Search Results (704)

This study addresses the issue of missing basic data and insufficient accuracy in predicting runoff and non-point-source pollution in the Heilongjiang region of China using the Soil and Water Assessment Tool (SWAT) model. Based on the China Ground Climate Data Daily Dataset (V3.0) and SPAW soil characteristic calculation formula, and assisted by the Python V3.0 language for data processing and computation, new high-precision weather generators and soil attribute databases suitable for the Heilongjiang region of China were established. The weather generator is based on daily data and contains detailed meteorological parameters such as temperature, humidity, wind speed, rainfall, etc., used to characterize the periodic changes in meteorological elements. And the differences and fluctuations outside this change curve were also retained in the basic construction of the weather generator. The soil database covers various parameters, such as soil type, texture, structure, nutrient content, organic matter content, etc., enabling the SWAT model to better simulate hydrological and pollutant transport processes in the soil. Additionally, point-source input data, including various industrial and domestic wastewater discharge situations, were collected and organized to improve data quality. Furthermore, a series of agricultural management measures were developed based on the use of fertilizers and pesticides for simulation, providing an important basis for analyzing non-point-source pollution using the SWAT model. By comparing the different results of the simulation using optimized databases, it is shown that the above work improved the simulation accuracy of the SWAT model in predicting runoff and pollution load in Heilongjiang, China. The NSE of runoff simulation increased from 0.923 to 0.988, and the NSE of ammonia nitrogen and CBOD simulation increased from 0.852 and 0.758 to 0.930 and 0.902, respectively. It is expected that these efforts will provide strong data support for subsequent research and provide a theoretical basis for government decision-makers to build scientifically rigorous and effective pollution control strategies. Full article

The growing global population intensifies food demand, challenging the agricultural sector to increase efficiency. Precision agriculture (PA) addresses this challenge by leveraging advanced technologies, such as the Internet of Things (IoT) and sensor networks, to collect and analyze field data. However, accessible tools for storing, managing, and analyzing these data are often limited. This study presents AgDataBox-IoT (ADB-IOT), a novel web application designed to fill this gap by providing a user-friendly platform for optimizing agricultural management. ADB-IOT integrates into the existing AgDataBox ecosystem, extending its capabilities with dedicated IoT functionalities. The application enables farmers to plan IoT networks, visualize and analyze field-collected data through thematic maps and graphs, and monitor and control IoT devices. This integrated approach facilitates informed decision-making, improves control over sustainable soil management, and enhances the overall efficiency of agricultural operations. As a freely accessible tool, ADB-IOT lowers the barrier to adopting precision agriculture technologies. Full article

Unmanned aerial vehicles (UAVs) have become essential tools for monitoring crop condition, detecting early signs of plant stress, and supporting timely interventions in modern precision agriculture. However, real-time onboard image analysis remains challenging due to the limited computational and energy resources of small embedded UAV platforms. This work presents a deployment-aware neural architecture search (NAS) framework for discovering lightweight object detection networks explicitly optimized for edge hardware constraints. Building on the YOLOv8n baseline, the proposed NAS procedure yields detector architectures that substantially reduce computational load while preserving high detection accuracy for agricultural field monitoring tasks. The best-discovered model reduces GFLOPs by 37.0% and parameters by 61.3% compared to YOLOv8n, with only a 1.96% decrease in mAP@50. When deployed on an NVIDIA Jetson Nano, it achieves a 28.1% increase in inference speed and an 18.5% improvement in energy efficiency under ONNX Runtime, with additional gains using TensorRT FP16. Evaluation on wheat head and cotton seedling datasets demonstrates strong generalization across crop types and varying imaging conditions. By enabling highly efficient onboard inference, the proposed NAS framework supports practical UAV-based crop monitoring workflows and contributes to the development of responsive, field-ready remote sensing systems in resource-limited environments. Full article

Accurate and non-destructive assessment of plant nutritional status remains a key challenge in precision agriculture, particularly under dynamic physiological conditions such as dehydration. Therefore, this study focused on developing an integrated nutritional assessment framework for avocado (Persea americana Mill.) leaves across progressive dehydration stages using spectral analysis. A novel nutritional function index (NFI) was innovatively constructed using an entropy-weighted multi-criteria decision-making approach. This unified assessment metric integrated critical physiological indicators, such as moisture content, nitrogen content, and chlorophyll content estimated from soil and plant analyzer development (SPAD) readings. To enhance the prediction accuracy and interpretability of NFI, innovative vegetation indices (VIs) specifically tailored to NFI were systematically constructed using exhaustive wavelength-combination screening. Optimal wavelengths identified from short-wave infrared regions (1446, 1455, 1465, 1865, and 1937 nm) were employed to build physiologically meaningful VIs, which were highly sensitive to moisture and biochemical constituents. Feature wavelengths selected via the successive projections algorithm and competitive adaptive reweighted sampling further reduced spectral redundancy and improved modeling efficiency. Both feature-level and algorithm-level data fusion methods effectively combined VIs and selected feature wavelengths, significantly enhancing prediction performance. The stacking algorithm demonstrated robust performance, achieving the highest predictive accuracy (R2V = 0.986, RMSEV = 0.032) for NFI estimation. This fusion-based modeling approach outperformed conventional single-model schemes in terms of accuracy and robustness. Unlike previous studies that focused on isolated spectral predictors, this work introduces an integrative framework combining entropy-weighted feature synthesis and multiscale fusion learning. The developed strategy offers a powerful tool for real-time plant health monitoring and supports precision agricultural decision-making. Full article

The Overseer model is widely used in New Zealand as a precision-agriculture-related tool for estimating nitrate (NO₃⁻) leaching losses in agricultural systems. This study evaluated the accuracy of the Overseer model in predicting nitrate (NO₃⁻) leaching through a two-year lysimeter experiment conducted at Woodhaven Gardens, New Zealand, under beetroot and pak choi cultivation. Seven distinct nitrogen (N) fertilizer treatments were applied to assess model performance. In year 1, Overseer overestimated NO₃⁻ leaching by an average of 45.2 kg N/ha (15.7%), and in year 2, the model overestimated by 35.2 kg N/ha (43.5%). A sensitivity analysis highlighted soil texture, impeded layer depth and crop residue incorporation as key drivers of leaching variability, underscoring the need for improved model calibration. Overseer performed reasonably well under lysimeter conditions, with a strong linear relationship (Pearson’s correlation coefficient r = 0.89, p < 0.0001) between measured and predicted values and explaining 77% of the variance (R² = 0.77) in the observed data. The model predicted a baseline leaching loss of 39.4 kg N/ha/year even when measured losses were zero. Overseer demonstrates moderate reliability in predicting NO₃⁻ leaching under vegetable cropping systems but exhibits notable limitations in handling crop-specific N dynamics, soil hydrology, and fertilizer timing. Full article

Unmanned aerial vehicle (UAV)-based multispectral monitoring has become an increasingly important tool for assessing crop vigor and stress under commercial agricultural conditions. However, most UAV-based studies using the normalized difference vegetation index (NDVI) in citrus systems have focused on yield estimation, disease detection, or canopy characterization during active growth phases, while the immediate post-harvest recovery period remains poorly documented. In this study, UAV-derived NDVI products were used to evaluate the canopy response in a commercial ‘Washington Navel’ orange orchard located in La Yarada Los Palos district (Tacna, Peru) following harvest. The study specifically assessed the effect of an on-farm, residue-based organic biostimulant produced from local organic wastes within a circular economy framework. The results indicate that treated plots exhibited a faster and more pronounced recovery of canopy vigor compared to untreated controls during the early post-harvest period. By integrating high-resolution UAV-based multispectral monitoring with a residue-derived biostimulant strategy, this work advances current NDVI-based applications in citrus by shifting the analytical focus from productive stages to post-harvest physiological recovery. The proposed approach provides a scalable and non-invasive framework for evaluating post-harvest canopy dynamics under water-limited, hyper-arid conditions and highlights the potential of locally sourced biostimulants as complementary management tools in precision agriculture systems. Full article

Digital twins, understood as computational replicas of poultry production systems updated in real time by sensor data, are increasingly invoked as transformative tools for precision livestock farming and sustainable agriculture. They are credited with enhancing feed efficiency, reducing greenhouse gas emissions, enabling disease detection earlier and improving animal welfare. Yet close examination of the published evidence reveals that these promises rest on a surprisingly narrow empirical foundation. Across the available literature, no peer reviewed study has quantified the full lifecycle carbon footprint of digital twin infrastructure in poultry production. Only one field validated investigation reports a measurable improvement in feed conversion ratio attributable to digital optimization, and that study’s design constrains its general applicability. A standardized performance assessment framework specific to poultry has not been established. Quantitative evaluations of reliability are scarce, limited to a small number of studies reporting data loss, sensor degradation and cloud system downtime, and no work has documented abandonment timelines or reasons for discontinuation. The result is a pronounced gap between technological aspiration and verified performance. Progress in this domain will depend on small-scale, deeply instrumented deployments capable of generating the longitudinal, multidimensional evidence required to substantiate the environmental and operational benefits attributed to digital twins. Full article

In the context of the rapid transformation of global agricultural production towards intensification and intelligence, the precise and intelligent management of horticultural crop production processes is key to enhancing resource utilization efficiency and industry profitability. Crop growth and development models, as digital representations of the interactions between environment, crops, and management, are core tools for achieving intelligent decision-making in facility production. This paper provides a comprehensive review of the advancements in intelligent management models and systems for horticultural crop growth and development. It introduces the developmental stages of horticultural crop growth models and the integration of multi-source data, systematically organizing and analyzing the modeling mechanisms of crop growth and development process models centered on developmental stages, photosynthesis and respiration, dry matter accumulation and allocation, and yield and quality formation. Furthermore, it summarizes the current status of expert decision-support system software development and application based on crop models, achieving comprehensive functionalities such as data and document management, model parameter management and optimization, growth process and environmental simulation, management plan design and effect evaluation, and result visualization and decision product dissemination. This illustrates the pathway from theoretical research to practical application of models. Addressing the current challenges related to the universality of mechanisms, multi-source data assimilation, and intelligent decision-making, the paper looks forward to future research directions, aiming to provide theoretical references and technological insights for the future development and system integration of intelligent management models for horticultural crop growth and development. Full article

Accurate estimation of crop evapotranspiration (ET) is essential for achieving efficient agricultural water use in the North China Plain. Although machine learning techniques have demonstrated considerable potential for ET simulation, a systematic evaluation of model-architecture suitability and hyperparameter optimization strategies specifically for summer soybean ET estimation in this region is still lacking. To address this gap, we systematically compared several machine learning architectures and their hyperparameter optimization schemes to develop a high-accuracy daily ET model for summer soybean in the North China Plain. Synchronous observations from a large-scale weighing lysimeter and an automatic weather station were first used to characterize the day-to-day dynamics of soybean ET and to identify the key driving variables. Four algorithms—support vector regression (SVR), Random Forest (RF), extreme gradient boosting (XGBoost), and a stacking ensemble—were then trained for ET simulation, while Particle Swarm Optimization (PSO), Genetic Algorithms (GAs), and Randomized Grid Search (RGS) were employed for hyperparameter tuning. Results show that solar radiation (R_S), maximum air temperature (T_max), and leaf area index (LAI) are the dominant drivers of ET. The Stacking-PSO-F3 combination, forced with Rs, T_max, LAI, maximum relative humidity (RH_max), and minimum relative humidity (RH_min), achieved the highest accuracy, yielding R² values of 0.948 on the test set and 0.900 in interannual validation, thereby demonstrating excellent precision, stability, and generalizability. The proposed model provides a robust technical tool for precision irrigation and regional water resource optimization. Full article

Cocoa production in West Africa supplies most of the global demand but is increasingly constrained by yield stagnation, soil degradation, disease pressure, and climate variability. This review examines how integrating regenerative agriculture (RA) with unmanned aerial systems (UAS) and artificial intelligence (AI) can support more precise and resilient cocoa management across heterogeneous smallholder landscapes. A PRISMA-guided systematic review of peer-reviewed literature published between 2000 and 2024 was conducted, yielding 49 core studies analyzed alongside supporting evidence. The synthesis evaluates regenerative agronomic outcomes, UAV-derived multispectral, thermal, and structural diagnostics, and AI-based analytical approaches for stress detection, yield estimation, and management zoning. Results indicate that regenerative practices consistently improve soil health and yield stability, while UAS data enhance spatial targeting of rehabilitation, shade management, and stress interventions. AI models further improve predictive capacity and decision relevance when aligned with data availability and institutional context, although performance varies across systems. Reported yield stabilization or improvement typically ranges from 12–30% under integrated approaches, with concurrent reductions in fertilizer and water inputs where spatial targeting is applied. The review concludes that effective scaling of RA–UAS–AI systems depends less on technical sophistication than on governance arrangements, extension integration, and cooperative service models, positioning these tools as enabling components rather than standalone solutions for sustainable cocoa intensification. Full article

Digital transformation in agriculture addresses key challenges such as climate change, water shortages, and sustainable production. Precision agriculture technologies rely on the Internet of Things (IoT) sensor networks, analytics, and automated systems to manage resources efficiently and increase productivity. Fragmented infrastructures and vendor-specific platforms lead to unintegrated data silos that obstruct regional solutions. This paper will emphasize FIWARE, an open-source, standard-based platform that can be integrated with existing agricultural sensors in municipalities or regions. FIWARE takes all these disparate sensors (soil probes, weather stations, and irrigation meters) and integrates them into a single real-time information system, providing a set of decision support tools to the user to facilitate adaptive irrigation. Case studies show the benefits of FIWARE, including water savings, reduced runoff, better decision-making, and improved climate resilience. Full article

A key challenge in precision agriculture is acquiring reliable spatial soil information under varying sampling densities, from sparse surveys to intensive monitoring. The individual predictive soil mapping (iPSM) method performs well in data-scarce conditions but neglects spatial proximity, limiting its predictive accuracy where spatial autocorrelation exists. To overcome this, we developed an enhanced framework, iPSM-Spatial Distance (iPSM-SD), which systematically integrates spatial proximity through multiplicative (MUL) and additive (ADD) strategies. The framework was validated using two contrasting cases: sparse soil organic carbon density data from Yunnan Province (n = 118) and dense soil organic matter data from Bayi Farm (n = 2511). Results show that the additive model (iPSM-ADD) significantly outperformed the original iPSM and benchmark models, including random forest, regression kriging, geographically weighted regression, and multiple linear regression, under sufficient sampling, achieving an R² of 0.86 and reducing RMSE by 46.6% at Bayi Farm. It also maintained robust accuracy under sparse sampling conditions. The iPSM-SD framework thus provides a unified and adaptive tool for digital soil mapping across a wide range of data availability, supporting scalable soil management decisions from regional assessment to field-scale variable-rate applications in precision agriculture. Full article

Understanding the complex interplay between environmental factors and fruit quality development requires sophisticated analytical approaches linking cellular architecture to environmental conditions. This study introduces a novel application of dual-resolution X-ray computed tomography (CT) for the non-destructive characterization of apple internal tissue architecture in relation to fruit growth, thereby advancing beyond traditional methods that are primarily focused on postharvest analysis. By extracting detailed three-dimensional structural parameters, we reveal tissue porosity and heterogeneity influenced by crop load, maturity timing and canopy position, offering insights into internal quality attributes. Employing correlation analysis, Principal Component Analysis, Canonical Correlation Analysis, and Structural Equation Modeling, we identify temperature as the primary environmental driver, particularly during early developmental stages (45 Days After Full Bloom, DAFB), and uncover nonlinear, hierarchical effects of preharvest environmental factors such as vapor pressure deficit, relative humidity, and light on quality traits. Machine learning models (Multiple Linear Regression, Random Forest, XGBoost) achieve high predictive accuracy (R² > 0.99 for Multiple Linear Regression), with temperature as the key predictor. These baseline results represent findings from a single growing season and require validation across multiple seasons and cultivars before operational application. Temporal analysis highlights the importance of early-stage environmental conditions. Integrating structural and environmental data through innovative visualization tools, such as anatomy-based radar charts, facilitates comprehensive interpretation of complex interactions. This multidisciplinary framework enhances predictive precision and provides a baseline methodology to support precision orchard management under typical agricultural variability. Full article

To address the challenge of optimizing hydrological parameters for nitrogen pollution control in paddy polders, this study coupled the Stella eco-dynamics model with an external optimization algorithm and developed a nonlinear programming framework using the water surface ratio and inflow rate as decision variables and the maximum nitrogen removal rate as the objective function. The simulation and optimization conducted for the Hongze Lake polder area indicated that the model exhibited strong robustness, as verified through Monte Carlo uncertainty analysis, with coefficients of variation (CV) of nitrogen outlet concentrations all below 3%. Under the optimal regulation scheme, the maximum nitrogen removal rates (${\eta }_1$, ${\eta }_2$, and ${\eta }_4$) during the soaking, tillering, and grain-filling periods reached 98.86%, 98.74%, and 96.26%, respectively. The corresponding optimal inflow rates ($Q^{*}$) were aligned with the lower threshold limits of each growth period (1.20, 0.80, and 0.50 m³/s). The optimal channel water surface ratios ($A_1^{*}$) were 3.81%, 3.51%, and 3.34%, respectively, while the optimal pond water surface ratios ($A_2^{*}$) were 19.94%, 16.30%, and 17.54%, respectively. Owing to the agronomic conflict between “water retention without drainage” and concentrated fertilization during the heading period, the maximum nitrogen removal rate (${\eta }_3$) during this stage was only 37.34%. The optimal channel water surface ratio ($A_1^{*}$) was 2.37%, the pond water surface ratio ($A_2^{*}$) was 19.04%, and the outlet total nitrogen load increased to 8.39 mg/L. Morphological analysis demonstrated that nitrate nitrogen and organic nitrogen dominated the outlet water body. The “simulation–optimization” coupled framework established in this study can provides quantifiable decision-making tools and methodological support for the precise control and sustainable management of agricultural non-point source pollution in the floodplain area. Full article

Reducing dependency on chemical pesticides is a core ambition of the European Green Deal, yet adoption of low-input practices remains uneven. This systematic review synthesizes evidence on the behavioural determinants of European farmers’ knowledge, attitudes, and practices (KAP) regarding sustainable pesticide use and evaluates the role of digital tools in facilitating Integrated Pest Management (IPM). Following PRISMA 2020 guidelines, we analysed 65 peer-reviewed articles published between 2011 and 2025, which were identified through Scopus and Web of Science. The synthesis reveals that while pro-environmental attitudes drive the intention to change, actual behaviour is frequently inhibited by loss aversion, ‘clean field’ social norms, and perceived economic risks. Digital tools—specifically Decision Support Systems (DSSs) and precision technologies—demonstrate technical potential to reduce pesticide loads but are constrained by the same behavioural barriers: a lack of trust in models, perceived complexity, and costs. Consequently, we propose a Psycho-Digital Integration Framework which posits that digital innovation acts as a catalyst only when embedded in systemic enablers—specifically green insurance schemes and independent advisory networks. These mechanisms are critical to redistribute perceived agricultural risk and bridge the gap between technical potential and behavioral adoption. Full article