Search Results (261)

The expansion of conventional agricultural models in the Colombian Amazon has caused deforestation, biodiversity loss, and socio-environmental degradation. In response, agroecology and bioeconomy are emerging as key strategies to regenerate landscapes and foster sustainable production systems. We evaluated the agroecological performance of 25 farms in the Andean–Amazon transition zone of Colombia using FAO’s Tool for Agroecology Performance Evaluation (TAPE). The analysis included land cover dynamics (2002–2024), characterization of the agroecological transition based on the 10 Elements of Agroecology, and 23 economic, environmental, and social indicators. Four farm typologies were identified; among them, Mixed Family Farms (MFF) achieved the highest transition score (CAET = 60.5%) and excelled in crop diversity (64%), soil health (SHI = 4.24), productive autonomy (VA/GVP = 0.69), and household empowerment (FMEF= 85%). Correlation analyses showed strong links between agroecological practices, economic efficiency, and social cohesion. Land cover dynamics revealed a continuous decline in forest cover (12.9% in 2002 to 7.1% in 2024) and an increase in secondary vegetation, underscoring the urgent need for restorative approaches. Overall, farms further along the agroecological transition were more productive, autonomous, and socially cohesive, strengthening territorial resilience. The application of TAPE proved robust multidimensional evidence to support agroecological monitoring and decision-making, with direct implications for land use planning, rural development strategies, and sustainability policies in the Amazon. At the same time, its sensitivity to high baseline biodiversity and to the complex socio-ecological dynamics of the Colombian Amazon underscores the need to refine the methodology in future applications. By addressing these challenges, the study contributes to the broader international debate on agroecological transitions, offering insights relevant for other tropical frontiers and biodiversity-rich regions facing similar pressures. Full article

In low-risk and open environments, such as farms and mining sites, efficient cargo transportation is essential. Despite the suitability of autonomous driving for these environments, its high deployment and maintenance costs limit large-scale adoption. To address this issue, a modular unmanned ground vehicle (UGV) system is proposed, which is adapted from existing platforms and supports both autonomous and manual control modes. The autonomous mode uses environmental perception and trajectory planning algorithms for efficient transport in structured scenarios, while the manual mode allows human oversight and flexible task management. To mitigate the control latency and execution delays caused by platform modifications, an enhanced transformer-based general dynamics model is introduced. Specifically, the model is trained on a custom-built dataset and optimized within a bicycle kinematic framework to improve control accuracy and system stability. In road tests allowing a positional error of up to 0.5 m, the transformer-based trajectory estimation method achieved 94.8% accuracy, significantly outperforming non-transformer baselines (54.6%). Notably, the test vehicle successfully passed all functional validations in autonomous driving trials, demonstrating the system’s reliability and robustness. The above results demonstrate the system’s stability and cost-effectiveness, providing a potential solution for scalable deployment of autonomous transport in low-risk environments. Full article

This article presents the development of a new pneumatic device for the precise application of mineral fertilizers, designed for use in precision agriculture systems involving farming robots. The proposed device is mounted on an autonomous agricultural platform and utilizes a machine vision system to determine plant coordinates. Its operating principle is based on accumulating a single dose of fertilizer in a chamber and delivering it precisely to the plant’s root zone using a directed airflow. The study includes a theoretical investigation of fertilizer movement inside the applicator tube under the influence of airflow and rotational motion of the tube. A mathematical model has been developed to describe both the relative and translational motion of the fertilizer. The equations, which account for frictional forces, inertia, and air pressure, enable the determination of optimal structural and kinematic parameters of the device depending on operating conditions and the properties of the applied material. The use of numerical methods to solve the developed mathematical model allows for synchronization of the device’s operating time parameters with the movement of the agricultural robot along the crop rows. The obtained results and the developed device improve the accuracy and speed of fertilizer application, minimize fertilizer consumption, and reduce soil impact, making the proposed device a promising solution for precision agriculture. Full article

Resource efficiency is essential in today’s approach to horticulture. The global problems of water scarcity, soil pollution, biodiversity loss, and rapid growth of the global population require increased food production with fewer resources. Resource efficiency is an indicator that allows defining how much biomass an agri-food system can produce per unit of the resource used. Closed hydroponic systems, such as vertical hydroponic towers (VHTs), exhibit high resource efficiency. In these systems, the water use efficiency (WUE) and the nutrient use efficiency (NUE) can be calculated in terms of the water loss through transpiration and the ion concentration in the nutrient solution. The research aimed to determine the WUE and NUE for chard crops in VHT under greenhouse conditions and to evaluate its feasibility as an urban and peri-urban system for leafy vegetable production. Trials were carried out with chard in the fall 2024 in a tunnel-type greenhouse at the facilities of the Autonomous University of San Luis Potosi. The VHTs were built with a 20 L square lower deposit on which a cylindrical pipeline of 11.5 cm in diameter and 1.6 m in height was vertically placed. Each pipe had 45 growing containers distributed on 15 levels of three containers spaced vertically 9 cm and a density of 25 plants·m⁻². The experimental design was completely randomized with three treatments (75, 100, and 125% of Steiner’s nutrient solution) and three replications. The transpiration (Tr) of the crop (recording weight loss in the deposit) and the shoot fresh weight (SFW) of the plants were measured daily using a scale. An ANOVA and Tukey’s test for mean differentiation were performed with p < 0.05. Significant differences were found between treatments for SFW, WUE and NUE obtaining the best results at 75% of Steiner’s nutrient solution. Results show that WUE increased between 3 and 6 times, and NUE between 3 and 12 times compared to chard grown in soil. These results were equal and even higher than horizontal hydroponic systems or vertical farms. Vertical hydroponic closed towers installed in greenhouses are an optimal horticultural production system with high resources use efficiency. The implementation of VHT is feasible in areas where there is water scarcity or have a high population density. Full article

Increasing global food production to address challenges from population growth, labor shortages, and climate change necessitates a significant enhancement of agricultural sustainability. Autonomous agricultural machinery, a recognized application of precision agriculture, offers a promising solution to boost productivity, resource efficiency, and environmental sustainability. This study presents a systematic review of autonomous driving technologies for agricultural machinery based on 506 rigorously selected publications. The review emphasizes three core aspects: navigation reliability assurance, motion control mechanisms for both vehicles and implements, and actuator fault-tolerance strategies in complex agricultural environments. Applications in farmland, orchards, and livestock farming demonstrate substantial potential. This study also discusses current challenges and future development trends. It aims to provide a reference and technical guidance for the engineering implementation of intelligent agricultural machinery and to support sustainable agricultural transformation. Full article

Six years after its deployment, SatShipAI, an operational platform combining AI models with Sentinel-1 SAR imagery and AIS data, has provided robust maritime surveillance around Denmark. A meta-analysis of archived outputs, logs, and manual reviews shows stable vessel detection and classification performance over time, including successful cross-sensor application to X-band SAR data without retraining. Key operational challenges included orbit file delays, nearshore detection limits, and emerging infrastructure such as wind farms. The platform proved particularly valuable for detecting offshore “dark” vessels beyond AIS coverage, informing maritime security, traffic management, and emergency response. These findings demonstrate the feasibility, resilience, and adaptability of long-term AI–geospatial systems, offering practical guidance for future autonomous monitoring infrastructure. Full article

Climate change and labour shortage are re-shaping farming methods. Agricultural tasks are often hard, tedious and repetitive for operators, and farms struggle to find specialized operators for such works. For this and other reasons (i.e., the increasing costs of agricultural labour) more and more farmers have decided to switch to autonomous (or semi-autonomous) field robots. In the past decade, an increasing number of robots has filled the market of agricultural machines all over the world. These machines can easily cover long and repetitive tasks, while operators can be employed in other jobs inside the farms. This paper reviews the current state-of-the-art of autonomous robots for agricultural operations, dividing them into categories based on main tasks, to analyze their main characteristics and their fields of applications. Seven main tasks were identified: multi-purpose, harvesting, mechanical weeding, pest control and chemical weeding, scouting and monitoring, transplanting and tilling-sowing. Field robots were divided into these categories, and different characteristics were analyzed, such as engine type, traction system, application field, safety sensors, navigation system, country of provenience and presence on the market. The aim of this review is to provide a global view on agricultural platforms developed in the past decade, analyzing their characteristics and providing future perspectives for next robotic platforms. The analysis conducted on 59 field robots, those already available on the market and not, revealed that one fifth of the platforms comes from Asia, and 63% of all of them are powered by electricity (rechargeable batteries, not solar powered) and that numerous platforms base their navigation system on RTK-GPS signal, 28 out of 59, and safety on LiDAR sensor (12 out of 59). This review considered machines of different size, highlighting different possible choices for field operations and tasks. It is difficult to predict market trends as several possibilities exist, like fleets of small robots or bigger size platforms. Future research and policies should focus on improving navigation and safety systems, reducing emissions and improving level of autonomy of robotic platforms. Full article

Black fungus cultivation in bagged form requires frequent inspection of mycelial growth, a process that is labor-intensive and susceptible to subjective judgment. In addition, timely detection of contamination in low-light and high-humidity environments remains a significant challenge. To address these issues, this paper proposed an intelligent agriculture system for black fungus cultivation, with emphasis on practical deployment under real farming conditions. The system integrates a private 5G network with a YOLOv8-based deep learning model for real-time object detection and growth monitoring. Continuous image acquisition and data feedback are achieved through a multi-parameter environmental sensing module and an autonomous ground vehicle (AGV) equipped with IP cameras. To improve model robustness, more than 42,000 labeled training images were generated through data augmentation, and a modified C2f network architecture was employed. Experimental results show that the model achieved a detection accuracy of 93.7% with an average confidence score of 0.96 under live testing conditions. The deployed 5G network provided a downlink throughput of 645.2 Mbps and an uplink throughput of 147.5 Mbps, ensuring sufficient bandwidth and low latency for real-time inference and transmission. Field trials conducted over five cultivation batches demonstrated improvements in disease detection, reductions in labor requirements, and an increase in the average yield success rate to 80%. These findings indicate that the proposed method offers a scalable and practical solution for precision agriculture, integrating next-generation communication technologies with deep learning to enhance cultivation management. Full article

With the widespread adoption of intelligent technologies in animal husbandry, traditional manual feeding methods can no longer meet the demands for precision and efficiency in modern sheep farming. To address this gap, we present an intelligent robotic feeding system designed to enhance feeding efficiency, reduce labor intensity, and enable precise delivery of feed. This system, developed on the ROS platform, integrates LiDAR-based SLAM with point cloud rendering and an Octomap 3D grid map. It combines an improved bidirectional RRT* algorithm with Dynamic Window Approach (DWA) for efficient path planning and uses 3D LiDAR data along with the RANSAC algorithm for slope detection and navigation information extraction. The YOLOv8s model is utilized for precise sheep pen marker identification, while integration with weighing sensors and a farm management system ensures accurate feed distribution control. The main research contribution lies in the development of a comprehensive, multi-sensor fusion system capable of achieving autonomous feeding in dynamic and complex environments. Experimental results show that the system achieves centimeter-level accuracy in localization and attitude control, with FAST-LIO2 maintaining precision within 1° of attitude angle errors. Compared to baseline performance, the system reduces node count by 17.67%, shortens path length by 0.58 cm, and cuts computation time by 42.97%. At a speed of 0.8 m/s, the robot achieves a maximum longitudinal deviation of 7.5 cm and a maximum heading error of 5.6°, while straight-line deviation remains within ±2.2 cm. In a 30 kg feeding task, the system demonstrates zero feed wastage, highlighting its potential for intelligent feeding in modern sheep farming. Full article

The rational design of artificial lighting systems in pig housing can enhance animal welfare, thereby boosting gilt health and reproductive performance while improving economic metrics for swine farms. To identify the optimal light environments for gilts under artificial illumination, we conducted self-selection-based photic preference testing, ultimately providing actionable insights for welfare-centric precision lighting protocols in modern pig production. In this study, a dynamic multi-chromatic self-selection system was developed, integrating programmable RGBW-LED arrays for spectral control, inter-compartment access channels for autonomous gilt movement, and real-time image recognition technology to investigate light color preferences. Twenty-four gilts (nulliparous female pigs) were housed for five weeks in pens with white, yellow, green, blue, or red light (100 lux), and they were given free access to all of the chromatic zones through inter-compartment channels. A YOLOv8n-based deep learning framework was used to quantify their spatiotemporal distribution, activity levels, and eating behavior. The key findings were the following: (1) a significant preference for green light environments (21.29 ± 3.77% distribution proportion) (p < 0.05), peaking at 6:00–13:00 and 18:00–20:00; (2) the average activity was the highest in a white light environment (25.49 ± 0.77%), significantly exceeding yellow (22.69 ± 0.63%) and green light (21.55 ± 0.61%) (p < 0.05); and (3) the daily feed consumption under green light was the lowest, significantly lower than that under white, blue, and red light (p < 0.05). The findings from this study offer insights into the light environment preferences of gilts, which could improve animal welfare. Full article

This manuscript investigates the key factors driving the diversification of agricultural production towards tourism and analyzes the impact of economic business aspects and farmers’ identity on this process. The study involved 420 farm owners from the Autonomous Province of Vojvodina. Factor analysis identified four main factors: motivation, resources, market conditions, and accessibility. Results show that the average monthly income of farmers is €1350, and they recognize diversification potential as a tool to improve the economic performance of their farms. However, the prevailing traditional farmer identity limits this process. This study provides insights into farmers’ attitudes towards sustainable agricultural diversification into tourism within the context of a developing country. Full article

As wind turbines are affected by the harsh marine environment, inspection is crucial for the continuous operation of offshore wind farms. Nowadays, the main method of inspection is manual inspection, which has significant limitations in terms of safety, economy, and labor. With the advancement of technology, unmanned inspection systems have attracted more attention from researchers and the industry. This study proposes a novel framework to enable Unmanned Aerial Vehicles (UAVs) to improve their adaptability in autonomous inspection tasks on offshore wind farms, which includes multi-UAVs, inspection task nodes, and multiple charging stations. The main contributions of this paper are as follows: we propose an improved PSO algorithm to improve the location of charging stations; based on the multi-depot traveling salesman problem, we establish a multi-station UAV cooperative task allocation model with energy constraints, with the inspection time consumption of UAVs as the optimization objective; we also propose the Dynamic elite Double population Genetic Algorithm (DDGA) to aid in the cooperative task allocation of UAVs. The simulation results show that, compared with other algorithms, the proposed framework has higher universality and superiority. This paper provides a specific method for the application of unmanned inspection systems in the inspection of wind turbines in offshore wind farms. Full article

The implementation of autonomous rovers in agriculture could be a promising solution to ensure, at the same time, productivity and sustainability. One of the key points of this kind of vehicle concerns their autonomous driving strategy. Generally, the strategy should include the path planning and path following algorithms. In this paper, an autonomous driving strategy assessing both is presented. To evaluate the effectiveness of this strategy, a case study of an agricultural rover is presented. A co-simulation model, including a multibody model of the rover, is developed in Matlab/Simulink R2021b and Hexagon Adams 2024 environments to virtually test the rover capabilities and the effects of its dynamics on the robustness of the algorithm. Given different orchard configurations, common but critical work scenarios are investigated, namely a 180° turn and an obstacle avoidance manoeuvre. The actual trajectory obtained during simulations are compared to the ideal trajectory defined in the path planning stage. Furthermore, the torque demand at the electric motors is evaluated. To consider a wide range of possible operating conditions, additional tests with different terrains, payloads and road slopes are included. Results showed that the rover managed to accomplish the considered manoeuvres on loam soil with a maximum trajectory deviation of 0.58 m, but a temporary overload of the motors is needed. On the contrary, in case of difficult terrains, such as muddy soil, the rover was not able to perform the manoeuvre. To limit tire slip, a traction control algorithm is developed and implemented, and the results are compared with the case without control. Full article

We document and analyze an emblematic case study of non-indigenous peasant autonomy and re-peasantization in Sa Kaeo province in the Issan region of Thailand, using a mostly qualitative, single case-study methodology. The Kok Edoi autonomous community, whose members engage in community forest management and increasingly in agroecological farming, was founded more than twenty-five years ago as the product of a land occupation by landless peasants associated with the national Thai social movement, the Assembly of the Poor (AoP), which is part of the global peasant movement, La Via Campesina (LVC). Partially inspired by opportunities given to the community and to AoP by LVC to learn and gain inspiration from Latin American experiences such as the Zapatistas in Mexico, Kok Edoi autonomy exemplifies how the exchange of social movement knowledge and experience can help shape and strengthen local struggles, and it is also suggestive of autonomy as an alternative pathway of resistance and sustainable development in Thailand. We review the literature on territorial autonomy, re-peasantization, and community forestry and autonomy in Thailand and the world. Situating Kok Edoi in Thai history concerning policies and conflicts around land and forests, we examine the type, dimensions, and facets of autonomy and re-peasantization present in Kok Edoi to demonstrate how these factors contribute to the community being considered an emblematic case of peasant autonomy, peasant land occupation, peasant management of and livelihood derived from natural resources, more autonomous alternative markets, collective accumulation, and political training and mobilization that contributes to a class-based national movement. This is novel in an academic literature that has to date focused principally on indigenous autonomy, largely in Latin America. Full article

Background: Reliable and non-invasive methods for assessing welfare in poultry are essential for improving evidence-based welfare monitoring and advancing management practices in commercial production systems. The iris-to-pupil (IP) ratio, previously validated by our group in primates and cattle, reflects autonomic nervous system balance and may serve as a physiological indicator of stress in laying hens. This study evaluated the utility of the IP ratio under field conditions across diverse commercial layer housing systems. Materials and Methods: In total, 296 laying hens (Lohmann Brown, n = 269; White Leghorn, n = 27) were studied across four locations in Canada housed under different systems: Guelph (indoor; pen), Spring Island (outdoor and scratch; organic), Ottawa (outdoor, indoor and scratch; free-range), and Toronto (outdoor and hobby; free-range). High-resolution photographs of the eye were taken under ambient lighting. Light intensity was measured using the light meter app. The IP ratio was calculated using NIH ImageJ software (Version 1.54p). Statistical analysis included one-way ANOVA and linear regression using GraphPad Prism (Version 5). Results: Birds housed outdoors had the highest IP ratios, followed by those in scratch systems, while indoor and pen-housed birds had the lowest IP ratios (p < 0.001). Subgroup analyses of birds in Ottawa and Spring Island farms confirmed significantly higher IP ratios in outdoor environments compared to indoor and scratch systems (p < 0.001). The IP ratio correlated weakly with ambient light intensity (r² = 0.25) and age (r² = 0.05), indicating minimal influence of these variables. Although White Leghorn hens showed lower IP ratios than Lohmann Browns, this difference was confounded by housing type; all White Leghorns were housed in pens. Thus, housing system but not breed was the primary driver of IP variation. Conclusions: The IP ratio is a robust, non-invasive physiological marker of welfare assessment in laying hens, sensitive to housing environment but minimally influenced by light or age. Its potential for integration with digital imaging technologies supports its use in scalable welfare assessment protocols. Full article