Unleashing the Power of Generative AI in Agriculture 4.0 for Smart and Sustainable Farming

Cognitive Computation, Journal Year: 2025, Volume and Issue: 17(1)

Published: Feb. 1, 2025

Language: Английский

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Aero-Thermodynamics of UAV Downwash for Dynamic Microclimate Engineering: Ameliorating Effects on Rice Growth, Yield, and Physiological Traits Across Key Growth Stages

Agriculture, Journal Year: 2025, Volume and Issue: 15(1), P. 78 - 78

Published: Jan. 1, 2025

A comprehensive investigation into the aero-thermodynamic impacts of UAV-generated airflow on rice microclimate is essential to elucidate complex relationships between wind speed, temperature, and temporal dynamics during critical growth stages rice. Focusing vulnerable such as heading, panicle, flowering, this research aims advance understanding microclimatic influences crops, thereby informing development UAV-based strategies enhance crop resilience optimize yields. By utilizing UAV rotor downwash, examines temperature speed at three key diurnal intervals: 9:00 a.m., 12:00 p.m., 3:00 p.m. At UAV-induced creates a stable with favourable temperatures (27.45–28.45 °C) optimal speeds (0.0700–2.050 m/s), which promote support pollen transfer grain setting. peak 2.370 m/s, inducing evaporative cooling while maintaining stability, yet leading some moisture loss. reach 28.48 °C, 72% decrease in from midday, effectively conserving phases. The results reveal that positively panicle flowering stages, where carefully moderated (up 3 m/s) reduce sterility, fertilization, reproductive development. This highlights potential UAV-engineered management mitigate stress factors improve yield through targeted regulation. Key agronomic parameters showed significant improvements, including stem diameter, canopy regulation, filling duration, productive tillers (increasing by 30.77%), total tillers, flag leaf area, grains per (rising 46.55%), biological yield, (surging 70.75%), harvest index. Conclusively, effects were observed a.m. applications outperforming midday late-afternoon treatments. Additionally, significantly increased yield. interaction timing stage (RRS × GS) exhibited low moderate effects, underscoring importance precise maximizing productivity.

Language: Английский

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Sustainable Agriculture with Self-Powered Wireless Sensing

Agriculture, Journal Year: 2025, Volume and Issue: 15(3), P. 234 - 234

Published: Jan. 22, 2025

Agricultural sustainability is becoming more and important for human health. Wireless sensing technology could provide smart monitoring in real time different parameters planting, breeding, the food supply chain with advanced sensors such as flexible sensors; wireless communication networks third-, fourth-, or fifth-generation (3G, 4G, 5G) mobile networks; artificial intelligence (AI) models. Many sustainable, natural, renewable, recycled facility energies light, wind, water, heat, acoustic, radio frequency (RF), microbe that exist actual agricultural systems be harvested by self-powered technologies devices using solar cells, electromagnetic generators (EMGs), thermoelectric (TEGs), piezoelectric (PZGs), triboelectric nanogenerators (TENGs), microbial full cells (MFCs). Sustainable energy harvesting to maximum extent possible lead creation of sustainable devices, reduce carbon emissions, result implementation precision monitoring, management, decision making production. Therefore, this article suggests proposing developing a system agriculture (SAS) would an effective way improve production efficiency while achieving green and, finally, ensuring quality safety

Language: Английский

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Development and laboratory evaluation of a novel IoT-based electric-driven metering system for high precision garlic planter

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(1), P. e0317203 - e0317203

Published: Jan. 17, 2025

In order to address many issues, such as the inconsistent and unreliable seeding process in traditional mechanical garlic seed metering systems (SMS), well lack of ability monitor effectiveness seeding, a highly accurate electric-driven system (EDMS) was developed created specifically for planters. This study provided description overall structure functioning principle, an analysis mechanism smooth transit delivery. A combination infrared (IR) sensor, Arduino Mega board, stepper motor, speed Wi-Fi module employed operate EDMS, count quantity seeds during planting determine qualified rate (QR) missing (MR). monitoring quality based on IoT concept. Then, performance EDMS validated laboratory setting utilizing bench test at six operating velocities 10, 20, 30, 40, 50, 60 rpm EDMS. The obtained results showed that correlation coefficient between actual detected using counting (GSMCS) 0.9723. Additionally, observed maximum QR 96.23% velocity 20 rpm, with standard division error 1.61030 0.72015, respectively. minimized MR up 3.77% same velocity, 1.65325 0.73936, Furthermore, indicated progressive increase MQ errors EDMS’s increased. sensor’s accuracy gradually declined Finally, this introduced novel planters not used before. GSMCS are technical manuals developing designing capable precisely measuring identifying rates qualifying measurements.

Language: Английский

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Development of an Optimized YOLO-PP-Based Cherry Tomato Detection System for Autonomous Precision Harvesting

Processes, Journal Year: 2025, Volume and Issue: 13(2), P. 353 - 353

Published: Jan. 27, 2025

An accurate and efficient detection method for harvesting is crucial the development of automated robots in short-cycle, high-yield facility tomato cultivation environments. This study focuses on cherry tomatoes, which grow clusters, addresses complexity reduced speed associated with current multi-step processes that combine target segmentation traditional image processing clustered fruits. We propose YOLO-Picking Point (YOLO-PP), an improved picking point network designed to efficiently accurately identify stem keypoints embedded devices. YOLO-PP employs a C2FET module EfficientViT branch, utilizing parallel dual-path feature extraction enhance performance dense scenes. Additionally, we implemented Spatial Pyramid Squeeze Pooling (SPSP) extract fine features capture multi-scale spatial information. Furthermore, new loss function based Inner-CIoU was developed specifically keypoint tasks further improve efficiency.The model tested real greenhouse dataset, achieving accuracy 95.81%, recall rate 98.86%, mean Average Precision (mAP) scores 99.18% 98.87% mAP50 mAP50-95, respectively. Compared DEKR, YOLO-Pose, YOLOv8-Pose models, mAP value by 16.94%, 10.83%, 0.81%, The proposed algorithm has been NVIDIA Jetson edge computing devices, equipped human–computer interaction interface. results demonstrate Improved Picking Detection Network exhibits excellent achieves real-time agriculture.

Language: Английский

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An efficient function placement approach in serverless edge computing

Computing, Journal Year: 2025, Volume and Issue: 107(3)

Published: Feb. 21, 2025

Language: Английский

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Sustainable farming with machine learning solutions for minimizing food waste

Journal of Stored Products Research, Journal Year: 2025, Volume and Issue: 112, P. 102611 - 102611

Published: March 3, 2025

Language: Английский

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Enhanced multi agent coordination algorithm for drone swarm patrolling in durian orchards

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 17, 2025

This study proposes an enhanced multi-agent swarm control algorithm (EN-MASCA) to solve the problem of efficient patrolling drone swarms in complex durian orchard environments. It introduces a virtual navigator model dynamically adjust patrol path and perform obstacle avoidance optimization real time according environmental changes. Different from traditional algorithms that only rely on fixed planning, significantly improves flexibility stability also applies deep reinforcement learning planning for first time, improving algorithm's adaptability capabilities by dynamic information innovation applicability existing methods terrain Finally, it incorporates simulation characteristics biological behavior, this basis, comprehensively optimizes flight path, swarm. By strategies parameter design, trajectory consistency mission completion efficiency UAV during flight. In experimental part, verified detail advantages EN-MASCA terms trajectory, stability, cluster task constructing six-degree-of-freedom motion environment simulation. provides intelligent solution collaborative operations drones orchards, which has important practical application value promotion prospects.

Language: Английский

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Weather-Driven Predictive Models for Jassid and Thrips Infestation in Cotton Crop

Sustainability, Journal Year: 2025, Volume and Issue: 17(7), P. 2803 - 2803

Published: March 21, 2025

Agriculture is a vital contributor to global food security but faces escalating threats from environmental fluctuations and pest incursions. Among the most prevalent destructive pests, Jassid (Amrasca biguttula) Thrips (Thrips tabaci) frequently afflict cotton, okra, other major crops, resulting in substantial yield losses worldwide. This paper integrates five machine learning (ML) models predict incidence based on key meteorological attributes, including temperature, relative humidity, wind speed, sunshine hours, evaporation. Two ensemble strategies, soft voting stacking, were evaluated enhance predictive performance. Our findings indicate that stacking yields superior results, achieving high multi-class AUC scores (0.985). To demystify underlying mechanisms of best-performing ensemble, this study employed SHapley Additive exPlanations (SHAP) quantify contributions individual weather parameters. The SHAP analysis revealed Standard Meteorological Week, evaporation, humidity consistently exert strongest influence forecasts. These insights align with biological studies highlighting role seasonality humid conditions fostering proliferation. Importantly, explainable approach bolsters practical utility AI-based solutions for integrated management (IPM), enabling stakeholders—farmers, extension agents, policymakers—to trust effectively operationalize data-driven recommendations. Future research will focus integrating real-time data satellite imagery further prediction accuracy, as well incorporating adaptive techniques refine model performance under varying climatic conditions.

Language: Английский

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A lightweight deep learning model for multi-plant biotic stress classification and detection for sustainable agriculture

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 9, 2025

Language: Английский

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