Search Results (3,586)

The phenomenon of arable land non-agriculturalization has become increasingly severe, posing significant threats to the security of arable land resources and ecological sustainability. This study focuses on Dachang Hui Autonomous County in Langfang City, Hebei Province, a region located at the edge of the Beijing–Tianjin–Hebei metropolitan cluster. In recent years, the area has undergone accelerated urbanization and industrial transfer, resulting in drastic land use changes and a pronounced contradiction between arable land protection and the expansion of construction land. The study period is 2016–2023, which covers the key period of the Beijing–Tianjin–Hebei synergistic development strategy and the strengthening of the national arable land protection policy, and is able to comprehensively reflect the dynamic changes of arable land non-agriculturalization under the policy and urbanization process. Multi-temporal Sentinel-2 imagery was utilized to construct a multi-dimensional feature set, and machine learning classifiers were applied to identify arable land non-agriculturalization with optimized performance. GIS-based analysis and the geographic detector model were employed to reveal the spatio-temporal dynamics and driving mechanisms. The results demonstrate that the XGBoost model, optimized using Bayesian parameter tuning, achieved the highest classification accuracy (overall accuracy = 0.94) among the four classifiers, indicating its superior suitability for identifying arable land non-agriculturalization using multi-temporal remote sensing imagery. Spatio-temporal analysis revealed that non-agriculturalization expanded rapidly between 2016 and 2020, followed by a deceleration after 2020, exhibiting a pattern of “rapid growth–slowing down–partial regression”. Further analysis using the geographic detector revealed that socioeconomic factors are the primary drivers of arable land non-agriculturalization in Dachang Hui Autonomous County, while natural factors exerted relatively weaker effects. These findings provide technical support and scientific evidence for dynamic monitoring and policy formulation regarding arable land under urbanization, offering significant theoretical and practical implications. Full article

In modern agriculture, tractors play a crucial role in powering tools and implements. Proper operation of agricultural tractors in mechanized field operations can support sustainable agriculture and reduce emissions of pollutants such as carbon dioxide (CO₂). This has been a recurring concern associated with agricultural intensification for food production. This study aimed to evaluate the optimization of tractor gears and engine speed during crop operations to minimize CO₂ emissions and promote sustainability. The experiment was conducted using a strip plot design with subdivided sections and six replications, following a double factorial structure. The first factor evaluated was the type of agricultural implement (disc harrow, subsoiler, or sprayer), while the second factor was the engine speed setting (nominal or reduced). Operational and energy performance metrics were analyzed, including fuel consumption and CO₂ emissions, travel speed, effective working time, wheel slippage, and working depth. Optimized gear selection and engine speeds resulted in a 20 to 40% reduction in fuel consumption and CO₂ emissions. However, other evaluated parameters remain unaffected by the reduced engine speed, regardless of the implement used, ensuring the operation’s quality. Thus, optimizing operator training or configuring machines allows for environmental impact reduction, making agricultural practices more sustainable. Full article

Accurate and non-destructive monitoring of leaf chlorophyll content (LCC) is essential for assessing crop photosynthetic activity and nitrogen status in precision agriculture. This study introduces a phenology-aware machine learning framework that combines hyperspectral reflectance data with various regression models to estimate leaf chlorophyll content (LCC) in cotton at six key reproductive stages. Field experiments utilized synchronized spectral and SPAD measurements, incorporating spectral transformations—such as vegetation indices (VIs), first-order derivatives, and trilateration edge parameters (TEPs, a new set of geometric metrics for red-edge characterization)—for evaluation. Five regression approaches were evaluated, including univariate and multivariate linear models, along with three machine learning algorithms: Random Forest, K-Nearest Neighbor, and Support Vector Regression. Random Forest consistently outperformed the other models, achieving the highest R² (0.85) and the lowest RMSE (4.1) during the bud stage. Notably, the optimal prediction accuracy was achieved with fewer than five spectral features. The proposed framework demonstrates the potential for scalable, stage-specific monitoring of chlorophyll dynamics and offers valuable insights for large-scale crop management applications. Full article

Floods stand as one of the most harmful natural disasters, which have become more dangerous because of climate change effects on urban structures and agricultural fields. This research presents a comprehensive flood mapping approach that combines multi-sensor satellite data with a machine learning method to evaluate the July 2021 flood in the Netherlands. The research developed 25 different feature scenarios through the combination of Sentinel-1, Landsat-8, and Radarsat-2 imagery data by using backscattering coefficients together with optical Normalized Difference Water Index (NDWI) and Hue, Saturation, and Value (HSV) images and Synthetic Aperture Radar (SAR)-derived Grey Level Co-occurrence Matrix (GLCM) texture features. The Random Forest (RF) classifier was optimized before its application based on two different flood-prone regions, which included Zutphen’s urban area and Heijen’s agricultural land. Results demonstrated that the multi-sensor fusion scenarios (S18, S20, and S25) achieved the highest classification performance, with overall accuracy reaching 96.4% (Kappa = 0.906–0.949) in Zutphen and 87.5% (Kappa = 0.754–0.833) in Heijen. For the flood class F1 scores of all scenarios, they varied from 0.742 to 0.969 in Zutphen and from 0.626 to 0.969 in Heijen. Eventually, the addition of SAR texture metrics enhanced flood boundary identification throughout both urban and agricultural settings. Radarsat-2 provided limited benefits to the overall results, since Sentinel-1 and Landsat-8 data proved more effective despite being freely available. This study demonstrates that using SAR and optical features together with texture information creates a powerful and expandable flood mapping system, and RF classification performs well in diverse landscape settings. Full article

Soil testing has long been used to optimize fertilization and crop production. More recently, soil health testing has emerged to reflect the growing interest in soil multifunctionality and ecosystem services. Soil health encompasses physical, chemical, and biological properties that support ecosystem functions and sustainable agriculture. Despite its relevance to several United Nations Sustainable Development Goals (SDGs 1, 2, 3, 6, 12, 13, and 15), comprehensive soil health testing is not widely practiced due to complexity and cost. The aim of the study presented here was to contribute to the further development, implementation, and testing of an integrated procedure for soil health assessment in practice. We developed and tested a rapid, standardized soil health assessment tool that combines near-infrared spectroscopy (NIRS) and multi-nutrient 0.01 M CaCl₂ extraction with Inductive Coupled Plasma Mass Spectroscopy analysis. The tool evaluates a wide range of soil characteristics with high accuracy (R² ≥ 0.88 for most parameters) and has been evaluated across more than 15 countries, including those in Europe, China, New Zealand, and Vietnam. The results are compiled into a soil health indicator report with tailored management advice and a five-level ABCDE score. In a Dutch test set, 6% of soils scored A (optimal), while 2% scored E (degraded). This scalable tool supports land users, agrifood industries, and policymakers in advancing sustainable soil management and evidence-based environmental policy. Full article

Cucumber, a high-yielding crop commonly grown in facility environments, is particularly susceptible to nitrogen (N) deficiency due to its rapid growth and high nutrient demand. This study used cucumber as its experimental subject and established a spectral dataset of leaves under four nutritional conditions, normal supply, nitrogen deficiency, phosphorus deficiency, and potassium deficiency, aiming to develop an efficient and robust method for quantifying N in cucumber leaves using Raman spectroscopy (RS). Spectral data were preprocessed using three baseline correction methods—BaselineWavelet (BW), Iteratively Improve the Moving Average (IIMA), and Iterative Polynomial Fitting (IPF)—and key spectral variables were selected using 4-Dimensional Feature Extraction (4DFE) and Competitive Adaptive Reweighted Sampling (CARS). These selected features were then used to develop a N content prediction model based on Partial Least Squares Regression (PLSR). The results indicated that baseline correction significantly enhanced model performance, with three methods outperforming unprocessed spectra. A further analysis showed that the combination of IPF, 4DFE, and CARS achieved optimal PLSR model performance, achieving determination coefficients (R²) of 0.947 and 0.847 for the calibration and prediction sets, respectively. The corresponding root mean square errors (RMSEC and RMSEP) were 0.250 and 0.368, while the residual predictive deviation (RPDC and RPDP) values reached 4.335 and 2.555. These findings confirm the feasibility of integrating RS with advanced data processing for rapid, non-destructive nitrogen assessment in cucumber leaves, offering a valuable tool for nutrient monitoring in precision agriculture. Full article

There has been much interest in the mathematical investigation of critical sets and unavoidable sets in Latin Squares, Sudoku, and their applications to practical problems in areas such as agriculture and cryptology. This paper considers the associated structures of Strictly Concentric Magic Squares (SCMSs) and Prime Strictly Concentric Magic Squares (PSCMSs). A framework of formal definitions is given that leads to the definitions of critical sets and unavoidable sets. Minimal critical sets are of interest in Latin Squares, and in this article, the cardinality of minimal critical sets of SCMS is given for all n, n odd. Two families of unavoidable sets are established for SCMS, leading to a complete classification of unavoidable sets of minimum PSCMS of order 5. Full article

Background/Objectives: Chronic kidney disease of unknown etiology (CKDu) disproportionately affects young male agricultural workers who are otherwise healthy. There is a scarcity of biomarkers for early detection of this type of kidney disease. We hypothesized that small extracellular vesicles (sEVs) released into urine may provide novel biomarkers. Methods: We obtained two urine samples at the start and the end of a workday in the fields from a limited set of workers with and without kidney impairment. Isolated sEVs were characterized for size, surface marker expression, and purity and, subsequently, their lipid composition was determined by mass spectrometry. Results: The number of particles per ml of urine normalized to osmolality and the size variance were larger in workers with possible CKDu than in control workers. Surface markers CD9, CD63, and CD81 are characteristic of sEVs and a second set of surface markers suggested the kidney as the origin. Differential expression of CD25 and CD45 suggested early inflammation in CKDu workers. Of the twenty-one lipids differentially expressed, several were bioactive, suggesting that they may have essential functions. Remarkably, fourteen of the lipids showed intermediate expression values in sEVs from healthy individuals with acute creatinine increases after a day of work. Conclusions: We identified twenty-one possible lipid biomarkers in sEVs isolated from urine that may be able to distinguish agricultural workers with early onset of CKDu. Differentially expressed surface proteins in these sEVs suggested early-stage inflammation. This pilot study was limited in the number of workers evaluated, but the approach should be further evaluated in a larger population. Full article

This study presents a comprehensive review and comparative analysis of traditional machine learning (ML) and deep learning (DL) models for land cover classification in agricultural remote sensing. We evaluate the reported successes, trade-offs, and performance metrics of ML and DL models across diverse agricultural contexts. Building on this foundation, we apply both model types to the specific case of almond crop field identification in California’s Central Valley using Landsat data. DL models, including U-Net, MANet, and DeepLabv3+, achieve high accuracy rates of 97.3% to 97.5%, yet our findings demonstrate that conventional ML models—such as Decision Tree, K-Nearest Neighbor, and Random Forest—can reach comparable accuracies of 96.6% to 96.8%. Importantly, the ML models were developed using data from a single year, while DL models required extensive training data spanning 2008 to 2022. Our results highlight that traditional ML models offer robust classification performance with substantially lower computational demands, making them especially valuable in resource-constrained settings. This paper underscores the need for a balanced approach in model selection—one that weighs accuracy alongside efficiency. The findings contribute actionable insights for agricultural land cover mapping and inform ongoing model development in the geospatial sciences. Full article

Wireless sensor networks (WSNs) have attracted significant attention due to their widespread applications in various fields such as environmental monitoring, agriculture, intelligent transportation, and healthcare. In these networks, the power cost of a sensor node is closely related to the radius of its coverage area, following a nonlinear relationship where power increases as the coverage radius grows according to an attenuation factor. This means that increasing the coverage radius of a sensor leads to a corresponding increase in its power cost. Consequently, minimizing the total power cost of the network while all clients are served has become a crucial research topic. The power cover problem focuses on adjusting the power levels of sensors to serve all clients while minimizing the total power cost. This survey focuses on the power cover problem and its related variants in WSNs. Specifically, it introduces nonlinear integer programming formulations for the power cover problem and its related variants, all within the specified sensor setting. It also provides a comprehensive overview of the power cover problem and its variants under both specified and unspecified sensor settings, summarizes existing results and approximation algorithms, and outlines potential directions for future research. Full article

Seafood contamination by heavy metals is a growing public health concern, particularly in regions like Tunisia where seafood is a major dietary component. This study assessed concentrations of cadmium (Cd), copper (Cu), lead (Pb), and zinc (Zn) in the muscle tissue of the red shrimp Parapenaeus longirostris, collected in 2023 from four coastal regions: Bizerte, Monastir, Kerkennah, and Gabes. Metal analysis was conducted using flame atomic absorption spectroscopy. This species was chosen due to its ecological and economic importance. The study sites were chosen based on their differing levels of industrial, urban, and agricultural influence, providing a representative overview of regional contamination patterns. Mean concentrations were 1.04 µg/g for Zn, 0.59 µg/g for Cu, 1.56 µg/g for Pb, and 0.21 µg/g for Cd (dry weight). Pb was the most prevalent metal across sites. Statistically significant variation was observed only for Cu (p = 0.0334). All metal concentrations were below international safety limits set by FAO/WHO and the European Union. Compared to similar studies, the levels reported were similar or slightly lower. Human health risk was evaluated using target hazard quotient (THQ), hazard index (HI), and cancer risk (CR) values. For adults, THQ ranged from 5.44 × 10⁻⁶ to 8.43 × 10⁻⁴, while for children it ranged from 2.40 × 10⁻⁵ to 3.72 × 10⁻³. HI values were also well below 1, indicating negligible non-carcinogenic risk. CR values for Cd and Pb in both adults and children fell within the acceptable risk range (10⁻⁶ to <10⁻⁴), suggesting no significant carcinogenic concern. This study provides the first field-based dataset on metal contamination in P. longirostris from Tunisia, contributing valuable insights for seafood safety monitoring and public health protection. Full article

Indoor horticulture requires a substantial quantity of electricity to meet crops extended photoperiodic requirements for optimal photosynthetic rate. Simultaneously, global electricity costs have grown dramatically in recent years, endangering the sustainability and profitability of indoor vertical farms and/or modern greenhouses that use artificial lighting systems to accelerate crop development and growth. This study investigates the growth rate and physiological development of cherry tomato plants cultivated in a pilot indoor vertical farm at the Agricultural University of Athens’ Laboratory of Farm Structures (AUA) under continuous and disruptive lighting. The leaf physiological traits from multiple photoperiodic stress treatments were analyzed and utilized to estimate the plant’s tolerance rate under varied illumination conditions. Four different photoperiodic treatments were examined and compared, firstly plants grew under 14 h of continuous light (C-14L10D/control), secondly plants grew under a normalized photoperiod of 14 h with intermittent light intervals of 10 min of light followed by 50 min of dark (NI-14L10D/stress), the third treatment where plants grew under 14 h of a load-shifted energy demand response intermittent lighting schedule (LSI-14L10D/stress) and finally plants grew under 13 h photoperiod following of a load-shifted energy demand response intermittent lighting schedule (LSI-13L11D/stress). Plants were subjected also under two different light spectra for all the treatments, specifically WHITE and Blue/Red/Far-red light composition. The aim was to develop flexible, energy-efficient lighting protocols that maintain crop productivity while reducing electricity consumption in indoor settings. Results indicated that short periods of disruptive light did not negatively impact physiological responses, and plants exhibited tolerance to abiotic stress induced by intermittent lighting. Post-harvest data indicated that intermittent lighting regimes maintained or enhanced growth compared to continuous lighting, with spectral composition further influencing productivity. Plants under LSI-14L10D and B/R/FR spectra produced up to 93 g fresh fruit per plant and 30.4 g dry mass, while consuming up to 16 kWh less energy than continuous lighting—highlighting the potential of flexible lighting strategies for improved energy-use efficiency. Full article

Computer vision is of wide interest to perform the phenotyping of horticultural crops such as apple trees at high throughput. In orchards specially constructed for variety testing or breeding programs, computer vision tools should be able to extract phenotypical information form each tree separately. We focus on segmenting individual apple trees as the main task in this context. Segmenting individual apple trees in dense orchard rows is challenging because of the complexity of outdoor illumination and intertwined branches. Traditional methods rely on supervised learning, which requires a large amount of annotated data. In this study, we explore an alternative approach using prompt engineering with the Segment Anything Model and its variants in a zero-shot setting. Specifically, we first detect the trunk and then position a prompt (five points in a diamond shape) located above the detected trunk to feed to the Segment Anything Model. We evaluate our method on the apple REFPOP, a new large-scale European apple tree dataset and on another publicly available dataset. On these datasets, our trunk detector, which utilizes a trained YOLOv11 model, achieves a good detection rate of $97\%$ based on the prompt located above the detected trunk, achieving a Dice score of 70% without training on the REFPOP dataset and 84% without training on the publicly available dataset.We demonstrate that our method equals or even outperforms purely supervised segmentation approaches or non-prompted foundation models. These results underscore the potential of foundational models guided by well-designed prompts as scalable and annotation-efficient solutions for plant segmentation in complex agricultural environments. Full article

Early crop yield prediction is a major challenge in precision agriculture, and efficient and rapid yield prediction is highly important for sustainable fruit production. The accurate detection of major fruit characteristics, including flowering, green fruiting, and ripening stages, is crucial for early yield estimation. Currently, most crop yield estimation studies based on the YOLO model are only conducted during a single stage of maturity. Combining multi-growth period data for crop analysis is of great significance for crop growth detection and early yield estimation. In this study, a new network model, YOLOv8-RL, was proposed using citrus multigrowth period characteristics as a data source. A citrus yield estimation model was constructed and validated by combining network identification counts with manual field counts. Compared with YOLOv8, the number of parameters of the improved network is reduced by 50.7%, the number of floating-point operations is decreased by 49.4%, and the size of the model is only 3.2 MB. In the test set, the average recognition rate of citrus flowers, green fruits, and orange fruits was 95.6%, the mAP@.5 was 94.6%, the FPS value was 123.1, and the inference time was only 2.3 milliseconds. This provides a reference for the design of lightweight networks and offers the possibility of deployment on embedded devices with limited computational resources. The two estimation models constructed on the basis of the new network had coefficients of determination R² values of 0.91992 and 0.95639, respectively, with a prediction error rate of 6.96% for citrus green fruits and an average error rate of 3.71% for orange fruits. Compared with network counting, the yield estimation model had a low error rate and high accuracy, which provided a theoretical basis and technical support for the early prediction of fruit yield in complex environments. Full article

Olive mill wastewaters (OMWWs) are an environmental problem in the Mediterranean region, and it is crucial to explore strategies for their treatment and repurposing. The chemical precipitation technique (CPT) has been presented as a cost-effective wastewater treatment solution that might be applied to OMWW. The CPT-resulting precipitant subproducts (sludge) may be reprocessed (e.g., agricultural fertilizer and/or soil amendment), while the treated wastewater may be repurposed or reused (e.g., irrigation, aquaponic, or industrial processes). This study aimed to evaluate the efficacy of CPT in treating wastewater from the olive oil industry from an ecotoxicological perspective. Additionally, to assess the safe use of the obtained sludge in CPT treatment, its effects on soil biota were assessed. For this, a set of ecotoxicological assays using freshwater (Raphidocelis subcapitata, Daphnia magna and Danio rerio), terrestrial invertebrates (Folsomia candida and Enchytraeus crypticus), and plants (Brassica oleracea and Lolium perenne) were used as model organisms. Results demonstrated that CPT reduced OMWW toxicity to freshwater organisms, offering a favorable outlook on CPT’s potential as a wastewater treatment method. Increasing application rates of sludge in soil reduced the shoot biomass and the hydric content of both plants compared to the control. Survival of F. candida and E. crypticus was not affected by sludge in soil at any tested application rate, yet sludge application negatively affected the reproduction of both species, even at relevant sludge application rates (2%) of sludge in soils. Overall, the applicability of this sludge obtained by the CPT treatment in soils should be carefully evaluated due to the observed adverse effects on soil biota. Although the results of CPT were promising in reducing the toxicity of OMWW for these aquatic species, some adjustments/improvements should be performed to improve this technique and use all the obtained resources (treated water and sludge) in a fully circular perspective. Full article