adapted to agro-climatic and socioeconomic conditions can contribute to strengthening the competitiveness of the agricultural sector, attracting young people to get involved in this field and opening up new economic opportunities. The results of previous research indicate that digital agriculture not only improves productivity but also proves to be a strategic mechanism for attracting and retaining young people in rural areas. Thus, this work additionally contributes to the broader goal of the European Union—the development of smart, inclusive, and sustainable rural areas, in which digital technologies are not only seen as tools for efficiency but also as key means for integrated and long-term rural development. Full article

Smart farming is an agricultural technology integrating advanced technology such as cloud computing, Artificial Intelligence (AI), the Internet of Things (IoT), and robots into traditional farming. Smart farming can help farmers by increasing agricultural production and managing resources efficiently. However, malicious attackers can attempt security attacks because communication in smart farming is conducted via public channels. Therefore, an authentication scheme is necessary to ensure security in smart farming. In 2024, Rahaman et al. proposed a privacy-centric authentication scheme for smart farm monitoring. However, we demonstrated that their scheme is vulnerable to stolen mobile device, impersonation, and ephemeral secret leakage attacks. This paper suggests a secure and privacy-preserving scheme to resolve the security defects of the scheme proposed by Rahaman et al. We also verified the security of our scheme through “the Burrows-Abadi-Needham (BAN) logic”, “Real-or-Random (RoR) model”, and “Automated Validation of Internet Security Protocols and Application (AVISPA) tool”. Furthermore, a performance analysis of the proposed scheme compared with related studies was conducted. The comparison result proves that our scheme was more efficient and secure than related studies in the smart farming environment. Full article

The integration of computer vision (CV) and generative artificial intelligence (GenAI) into smart agriculture has revolutionised traditional farming practices by enabling real-time monitoring, automation, and data-driven decision-making. This review systematically examines the applications of CV in key agricultural domains, such as crop health monitoring, precision farming, harvesting automation, and livestock management, while highlighting the transformative role of GenAI in addressing data scarcity and enhancing model robustness. Advanced techniques, including convolutional neural networks (CNNs), YOLO variants, and transformer-based architectures, are analysed for their effectiveness in tasks like pest detection, fruit maturity classification, and field management. The survey reveals that generative models, such as generative adversarial networks (GANs) and diffusion models, significantly improve dataset diversity and model generalisation, particularly in low-resource scenarios. However, challenges persist, including environmental variability, edge deployment limitations, and the need for interpretable systems. Emerging trends, such as vision–language models and federated learning, offer promising avenues for future research. The study concludes that the synergy of CV and GenAI holds immense potential for advancing smart agriculture, though scalable, adaptive, and trustworthy solutions remain critical for widespread adoption. This comprehensive analysis provides valuable insights for researchers and practitioners aiming to harness AI-driven innovations in agricultural ecosystems. Full article

The change in the socio-political formation of Russia from a socialist planned system to a capitalist market system significantly influenced agriculture and one of its components—arable land. The loss of the sustainability of land management for arable land led to a reduction in sown areas by 38% (from 119.7 to 74.7 million ha) and a synchronous drop in gross harvests of grain and leguminous crops by 48% (from 117 to 61 million tons). The situation stabilized in 2020, with a sowing area of 80.2 million ha and gross harvests of grain and leguminous crops of 120–150 million tons. This process was not formalized legally, and the official (legal) area of arable land decreased by only 8% from 132.8 to 122.3 million ha. Legal conflict arose for 35 million ha for unused arable land, for which there was no classification of its condition categories and no monitoring of the withdrawal time of the arable land from actual agricultural use. The aim of this study was to resolve the challenges in the method of retrospective monitoring of soil–land cover, which allowed for the achievement of the aims of the investigation—to elucidate the history of land use on arable lands from 1985 to 2025 with a time step of 5 years and to obtain a detailed classification of the arable lands’ abandonment degrees. It was also established that on most of the abandoned arable land, carbon sequestration occurs in the form of secondary forests. In the course of this work, it was shown that the reasons for the formation of an array of abandoned arable land and the stabilization of agricultural production turned out to be interrelated. The abandonment of arable land occurred proportionally to changes in the soil’s natural fertility and the degree of land degradation. Economically unprofitable lands spontaneously (without centralized planning) left the sowing zone. The efficiency of land use on the remaining lands has increased and has allowed for the mass application of modern farming systems (smart, precise, landscape-adaptive, differentiated, no-till, strip-till, etc.), which has further increased the profitability of crop production. The prospect of using abandoned lands as a carbon sequestration zone in areas of forest overgrowth has arisen. Full article