Search Results (2,318)

Understanding when and where to shift land from agriculture to forestry is essential for designing sustainable land use strategies that align with climate, biodiversity, and rural development goals. However, traditional profitability comparisons rely on long-term discounting, which is highly sensitive to assumptions and often misaligned with the shorter-term decision-making horizons that are relevant for policymakers. This study presents a deposit-based framework that interprets annual timber biomass growth as accumulating economic value, enabling direct, per-hectare comparisons with yearly agricultural profits. The framework integrates parcel-level spatial data, land quality indicators, national statistics, and expert inputs to produce high-resolution maps of annual profitability for both agriculture and forestry. Applied to the case of Latvia, the results show strong spatial variation in agricultural returns, particularly in low-quality areas where profits are marginal or negative. By contrast, forestry provides more stable, though modest, economic gains across a wide range of biophysical conditions. These insights help identify where afforestation becomes a financially viable land use alternative. The framework is designed to be transferable to other regions by substituting local data on land quality, prices and growth. It complements policy instruments such as performance-based CAP payments and afforestation support, offering a future-oriented tool for spatially explicit and economically grounded land use planning. Full article

Accurate cropland classification within smallholder farming systems is essential for effective land management, efficient resource allocation, and informed agricultural decision-making. This study evaluates cropland classification performance using Red, Green, Blue (RGB) and multi-spectral (blue, green, red, red-edge, near-infrared) unmanned aerial vehicle (UAV) imagery. Both datasets were derived from imagery acquired using a MicaSense Altum sensor mounted on a DJI Matrice 300 UAV. Cropland classification was performed using machine learning algorithms implemented within the Google Earth Engine (GEE) platform, applying both a non-binary classification of five land cover classes and a binary classification within a probabilistic framework to distinguishing cropland from non-cropland areas. The results indicate that multi-spectral imagery achieved higher classification accuracy than RGB imagery for non-binary classification, with overall accuracies of 75% and 68%, respectively. For binary cropland classification, RGB imagery achieved an area under the receiver operating characteristic curve (AUC–ROC) of 0.75, compared to 0.77 for multi-spectral imagery. These findings suggest that, while multi-spectral data provides improved classification performance, RGB imagery can achieve comparable accuracy for fundamental cropland delineation. This study contributes baseline evidence on the relative performance of RGB and multi-spectral UAV imagery for cropland mapping in heterogeneous smallholder farming landscapes and supports further investigation of RGB-based approaches in resource-constrained agricultural contexts. 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

Expanded and intensified agriculture is a major driver of habitat loss for endangered species such as the Oriental White Stork (Ciconia boyciana), making wildlife-friendly farming an increasingly important approach for reconciling biodiversity conservation with agricultural development. Building on a 2018 feasibility study in the Sanjiang Plain, this research employs a choice experiment to examine how preferences for Oriental White Stork-friendly farming have evolved among urban consumers and residents of stork habitats under expanding green consumption and increasing experience with environmentally friendly farming. The results reveal pronounced preference heterogeneity and persistent cognitive separation between wildlife conservation and agricultural production, particularly among urban consumers, despite a stable group being willing to pay a premium for stork-friendly products. Rural residents’ decisions remain largely economically driven, though younger farmers with prior experience in environmentally friendly practices show more positive attitudes. Significant urban–rural differences suggest policy complementarities, whereby price-oriented incentives may encourage price-sensitive farmers to adopt green agriculture, while intrinsically motivated farmers require support through an Oriental White Stork-oriented value chain. Overall, the findings demonstrate that Wildlife-Friendly Farming cannot be effectively promoted through a one-size-fits-all approach; instead, stratified, group-specific policy and market mechanisms are essential for aligning producer incentives with consumer demand and supporting the long-term viability of biodiversity-friendly agricultural systems. Full article

Multi-generation intelligent breeding (MGIB) decision-making is a technique used by plant breeders to select mating individuals to produce new generations and allocate resources for each generation. However, existing research remains scarce on dynamic optimization of resources under limited budget and time constraints. Inspired by advances in reinforcement learning (RL), a framework that integrates evolutionary algorithms with deep RL was proposed to fill this gap. The framework combines two modules: the Improved Look-Ahead Selection (ILAS) module and Deep Q-Networks (DQNs) module. The former employs a simulated annealing-enhanced estimation of the distribution algorithm to make mating decisions. Based on the selected mating individual, the latter module learns multi-generation resource allocation policies using DQN. To evaluate our framework, numerical experiments were conducted on two realistic breeding datasets, i.e., Corn2019 and CUBIC. The ILAS outperformed LAS on corn2019, increasing the maximum and mean population Genomic Estimated Breeding Value (GEBV) by 9.1% and 7.7%. ILAS-DQN consistently outperformed the baseline methods, achieving significant and practical improvements in both top-performing and elite-average GEBVs across two independent datasets. The results demonstrated that our method outperforms traditional baselines, in both generalization and effectiveness for complex agricultural problems with delayed rewards. Full article

Uncertainty-aware decision-making increasingly relies on multimodal sensing pipelines that must fuse correlated measurements, propagate uncertainty, and trigger reliable control actions. This study develops a unified mathematical framework for multimodal data fusion and Bayesian decision-making under uncertainty. The approach integrates adaptive Covariance Intersection (aCI) for correlation-robust sensor fusion, a Gaussian state–space backbone with Kalman filtering, heteroskedastic Bayesian regression with full posterior sampling via an affine-invariant MCMC sampler, and a Bayesian likelihood-ratio test (LRT) coupled to a risk-sensitive proportional–derivative (PD) control law. Theoretical guarantees are provided by bounding the state covariance under stability conditions, establishing convexity of the aCI weight optimization on the simplex, and deriving a Bayes-risk-optimal decision threshold for the LRT under symmetric Gaussian likelihoods. A proof-of-concept agro-environmental decision-support application is considered, where heterogeneous data streams (IoT soil sensors, meteorological stations, and drone-derived vegetation indices) are fused to generate early-warning alarms for crop stress and to adapt irrigation and fertilization inputs. The proposed pipeline reduces predictive variance and sharpens posterior credible intervals (up to 34% narrower 95% intervals and 44% lower NLL/Brier score under heteroskedastic modeling), while a Bayesian uncertainty-aware controller achieves 14.2% lower water usage and 35.5% fewer false stress alarms compared to a rule-based strategy. The framework is mathematically grounded yet domain-independent, providing a probabilistic pipeline that propagates uncertainty from raw multimodal data to operational control actions, and can be transferred beyond agriculture to robotics, signal processing, and environmental monitoring applications. Full article

Drought remains a phenomenal disaster of critical concerns in West Africa, particularly within the Niger River Basin, due to its insidious, multifaceted, and long-lasting nature. Its continuous severe impacts on communities, combined with the limitations of existing univariate index-based monitoring methods, worsen the challenge. This paper introduces and evaluates a Hybrid Drought Resilience Empirical Model (DREM) that integrates meteorological, agricultural, and hydrological indicators to improve their concurrent monitoring and early warning for effective decision-making in the region. Using reanalysis hydrometeorological data (1980–2016) and community vulnerability records, results show that the DREM-based composite index detects drought earlier than the Standardized Precipitation Index (SPI), with stronger alignment to soil moisture and streamflow variations. The model identifies drought onset when thresholds range from −0.26 to −1.19 over three consecutive months, depending on location, and signals drought termination when thresholds rise between −0.08 and −0.82. The study concludes that the DREM-based composite index provides a more reliable and integrated framework for early drought detection and decision-making across the Niger River Basin, and hence, has proven to be a suitable drought monitor for stakeholders in the Niger Basin which can be relied upon and trusted with high confidence. Full article

Efficient irrigation planning in data-scarce regions remains challenging due to limited access to localized meteorological data, reliance on complex computer-based models, and the technical knowledge required to deploy them at the field scale. Hence, the need for accessible, smartphone-based tools that simplify soil water balance calculations using public data to support practical decision-making in resource-limited contexts. This smartphone-based application estimates Net and Gross Irrigation Requirements using a Soil Water Balance (SWB) framework. The app combines region-specific empirical formulations for Effective Rainfall (Pe) calculation. The application utilizes user-supplied crop and irrigation parameters and meteorological data available in the public domain and operates at multiple temporal scales (daily, 10-day, weekly, and monthly), thereby supporting flexible irrigation schedules. The performance of app was evaluated through simulation-based benchmarking against FAO-CROPWAT 8.0 using harmonized inputs across five representatives agro-climatic region: Central India, Southern Vietnam, Northern Thailand, Western Bangladesh, and Central Sri Lanka. Quantitative comparison showed deviations within ±5% for Effective Rainfall, crop evapotranspiration, Net Irrigation, and Gross Irrigation, and low mean bias values (−2.8% to +3.3%) show the absence of systematic over- or under-estimation compared to CROPWAT model. The application also demonstrated responsiveness to climatic variability. Although the validation is limited to few representative locations and assumed minimal runoff conditions, the results suggest that the proposed method is technically consistent and feasible in practice. This study demonstrates smartphone-based application as a decision support for field-level irrigation planning and water resource management, particularly in data-limited agricultural contexts. Full article

Maize is one of the top three crops globally, ranking only behind rice and wheat, making it an important crop of interest. Aboveground biomass is a key indicator for assessing maize growth and its yield potential. This study developed an efficient and stable biomass prediction model to estimate the aboveground biomass (AGB) of spring maize (Zea mays L.) under subsurface drip irrigation in arid regions, based on UAV multispectral remote sensing and machine learning techniques. Focusing on typical subsurface drip-irrigated spring maize in arid Xinjiang, multispectral images and field-measured AGB data were collected from 96 sample points (selected via stratified random sampling across 24 plots) over four key phenological stages in 2024 and 2025. Sixteen vegetation indices were calculated and 40 texture features were extracted using the gray-level co-occurrence matrix method, while an integrated feature-selection strategy combining Elastic Net and Random Forest was employed to effectively screen key predictor variables. Based on the selected features, six machine learning models were constructed, including Elastic Net Regression (ENR), Gradient Boosting Decision Trees (GBDT), Gaussian Process Regression (GPR), Partial Least Squares Regression (PLSR), Random Forest (RF), and Extreme Gradient Boosting (XGB). Results showed that the fused feature set comprised four vegetation indices (GRDVI, RERVI, GRVI, NDVI) and five texture features (R_Corr, NIR_Mean, NIR_Vari, B_Mean, B_Corr), thereby retaining red-edge and visible-light texture information highly sensitive to AGB. The GPR model based on the fused features exhibited the best performance (test set R² = 0.852, RMSE = 2890.74 kg ha⁻¹, MAE = 1676.70 kg ha⁻¹), demonstrating high fitting accuracy and stable predictive ability across both the training and test sets. Spatial inversions over the two growing seasons of 2024 and 2025, derived from the fused-feature GPR optimal model at four key phenological stages, revealed pronounced spatiotemporal heterogeneity and stage-dependent dynamics of spring maize AGB: the biomass accumulates rapidly from jointing to grain filling, slows thereafter, and peaks at maturity. At a constant planting density, AGB increased markedly with nitrogen inputs from N0 to N3 (420 kg N ha⁻¹), with the high-nitrogen N3 treatment producing the greatest biomass; this successfully captured the regulatory effect of the nitrogen gradient on maize growth, provided reliable data for variable-rate fertilization, and is highly relevant for optimizing water–fertilizer coordination in subsurface drip irrigation systems. Future research may extend this integrated feature selection and modeling framework to monitor the growth and estimate the yield of other crops, such as rice and cotton, thereby validating its generalizability and robustness in diverse agricultural scenarios. Full article

Sugarcane diseases cause estimated global annual losses of over $5 billion. While deep learning shows promise for disease detection, current approaches lack transparency and confidence estimates, limiting their adoption by agricultural stakeholders. We developed an uncertainty-aware detection system integrating Monte Carlo (MC) dropout with MobileNetV3, trained on 2521 images across five categories: Healthy, Mosaic, Red Rot, Rust, and Yellow. The proposed framework achieved 97.23% accuracy with a lightweight architecture comprising 5.4 M parameters. It enabled a 2.3 s inference while generating well-calibrated uncertainty estimates that were 4.0 times higher for misclassifications. High-confidence predictions (>70%) achieved 98.2% accuracy. Gradient-weighted Class Activation Mapping provided interpretable disease localization, and the system was deployed on Hugging Face Spaces for global accessibility. The model demonstrated high recall for the Healthy and Red Rot classes. The model achieved comparatively higher recall for the Healthy and Red Rot classes. The inclusion of uncertainty quantification provides additional information that may support more informed decision-making in precision agriculture applications involving farmers and agronomists. Full article

The accelerating low-carbon transition requires decision-support approaches capable of addressing complex, interdependent sustainability challenges across multiple sectors. While Multi-Criteria Decision-Making (MCDM) techniques are gaining popularity in assessing sustainability within energy and agricultural systems, their current application remains fragmented, sector-focused, and poorly aligned with the fundamental system characteristics of uncertainty, circularity, and social equity. This Perspective employs a systematized conceptual analysis to integrate different MCDM techniques, methodological trends, and integration challenges in energy and agricultural systems. Through a literature review, this work provides a critical view of the predominant structural deficiencies, which stem from methodological isolation, the use of disparate and heterogeneous datasets, ad hoc treatment of uncertainty, and the lack of incorporation of the circular economy (CE) and equity dimensions in the analysis. Given the presence of multifunctionality, circularity, climate sensitivity, and strong social characteristics, the analysis underscores that agriculture is a prime candidate to serve as a system-level testbed for the development of integrated MCDM frameworks. Based on this analysis, the paper articulates the fundamental characteristics of next-generation MCDM frameworks that are cross-sectoral, flexible, adaptive, uncertainty-resilient, and actionable. In doing so, it prioritizes integrated approaches that combine MCDM with life cycle assessment (LCA), data analytics, and nexus modelling. This paper stresses that structural deficiencies need to be addressed for MCDM to evolve from sectoral and fragmented analytical frameworks to cohesive decision-support systems that can guide energy and agricultural systems transitions towards equity, circularity, and climate change adaptation. As a perspective, this paper does not aim to provide empirical validation but instead articulates conceptual design principles for next-generation MCDM frameworks that integrate uncertainty, circularity, and social equity across energy and agricultural systems. 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

This paper presents a low-cost, fully on-premise Edge Artificial Intelligence (AI) system designed to support real-time pest and disease detection in open-field chili pepper cultivation. The proposed architecture integrates AI-Thinker ESP32-CAM module (ESP32-CAM) image acquisition nodes (“Sticks”) with a Raspberry Pi 5–based edge server (“Module”), forming a plug-and-play Internet of Things (IoT) pipeline that enables autonomous operation upon simple power-up, making it suitable for aging farmers and resource-limited environments. A Leaf-First 2-Stage vision model was developed by combining YOLOv8n-based leaf detection with a lightweight ResNet-18 classifier to improve the diagnostic accuracy for small lesions commonly occurring in dense pepper foliage. To address network instability, which is a major challenge in open-field agriculture, the system adopted a dual-protocol communication design using Hyper Text Transfer Protocol (HTTP) for Joint Photographic Experts Group (JPEG) transmission and Message Queuing Telemetry Transport (MQTT) for event-driven feedback, enhanced by Redis-based asynchronous buffering and state recovery. Deployment-oriented experiments under controlled conditions demonstrated an average end-to-end latency of 0.86 s from image capture to Light Emitting Diode (LED) alert, validating the system’s suitability for real-time decision support in crop management. Compared to heavier models (e.g., YOLOv11 and ResNet-50), the lightweight architecture reduced the computational cost by more than 60%, with minimal loss in detection accuracy. This study highlights the practical feasibility of resource-constrained Edge AI systems for open-field smart farming by emphasizing system-level integration, robustness, and real-time operability, and provides a deployment-oriented framework for future extension to other crops. Full article

In the field of smart agriculture, soil property data at the field scale drives the precision decision-making of agricultural inputs such as seeds and chemical fertilizers. However, soil texture has significant spatial variability at the field scale, and traditional remote sensing monitoring methods have certain data intermittency, which limits small-scale prediction research. In this study, based on the Google Earth Engine platform, soil synthetic images were generated according to different time intervals using mean compositing and median compositing modes, image bands were extracted, and spectral indices were introduced; combined with the random forest algorithm, the effects of different compositing time windows, compositing modes, and compositing data types on prediction accuracy were evaluated; and three partitioning strategies based on crop growth, soil synthetic image brightness, and soil type were adopted to conduct local partitioning regression of soil texture. The results show that: (1) The use of mean compositing of multi-year May images from 2021 to 2024 can improve prediction accuracy. When this method is combined with the “band reflectance + spectral indices” dataset, compared with other compositing methods, the R² of clay particles, silt particles, and sand particles can be increased by 8.89%, 9.50%, and 2.48% on average. (2) Compared with using only image band data, the introduction of spectral indices can significantly improve the prediction accuracy of soil texture at the field scale, and the R² of clay particles, silt particles, and sand particles is increased by 4.58%, 3.43%, and 4.59% on average, respectively. (3) Global regression is superior to local partitioning regression; however, the local partitioning regression strategy based on soil type has good accuracy performance. Under the optimal compositing method, the average R² of soil particles of each size fraction is only 1.08% lower than that of global regression, which has great application potential. This study innovatively constructs a comprehensive strategy of “moisture spectral indices + specific compositing time window + specific compositing mode + soil type partitioning”, providing a new paradigm for soil texture prediction at the field scale in Northeastern China, and lays the foundation for data-driven water and fertilizer decision-making. Full article

Support Vector Machines (SVMs) are widely used in critical decision-making applications, such as precision agriculture, due to their strong theoretical foundations and their ability to construct an optimal separating hyperplane in high-dimensional spaces. However, the effectiveness of SVMs is highly dependent on the efficiency of the optimization algorithm used to solve their underlying dual problem, which is often complex and constrained. Classical solvers, such as Sequential Minimal Optimization (SMO) and Stochastic Gradient Descent (SGD), present inherent limitations: SMO ensures numerical stability but lacks scalability and is sensitive to heuristics, while SGD scales well but suffers from unstable convergence and limited suitability for nonlinear kernels. To address these challenges, this study proposes a novel hybrid optimization framework based on Open Competency Optimization and Particle Swarm Optimization (OCO–PSO) to enhance the training of SVMs. The proposed approach combines the global exploration capability of PSO with the adaptive competency-based learning mechanism of OCO, enabling efficient exploration of the solution space, avoidance of local minima, and strict enforcement of dual constraints on the Lagrange multipliers. Across multiple datasets spanning medical (diabetes), agricultural yield, signal processing (sonar and ionosphere), and imbalanced synthetic data, the proposed OCO-PSO–SVM consistently outperforms classical SVM solvers (SMO and SGD) as well as widely used classifiers, including decision trees and random forests, in terms of accuracy, macro-F1-score, Matthews correlation coefficient (MCC), and ROC-AUC. On the Ionosphere dataset, OCO-PSO achieves an accuracy of $95.71\%$, an F1-score of $0.954$, and an MCC of $0.908$, matching the accuracy of random forest while offering superior interpretability through its kernel-based structure. In addition, the proposed method yields a sparser model with only 66 support vectors compared to 71 for standard SVC (a reduction of approximately $7\%$), while strictly satisfying the dual constraints with a near-zero violation of $1.3\times {10}^{-3}$. Notably, the optimal hyperparameters identified by OCO-PSO ($\mathrm{C}=2$, $\gamma \approx 0.062$) differ substantially from those obtained via Bayesian optimization for SVC ($\mathrm{C}=10$, $\gamma \approx 0.012$), indicating that the proposed approach explores alternative yet equally effective regions of the hypothesis space. The statistical significance and robustness of these improvements are confirmed through extensive validation using 1000 bootstrap replications, paired Student’s t-tests, Wilcoxon signed-rank tests, and Holm–Bonferroni correction. These results demonstrate that the proposed metaheuristic hybrid optimization framework constitutes a reliable, interpretable, and scalable alternative for training SVMs in complex and high-dimensional classification tasks. Full article