Search Results (2,075)

Derelict crab pots (“ghost pots”) cause bycatch mortality, habitat degradation, and lost harvest in shallow coastal ecosystems. Existing detection and recovery programs rely on expert operators and high-cost sonar, limiting coverage and reproducibility. Here, we present GhostVision, an open-source framework that integrates low-cost consumer side-scan sonar with modern object-detection models to enable scalable, rapid post-processing and mapping of derelict gear. Mobile Mapping Units (MMUs) equipped with off-the-shelf fishfinders surveyed more than 1500 acres in Delaware’s Inland Bays between 2020 and 2022. Three architectures (YOLOv12, YOLOv26, RF-DETR) were trained on 3110 manually annotated sonar images and evaluated with both dataset-centric metrics and full pipeline implementation. YOLOv12 showed the strongest untuned operational performance (F1 = 0.512; recall = 0.922), while post-processing optimization produced comparable performance across all three models (F1 ≈ 0.71–0.73). Across 11 complete test recordings, end-to-end processing required only 8.87–9.79% of survey time (approximately 10–11× faster than real-time), supporting same-day analysis and recovery workflows. GhostVision can foster community engagement in derelict crab-pot removal by pairing low-cost sonar with AI to aid recovery efforts at management-relevant scales. By lowering financial and technical barriers, GhostVision provides a reproducible pathway for large-scale stewardship and supports future extensions to multi-class detection and autonomous platforms. Full article

The field inspection and ripeness detection of tomatoes in China remain heavily dependent on manual labor, while existing robotic solutions often exhibit limited functionality, poor environmental adaptability, prohibitive hardware costs, and unstable positioning accuracy. To address these limitations, this study proposes an intelligent tomato inspection robot that seamlessly integrates real-time ripeness recognition with precise spatial localization. Built upon a Raspberry Pi 5 core controller, the robot employs a lightweight, layered modular architecture designed to flexibly navigate complex agricultural environments. A comprehensive, multi-dimensional image dataset of tomato ripeness was constructed to train a three-category detection model based on the YOLOv8n architecture. Following 413 training epochs, the model demonstrated exceptional performance, achieving an overall mAP@0.5 of 87.8% and an mAP@0.5:0.95 of 72.7% on the held-out test dataset. In field inspections, the system achieved detection precisions of 82.22% for immature tomatoes, 92.66% for half-ripened tomatoes, and 100% for fully ripe tomatoes, successfully identifying all ripe tomatoes and satisfying the practical demands of field inspection. Furthermore, the integration of an Ultra-Wideband positioning system yielded an overall Root Mean Square Error of 0.231 m, successfully confining positioning errors to within 0.24 m to fully satisfy the stringent localization demands of crop-level inspection. Field evaluations confirmed that under optimal configurations, the robot can efficiently inspect a 50-m planting row in 10 min (±1 min) and maintains a continuous operational battery life of 2 h (±10 min). The core contribution of this work is the system-level integration and optimization of technologies for greenhouse agriculture. This integrated design achieves low hardware cost and high deployment flexibility, addressing longstanding challenges of labor-intensive inspection and delayed harvesting, and delivering a practical solution for intelligent tomato plantation management. Full article

Ablation and harvesting are among the most labor-intensive and physically demanding operations in oil palm cultivation, often resulting in significant drudgery and safety concerns when performed manually through climbing or pole-assisted methods. To overcome these challenges, a motorized backpack-type machine was developed and evaluated for its field performance, ergonomics, and economic feasibility. The machine met required quality standards and exhibited satisfactory performance under field conditions, achieving average ablation and harvesting capacities of 286 inflorescences per day and 4.115 t day⁻¹, with actual field capacities of 0.727 ha h⁻¹ (ablation), 0.516 ha h⁻¹ (sickle), and 0.537 ha h⁻¹ (chisel), and field efficiencies of 81.23%, 76.3%, and 79.91%, respectively. Ergonomic evaluation indicated that operation of the machine falls within a moderate workload category, thereby reducing operator fatigue compared to manual methods. Economic analysis further revealed that the cost of operation was substantially reduced to 3.02 USD t⁻¹ and 60.40 USD ha⁻¹ year⁻¹, resulting in increased harvester earnings of 174.72% and 64.83% compared to climbing and pole harvesting methods, respectively. These findings demonstrate that the motorized backpack machine is a practical, efficient, and economically viable alternative to traditional techniques and minimizes drudgery while improving productivity and profitability in oil palm plantations. Full article

To improve grape target perception and picking-point positioning for intelligent harvesting robots, this study develops a vision-based method for orchard grape detection and harvesting-point localization. The method is intended to address missed detections, insufficient recognition accuracy, and unsatisfactory peduncle segmentation caused by illumination variation, occlusion, and interference from branches and leaves in complex orchard scenes. For grape cluster and peduncle detection, a lightweight YOLOv7-derived model, termed YOLO-FES, was established. In this model, FasterNet and SCConv were introduced to refine the backbone and neck structures, and the EMA mechanism was incorporated to lower parameter complexity and computational cost while improving detection performance. For suspended grape structure association and peduncle extraction, the GJK algorithm was combined with nearest-neighbor rectangular discrimination, and an improved YOLACT-based peduncle segmentation network, named M-YOLACT, was constructed. With the integration of the MLCA mechanism and the Mish activation function, accurate peduncle segmentation was achieved. In addition, a stereo depth camera was employed to obtain two-dimensional picking-point information and further recover the corresponding three-dimensional spatial coordinates. Experimental results showed that the mAP@0.5 of YOLO-FES for grape clusters and peduncles reached 95.37%. For grape peduncle segmentation, the mAP@0.5 values of the bounding boxes and masks produced by M-YOLACT reached 95.73% and 94.36%, respectively. The proposed method achieved an overall harvesting success rate of 89.2%, with an average time consumption of 11 s for a single harvesting operation. By integrating deep-learning-based detection and segmentation with binocular-vision localization, this study provides a practical technical solution and useful reference for the visual system design of grape-harvesting robots. Full article

Introduction: Simulation-based training is a key component of surgical education; however, existing models, such as dry-laboratory and virtual reality simulators, have limitations in terms of realism and accessibility. The use of human cadaveric material is also challenging because of its high cost and limited availability. Objectives: To evaluate the effectiveness of biological models based on large animal cadaveric material, specifically cattle and pigs, for practicing head and neck surgical skills. Materials and Methods: The study included 100 third- and fourth-year students, who were divided into a study group and a comparison group, with 50 participants in each group. The study group practiced surgical skills using animal cadaveric material: a porcine mandible for bone graft harvesting and a bovine head for resection craniotomy. The comparison group practiced using 3D-printed models. The results were assessed using an anonymous 8-item Likert-scale questionnaire, followed by statistical analysis using the Mann–Whitney U test. Results: In the study group, statistically significant increases were observed in satisfaction with participation, fulfillment of expectations, perceived subjective acquisition of manual skills, and overall satisfaction with the training process (p < 0.001; median scores: 38.0 and 34.0, respectively). The greatest differences were observed in satisfaction with participation, where 54% of participants rated it as “Excellent” compared with 6% in the comparison group, and in perceived subjective acquisition of manual skills, reported by 80% of participants in the study group compared with 24% in the comparison group. Conclusions. The use of cadaveric specimens from large animals is associated with higher satisfaction and represents an accessible alternative for practicing basic and commonly performed head and neck surgical procedures that do not require fine dissection of neurovascular bundles. This model provides a high degree of tactile realism and anatomical context and is subjectively preferred over non-anthropomorphic simulators. Full article

With increasing water scarcity and growing food demand, enhancing agricultural productivity has become a pressing necessity, aligning with the Sustainable Development Goals (SDGs). Maize is a strategic crop, yet under surface irrigation, modern technologies are required to optimize irrigation efficiency and reduce water losses. Two field trials were conducted during the summer seasons of 2024 and 2025 on a private farm in Banha, Qalyubia Governorate, Egypt, using a three-replication split-block design. This study evaluated three land slopes (0, 0.05, and 0.15%) and two furrow lengths (50 and 75 m) under furrow irrigation in clay loam soil, using the maize hybrid “Single Cross 2036.” The results demonstrated that both furrow length and land slope significantly affected all measured parameters. Shorter furrows (50 m) consistently outperformed longer ones (75 m), achieving better growth parameters, higher grain yield, improved harvest index, and enhanced irrigation water productivity. Regarding land slope, the 0.15% slope produced the best results, although it was not significantly different from the 0.05% slope in most cases. The interaction between furrow length and land slope was significant; the combination of 50 m furrows with 0.15% slope produced the highest values across all parameters. For longer furrows (75 m), the gentler 0.05% slope was more effective than the steeper 0.15% slope. Notably, 50 m furrows, even with 0% slope, performed better than 75 m furrows with the optimal 0.05% slope, indicating that furrow length is more critical than slope for maximizing maize productivity in clay loam soils. Economic analysis confirmed these findings, with the combination of 50 m furrows and 0.15% slope achieving the highest net return (29,565 EGP ha⁻¹) and revenue-to-cost ratio (1.38), representing a substantial increase in net profit compared to traditional practices. Therefore, a 0.15% slope is recommended for shorter furrows (50 m), while a gentler 0.05% slope is more suitable for longer furrows (75 m). These findings provide a practical pathway for policymakers and farmers to enhance resource efficiency and contribute to SDG 2 (Zero Hunger) and SDG 6 (Clean Water and Sanitation). Full article

An adaptive neuro-fuzzy inference system (ANFIS)-based maximum power point tracking (MPPT) controller, trained with real-world data, is proposed and experimentally validated for photovoltaic systems operating under highly variable climatic conditions. Unlike conventional approaches relying on synthetic data, this work integrates a double-diode model (DDM) with high-fidelity measurements from a class-A pyranometer to capture the stochastic nature of the tropical environment. The controller was implemented on a Raspberry Pi 5 platform, governing a 100 kHz DC–DC converter connected to a 340 W photovoltaic array in Cúcuta, Colombia. Experimental results demonstrate a near instantaneous tracking time of 100 ms, significantly outperforming the 240 ms achieved by the conventional Perturb and Observe (P&O) method. Under rapid irradiance fluctuations, the system reached a peak static efficiency of 96.4% and a dynamic efficiency of 94.5%. Furthermore, a 10-h comparative energy analysis revealed that the ANFIS engine harvested 926.6 Wh, representing an 8.21% increase in cumulative energy yield compared to the P&O algorithm. This study confirms that intelligent MPPT controllers trained with real physical data can operate robustly on low-cost, non-deterministic embedded platforms, providing a resilient solution for energy optimization in modern PV microgrids. Full article

This study proposes a health and performance optimization framework based on Particle Swarm Optimization (PSO) to improve the structural performance and energy utilization efficiency of coastal wind–wave energy harvesting systems. A semi-submersible floating wind turbine–wave energy integrated system is selected as the case study. The control variable method and numerical simulations are employed to determine the optimal structural parameters. Furthermore, a multi-scenario coordinated optimization model for wind, wave, energy storage, and load is established using the Velocity Pause Particle Swarm Optimization (VPPSO) algorithm. The optimal structural parameters are identified as an outer diameter of 16 m, an inner diameter of 8 m, a height of 8 m, and a draft of 3.5 m. The results show that VPPSO achieves faster convergence and better optimization performance compared to conventional algorithms. In the optimal scenario with wind–wave curtailment and energy storage participation, the minimum economic cost of 1780 CNY is achieved after 200 iterations. The proposed method provides theoretical guidance for the optimal design and efficient operation of coastal wind–wave energy harvesting systems. Full article

Indigenous communities in northern Canada experience severe household food insecurity rates ranging from 21.8% to 70%. However, the relationship between national-level economic and environmental indicators and Indigenous Community Well-being (ICWB) remains inadequately understood. This study examines national-level correlates of ICWB from 1991 to 2021, analyzing relationships between ICWB scores and agricultural production volumes (canola, corn, wheat, soybeans), their commodity prices, and greenhouse gas (GHG) emissions, with a particular focus on the role of traditional food systems. The study uses data from the Government of Canada, Statistics Canada, and Environment Canada, supplemented by secondary literature on Indigenous traditional food systems. Three documented mechanisms provide a framework for interpreting how national indicators may affect northern communities: commodity price transmission through integrated markets, federal policy responses calibrated to national economic data, and supply chain dependencies linking southern production to northern availability. Correlation analysis reveals significant positive associations between ICWB and production volumes of canola, corn, and soybeans, as well as the prices of wheat, corn, canola, and soybeans. Regression analysis that accounts for temporal trends reveals that soybean and canola prices are negatively associated with ICWB, indicating that increasing prices may reduce community well-being, potentially reflecting increased economic pressure or reduced affordability. GHG emissions correlate positively with ICWB, likely reflecting confounding by economic development rather than direct environmental benefits. These national-level correlates have potential implications for northern Indigenous food security and well-being through recognized transmission mechanisms. The paradoxical positive correlation between rising commodity prices and ICWB is consistent with an adaptive response: as market food costs increase, communities may strengthen traditional food harvesting and local production, though higher equipment and resource prices may constrain these efforts, making food sovereignty enhancement a complex challenge. Findings suggest that northern communities participate in national economic systems through price, policy, and supply chain pathways, but may yet retain adaptive capacity through traditional food systems if persistent multi-stage supports are provided. Policy implications include indexing northern food subsidies to commodity price volatility, prioritizing funding for Indigenous-led food sovereignty initiatives that integrate traditional knowledge with modern techniques, and investing in infrastructure to reduce supply chain vulnerabilities. Future research should examine community-specific responses to national economic patterns and identify local factors that strengthen nature-led traditional food systems in northern Indigenous contexts. Full article

To determine the optimal operating parameters of the single-longitudinal-axial-flow threshing device for wheat harvesters under high feed rate conditions and lay a theoretical and methodological foundation for the subsequent development of a specialist system for threshing device parameter optimization, this study constructed a discrete element model (DEM) of wheat plants at harvest stage in EDEM software based on the Hertz–Mindlin with bonding contact model, combined with the discrete element parameters of wheat plants calibrated by a texturometer. Simulation experiments were designed and conducted to optimize the operating parameters of the threshing device, and a methodology that employs simulation experiments to obtain optimal parameter combinations under different operating environments was proposed, which was applied to the development of a specialist system for threshing device parameter optimization. Taking cylinder speed, cylinder inclination and threshing gap as experimental factors, and unthreshed rate, separation loss rate and breakage rate as evaluation indexes, single-factor and Box–Behnken simulation experiments were carried out. Mathematical models were established to reveal the correlation between evaluation indexes and influencing parameters, and then the optimal parameter combination was solved. The results showed that the threshing device achieved the optimal comprehensive operating performance at a cylinder speed of 850 r/min, a cylinder inclination of 6° and a threshing gap of 20 mm, with an unthreshed rate of 1.575%, a separation loss rate of 0.158% and a breakage rate of 0.509%. Bench validation tests indicated that the relative errors between simulated and measured indexes were all less than 5%, verifying the reliability of the established DEM. Compared with bench tests, the simulation experiment method in this study ensured high accuracy while significantly reducing the time and labor costs of tests. The obtained optimal operating parameters and the proposed simulation optimization methodology provide core data support and technical reference for the development of a specialist system for parameter optimization of the threshing device of wheat harvesters. Full article

Indonesia’s E10 blending mandate presents a strategic opportunity for decarbonization and inclusive rural development, contingent on a robust supply chain integrating smallholder farmers. This study developed a novel supply chain framework for corn products in Sumenep to facilitate sustainable ethanol production. Methods involved comprehensive data collection, mathematical modeling using the p-median method, and farmer clustering techniques. Findings reveal that Sumenep Regency’s substantial corn harvest of 8,475,914.5 tons, yielding 1,271,387.175 tons of kernels, can produce 381,416.1525 L of bioethanol. By applying a clustering supply chain model, the farmers’ group profit is IDR 205,693,725,826, while it is IDR 177,394,823,353 for the non-clustering model, meaning that the clustering supply chain model increases profit by 16% compared to the model without clustering. This localized production, enabled by a simplified, decentralized supply chain architecture, significantly enhances national energy security, reduces greenhouse gas emissions, and improves the economic stability of smallholder farmers through equitable value capture and minimized logistical costs. The framework offers a practical, implementable strategy for Indonesia’s energy transition, fostering environmental sustainability and inclusive socio-economic development. Full article

Wireless Sensor Networks (WSNs) enable energy-efficient data collection in dynamic environments but continue to face the dual challenges of severely constrained node energy and the spatiotemporal heterogeneity of data traffic. Inspired by honeybee foraging behavior, this paper proposes a hybrid optimization framework that integrates mixed-integer linear programming (MILP) and Markov decision processes (MDP), utilizing Q-learning for adaptive decision-making. The proposed framework systematically maps the dual-layer decision-making mechanism of honeybee foraging onto a synergistic architecture combining MILP-based global planning and MDP-based local adaptation, offering a novel bio-inspired solution for mobile sink trajectory planning and adaptive routing. Specifically, the upper-level MILP module simulates a colony-level global assessment of distant nectar sources, generating an initial global trajectory by determining the optimal access sequence of cluster heads to minimize the movement cost of the mobile sink. The lower-level Q-learning module simulates the individual-level local adaptation, where bees adjust harvesting behavior in real-time based on nectar quality and distance. This module continuously optimizes routing parameters based on real-time network states, including residual energy, the ratio of surviving nodes, data queue lengths, and cluster head density. The algorithm employs an $ϵ$-greedy strategy to balance exploration and exploitation, while a periodic decision-update mechanism is introduced to harmonize computational efficiency with learning stability. Furthermore, a multi-objective reward function is designed to jointly optimize energy efficiency, network lifetime, end-to-end latency, and path length. Extensive simulation results demonstrate that the proposed MILP-MDP hybrid framework significantly outperforms several representative baseline algorithms in terms of network lifetime extension and energy balance. These findings validate that the integration of bio-inspired foraging strategies and reinforcement learning provides an efficient and robust solution for trajectory planning and adaptive routing in dynamic WSNs. Full article

Human–AI systems often involve repeated interaction among users, organizations, and AI components rather than isolated model outputs. In such settings, cooperation can be pursued either by changing agent incentives or by adding an explicit accountability layer. We formalize the Institutional Monitoring and Ledger (IML) framework, which augments a Markov game with monitoring, evidence logging, delayed settlement, and review while leaving the base dynamics unchanged. We derive conservative incentive checks that clarify how detection quality, review accuracy, settlement delay, and sanction size jointly shape deterrence and wrongful-penalty risk. We then provide pilot evidence in two canonical sequential social dilemmas, Harvest and Cleanup, using five agents, PPO training, five training seeds per condition, and comparisons against PPO, inequity aversion, social influence, and IML ablations. In these settings, IML avoided some of the optimization instability observed in the representative internalization baselines tested here, made monitoring error directly visible through ledger records, and showed how false positives can accumulate into a persistent welfare cost. Agent-level analyses in these symmetric environments found nearly uniform measured enforcement burden, while temporal analyses showed that late-stage enforcement is increasingly dominated by residual false positives. These results do not establish legitimacy in human-facing settings or deployment readiness. They instead position IML as a framework with pilot evidence for studying accountability mechanisms in cooperative human–AI systems and highlight measurement error, review design, and due process as central design constraints. Full article

$Background$: In the grain logistics context, pre-processing operations such as reception, pre-cleaning, drying, storage, and shipping are performed at farm, collecting, intermediate, sub-terminal, and terminal storage units to preserve quality, reduce losses, and add value in the products. However, high transportation costs and limited static storage capacity reduce the selling prices. The objective of this article is to maximize profit associated with pre-processing, storage, and transportation along the grain flow in Brazil. $Methods$: A generic post-harvest logistics network is represented as a graph connecting producers, multi-level storage units, agribusiness facilities, and ports. A multi-period, multi-level mathematical model is applied in a case study framework explored in three scenarios, covering pre-cleaning, drying, storage, and transportation costs from production areas to commercialization nodes. $Results$: In all three scenarios, road transport resulted in transportation costs ranging from approximately US$ 49 million to US$ 492 million, mainly over long distances. $Conclusions$: The location and static capacity of collecting and intermediate storage units strongly influenced transport, storage use, CO₂ emissions, and post-harvest efficiency. Also, the flow concentration increased heavy-vehicle traffic, reducing overall logistics performance. Full article

Artificial intelligence (AI) is transforming modern agriculture from experience-driven practices to data-driven production paradigms. To provide an in-depth analysis of AI technologies in intelligent agriculture, we retrieved literature from Web of Science, IEEE Xplore, Google Scholar and Scopus, covering publications from 2015 to 2025, and 85 articles remained after screening 1867 relevant publications. These articles are grouped into three stages from perception, to decision making, to execution (PDE) in a closed-loop framework. At the perception level, we highlight progress in intelligent sensing systems, such as unmanned aerial vehicle (UAV) and multi-modal monitoring platforms, for crop disease and pest detection, growth monitoring and abiotic stress assessment. At the decision making level, integration of heterogeneous data sources, including meteorological records, soil measurements, remote sensing (RS) imagery and market information, supports advanced analytics, such as yield prediction, pest and disease warning, irrigation and fertilization planning, and crop management optimization. At the execution level, agricultural robots equipped with simultaneous localization and mapping (SLAM) and deep reinforcement learning (RL) facilitate precision spraying, autonomous harvesting, and unmanned field operations. Overall, AI technologies demonstrate substantial potential in the PDE pipeline of agricultural production. However, several challenges remain, including heterogeneous data fusion, limited generalization across diverse environments, complex system integration, and high hardware and deployment costs. Future directions are discussed from the perspectives of lightweight model design, cross-platform standardization, enhanced human–machine collaboration, and a deeper integration of emerging AI paradigms to support scalable, robust, and autonomous agricultural intelligence systems. Full article