Search Results (3,223)

Heat and high-irradiance stress increasingly threaten almond production in Mediterranean environments, where rising temperatures and prolonged summer droughts impair photosynthetic performance and yield. This study evaluated the effectiveness of three mineral-based shielding materials: kaolin, basalt powder, and zeolite. We hypothesized that the foliar application of reflective mineral materials would reduce leaf temperature, enhance photosynthetic efficiency, and improve yield without altering nut nutraceutical quality. A two-year field experiment (2024–2025) was conducted using a randomized block design with four materials (untreated control, kaolin, basalt powder, and zeolite). Physiological traits (gas exchange, chlorophyll fluorescence, leaf temperature, and SPAD index), morpho-biometric and biochemical parameters, and yield components were assessed. Kaolin and basalt powder significantly lowered leaf temperature (−1.6 to −1.8 °C), increased stomatal conductance and net photosynthesis, and improved photochemical efficiency (Fv′/Fm′) and electron transport rates. These treatments also enhanced drupe weight, kernel dry matter, and productive yield (up to +32% compared with the control). Zeolite produced positive but less prominent effects. No significant differences were detected in fatty acid profile, total polyphenols, or antioxidant capacity, indicating that the materials did not affect almond nutraceutical quality. Principal component analysis confirmed the strong association between kaolin and basalt powder and improved eco-physiological performance. Overall, mineral shielding materials, particularly kaolin and basalt powder, represent a promising, sustainable strategy for enhancing almond orchard resilience under Mediterranean climate change scenarios. Full article

Orchard crops generate substantial quantities of diverse biomass each year, with grapevines being among the most economically significant species worldwide. Considering the scale of this biomass, there is a growing need to explore rational strategies for its utilisation, for example, for energy production or other value-added applications. Such approaches may contribute to improving resource efficiency and reducing the environmental burden associated with agricultural waste. The aim of this study was to examine the energy potential of woody post-production waste from wine processing, with particular emphasis on grape stems of four cultivars—Chardonnay, Riesling, Merlot, and Zweigelt—grown in two contrasting climatic regions: south-eastern Poland and Moravia (Czech Republic). The results demonstrated that both the grape variety and cultivation site significantly influenced the majority of bunch biometric traits, including bunch and berry weight, berry number, and stem dimensions. A moderately warm climate promoted the development of larger and heavier bunches as well as more robust stems across all examined cultivars. Energy analyses indicated that Zweigelt stems produced under moderately warm conditions and Chardonnay stems from a temperate climate exhibited the most favourable combustion properties. Nonetheless, certain constraints were identified, such as increased ash (12.20%) and moisture content (11.51%) in Chardonnay grown in warmer conditions, and elevated CO and CO₂ emissions observed for Zweigelt (1333.26 kg·mg⁻¹). Overall, the findings confirm that grape stems constitute a promising local source of bioenergy, with their energy performance determined predominantly by varietal characteristics and climatic factors. Their utilisation aligns with circular-economy principles and may help reduce the environmental impacts associated with traditional viticultural waste management. Full article

Fruit growing is a traditional component of Bulgarian agricultural production. According to the latest statistical data, the share of areas planted with cherries is 10.5% of the total orchard area, and with apples, 7.2%, totaling 67,800 ha. This article presents the results of ground and remote (satellite) measurements and observations of cherry and apple orchards, along with the methods for their processing and interpretation, to define the current state and forecast their expected development. This research aims to combine the capabilities of the two approaches by improving and expanding observation and forecasting activities. Ground-based measurements and observations consider the dates of a permanent transition in air temperature above 5 °C and several cardinal phenological stages, based on the idea that a certain temperature sum (CU, GDH, GDD) must accumulate to move from one phenological stage to another. The obtained data were statistically analyzed, and by means of classification with the Random Forest algorithm, the dates for the occurrence of the stages of bud break, flowering, and fruit ripening in the development of cherry and apple orchards were predicted with an accuracy of −6 to +2 days. Satellite studies include creating a database of Sentinel-2 digital images across different spectral bands for the studied orchards, investigating various post-processing approaches, and deriving indicators of developmental phenostages. Ground data from the 2021–2023 experiment in Kyustendil and Plovdiv were used to determine the phases of fruit bursting, flowering, and ripening through satellite images. An assessment of the two approaches to predicting the development of the accuracy of the models was carried out by comparing their predictions for bud swelling and bursting (BBCH 57), flowering (BBCH 65), and fruit ripening (BBCH 87/89) of the observed phenological events in the two selected orchard types, representatives of stone and pome fruit species. Full article

To investigate the impacts of the continuous application of urban sewage sludge on heavy metal pollution risks in wine grape orchards, this study conducted a five-year field plot experiment using wine grapes as the test crop. The experimental design included three sludge application rates and a control without sludge application. Soil physicochemical properties, the single-factor and integrated pollution indices (PI and NIPI) of heavy metals, potential ecological risk indices (EI and RI), and the safe application duration of sludge were analyzed. The results suggest that sludge application significantly increased soil organic matter, total nitrogen, total phosphorus, and available phosphorus by 39.99–46.56%, 59.37–73.69%, 83.57–143.19%, and 88.79%, respectively, while reducing soil bulk density by 8.70–27.92%. The PI and EI of Cd exhibited significant linear increases with the duration of sludge application, with annual increments of 0.010 and 0.31, respectively. Hg was influenced by both the application rates and duration, with annual increments of 0.013 and 0.52 for the PI and EI, respectively. These two elements collectively drove overall increases of 7.31–24.96% in NIPI and 32.51–59.90% in RI, with mean annual increases of 0.0064 and 0.84, respectively. In contrast, Cr, Pb, and As showed no significant changes. Based on the calculated environmental capacities of Cd and Hg, the safe application durations were estimated to be 46.99–126.93 and 48.58–131.21 years, respectively. These results demonstrate that under the current application intensity, sludge can improve soil fertility in the short term with controllable ecological risks. However, considering their potential environmental risks, the continuous accumulation of Cd and Hg necessitates vigilance. Full article

Hilly and mountainous areas are important agricultural production regions globally. Their dramatic topography, dense fruit tree planting, and steep slopes severely restrict the application of traditional plant protection machinery. Pest and disease control has long relied on manual spraying, resulting in high labor intensity, low efficiency, and pesticide utilization rates of less than 30%. Plant protection UAVs, with their advantages of flexibility, high efficiency, and precise application, provide a feasible technical approach for plant protection operations in hilly and mountainous areas. However, steep slopes and dense orchard environments place higher demands on key technologies such as drone positioning and navigation, attitude control, trajectory planning, and terrain following. Achieving accurate identification and adaptive following of the undulating fruit tree canopy while maintaining a constant spraying distance to ensure uniform pesticide coverage has become a core technological bottleneck. This paper systematically reviews the key technologies and research progress of plant protection UAVs in hilly and mountainous operations, focusing on the principles, advantages, and limitations of core methods such as multi-sensor fusion positioning, intelligent SLAM navigation, nonlinear attitude control and intelligent control, three-dimensional trajectory planning, and multimodal terrain following. It also discusses the challenges currently faced by these technologies in practical applications. Finally, this paper discusses and envisions the future of plant protection UAVs in achieving intelligent, collaborative, and precise operations on steep slopes and in dense orchards, providing theoretical reference and technical support for promoting the mechanization and intelligentization of mountain agriculture. Full article

This study is Part II of a five-year (2018–2022) field trial in western Portugal evaluating the effects of three microbial biofertilizers—Mycoshell^® (Glomus spp. + humic/fulvic acids), Kiplant iNmass^® (Azospirillum brasilense, Bacillus megaterium, Saccharomyces cerevisiae), and Kiplant All-Grip^® (Bacillus megaterium, Pseudomonas spp.)—applied at different dosages alongside two mineral fertilizer regimes, T100 (full dose) and T70 (70% of T100, alone or combined with biofertilizers), on the physiological performance of ‘Gala Redlum’ apple trees. Part I had shown that Myc4 (Mycoshell^®, 4 tablets/tree), iNM6, and iNM12 (Kiplant iNmass^®, 6 and L ha⁻¹, respectively) consistently enhanced fruit growth, yield, and selected quality traits. While Part I showed clear agronomic gains, Part II demonstrates that these improvements occurred without significant alterations in seasonal photosynthetic performance, canopy reflectance, or chlorophyll fluorescence parameters over five years, highlighting the contrast between observed yield improvements and physiological stability. Seasonal monitoring of physiological traits—including specific leaf area (SLA), chlorophyll content index (CCI), gas exchange (A_n, g_s, E, C_i), spectral indices (NDVI, OSAVI, SIPI, GM2), and chlorophyll fluorescence (OJIP). It is clear that physiological values remained largely stable across biofertilizer treatments and years. Importantly, this stability was maintained even under a 30% reduction in mineral fertilizer (T70), indicating that specific microbial biofertilizers can sustain physiological resilience under reduced nutrient inputs, thereby providing a physiological basis for the yield-enhancing effects observed and supporting their integration into fertilizer reduction strategies in Mediterranean orchards. Full article

The Soil and Plant Analyzer Development (SPAD) value is a reliable proxy for chlorophyll, yet conventional field measurement remains labor-intensive and spatially limited. Current remote sensing inversion models typically depend on costly multispectral sensors and rarely account for phenological changes, restricting their applicability across orchards and seasons. To overcome these limitations, this study introduces a stage-aware and low-cost SPAD inversion framework for jujube trees, integrating multi-source data fusion and an optimized ensemble model. A two-year experiment (2023–2024) combined UAV multispectral vegetation indices (VI) with RGB-derived color indices (CI) across leaf expansion, flowering, and fruit-setting stages. Rather than using static features, stage-specific predictors were systematically identified through a hybrid selection mechanism combining Random Forest Cumulative Feature Importance (RF-CFI), Recursive Feature Elimination (RFE), and F-tests. Building on these tailored features, XGBoost, decision tree (DT), CatBoost, and an Optimized Integrated Architecture (OIA) were developed, with all hyperparameters globally tuned using a genetic algorithm (GA). The RFI-CFI-OIA-GA model delivered superior accuracy (R² = 0.758–0.828; MSE = 0.214–2.593; MAPE = 0.01–0.045 in 2024) in the training dataset, and robust cross-year transferability (R² = 0.541–0.608; MSE = 0.698–5.139; MAPE = 0.015–0.058 in 2023). These results demonstrate that incorporating phenological perception into multi-source data fusion substantially reduces interference and enhances generalizability, providing a scalable and reusable strategy for precision orchard management and spatiotemporal SPAD mapping. Full article

To address the challenges in unstructured orchard environments, including high geometric similarity between fruit trees (with the measured average Euclidean distance difference between point cloud descriptors of adjacent trees being less than 0.5 m), significant dynamic interference (e.g., interference from pedestrians or moving equipment can occur every 5 min), and uneven terrain, this paper proposes an improved mapping algorithm named OSC-LIO (Orchard Scan Context Lidar Inertial Odometry via Smoothing and Mapping). The algorithm designs a dynamic point filtering strategy based on Euclidean clustering and spatiotemporal consistency within a 5-frame sliding window to reduce the interference of dynamic objects in point cloud registration. By integrating local semantic features such as fruit tree trunk diameter and canopy height difference, a two-tier verification mechanism combining “global and local information” is constructed to enhance the distinctiveness and robustness of loop closure detection. Motion compensation is achieved by fusing data from an Inertial Measurement Unit (IMU) and a wheel odometer to correct point cloud distortion. A three-level hierarchical indexing structure—”path partitioning, time window, KD-Tree (K-Dimension Tree)”—is built to reduce the time required for loop closure retrieval and improve the system’s real-time performance. Experimental results show that the improved OSC-LIO system reduces the Absolute Trajectory Error (ATE) by approximately 23.5% compared to the original LIO-SAM (Tightly coupled Lidar Inertial Odometry via Smoothing and Mapping) in a simulated orchard environment, while enabling stable and reliable path planning and autonomous navigation. This study provides a high-precision, lightweight technical solution for autonomous navigation in orchard scenarios. Full article

In response to the urgent need for efficient, precise and low-cost monitoring of Huanglongbing in large-scale citrus orchards, this study explored a two-step technical framework of “wide-area screening to precise diagnosis” for the aerial-ground collaboration. In the wide-area screening stage of the drone, a lightweight YOLOv8-SNNL new model was constructed, which significantly reduced the number of parameters while enhancing the ability to capture subtle symptoms of the canopy. This model achieved an accuracy rate of 90.6%, a recall rate of 93.3%, and an mAP50 of 96.6% on the independent test set, enabling efficient and reliable positioning of suspected diseased plants. Then, the ground operators reached the corresponding locations based on the geographic coordinate position information output by the drone. Using the constructed SRSA-YOLO-World model for ground precise diagnosis, the constructed model achieved an identification accuracy of 99.5% for diseased leaves in complex field environments. Based on the positioning and diagnosis results output by both models, managers were provided with a decision-making basis. The aerial-ground collaboration strategy integrates the efficiency of drone “wide-area screening” and the precision of ground equipment “precise diagnosis”, providing a new solution for replacing the traditional manual inspection mode and achieving precise prevention and control in large-scale orchards. Full article

Aiming at the problem that the current traditional apple yield estimation methods rely on manual investigation and do not make full use of multi-source information, this paper proposes an apple orchard yield prediction framework combining multispectral remote sensing features and ground features. The framework is oriented to the demand of yield prediction at different scales. It can not only realize the prediction of apple yield at the district and county scales, but also modify the prediction results of small-scale orchards based on the acquisition of orchard features. The framework consists of three parts, namely, apple orchard planting area extraction, district and county large-scale yield prediction and small-scale orchard yield prediction correction. (1) During apple orchard planting area extraction, the samples of some apple planting areas in the study area were obtained through field investigation, and the orchard and non-orchard areas were classified and discriminated, providing a spatial basis for the collection of subsequent yield prediction-related data. (2) In the large-scale yield prediction of districts and counties, based on the obtained orchard-planting areas, the corresponding multispectral remote sensing features and environmental features were obtained using Google Earth engine platform. In order to avoid the noise interference caused by local pixel differences, the obtained data were median synthesized, and the feature set was constructed by combining the yield and other information. On this basis, the feature set was divided and sent to Apple Orchard Yield Prediction Network (APYieldNet) for training and testing, and the district and county large-scale yield prediction model was obtained. (3) During the part of small-scale orchard yield prediction correction, the optimal model for large-scale yield prediction at the district and county levels is utilized to forecast the yield of the entire planting area and the internal local sampling areas of the small-scale orchard. Within the local sampling areas, the number of fruits is identified through the YOLO-A model, and the actual yield is estimated based on the empirical single fruit weight as a ground feature, which is used to calculate the correction factor. Finally, the proportional correction method is employed to correct the error in the prediction results of the entire small-scale orchard area, thus obtaining a more accurate yield prediction for the small-scale orchard. The experiment showed that (1) the yield prediction model APYieldNet (MAE = 152.68 kg/mu, RMSE = 203.92 kg/mu) proposed in this paper achieved better results than other methods; (2) the proposed YOLO-A model achieves superior detection performance for apple fruits and flowers in complex orchard environments compared to existing methods; (3) in this paper, through the method of proportional correction, the prediction results of APYieldNet for small-scale orchard are closer to the real yield. Full article

This study presents a field-oriented AI web system for durian orchard management that recognizes leaf health from on-orchard images under variable illumination. Two complementary pipelines are employed: (1) a rule-based module operating in HSV and CIE Lab color spaces that suppresses sun-induced specular highlights via V/L* thresholds and applies interpretable hue–chromaticity rules with spatial constraints; and (2) a Deep Feature (PCA–SVM) pipeline that extracts features from pretrained ResNet50 and DenseNet201 models, performs dimensionality reduction using Principal Component Analysis, and classifies samples into three agronomic classes: healthy, leaf-spot, and leaf-blight. This hybrid architecture enhances transparency for growers while remaining robust to illumination variations and background clutter typical of on-farm imaging. Preliminary on-farm experiments under real-world field conditions achieved approximately 80% classification accuracy, whereas controlled evaluations using curated test sets showed substantially higher performance for the Deep Features and Ensemble model, with accuracy reaching 0.97–0.99. The web interface supports near-real-time image uploads, annotated visual overlays, and Thai-language outputs. Usability testing with thirty participants indicated very high satisfaction (mean 4.83, SD 0.34). The proposed system serves as both an instructional demonstrator for explainable AI-based image analysis and a practical decision-support tool for digital horticultural monitoring. Full article

Soil contamination by heavy metals (HMs) threatens crop productivity, food safety, and ecosystem health, especially in intensively cultivated Mediterranean regions. This study investigated the influence of soil HM contamination on nutrient uptake, photosynthetic traits, and metal bioaccumulation in avocado (Persea americana Mill.) orchards. Twenty orchard sites were sampled, collecting paired soil and mature leaf samples. Soil physicochemical properties and HM concentrations were determined, while leaves were analyzed for macro- and micronutrients, photosynthetic pigments, and metal contents. Bioaccumulation Factors (BAFs) were computed, and multivariate analyses (Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), Linear Discriminant Analysis (LDA), and Partial Least Squares Regression (PLSR)) were applied to assess soil–plant relationships, complemented by Monte Carlo simulations to quantify probabilistic contamination risks. Results revealed substantial inter-site variability, with leaf Cd and Pb concentrations reaching 0.92 and 3.54 mg/kg, and BAF values exceeding 1 in several orchards. PLSR models effectively predicted leaf Cd (R² = 0.789) and Pb (R² = 0.772) from soil parameters. Monte Carlo simulations indicated 15–25% exceedance of FAO/WHO safety limits for Cd and Pb. These findings demonstrate that soil metal accumulation substantially alters avocado nutrient balance and photosynthetic efficiency, highlighting the urgent need for site-specific soil monitoring and sustainable remediation strategies in contaminated orchards. Full article

Polystigma amygdalinum the causal agent of Red Leaf Blotch (RLB), is responsible for one of the most important foliar diseases affecting almond [Prunus dulcis (Miller) D.A. Webb] in the Mediterranean Basin and the Middle East. The study is aimed at improving knowledge on RLB epidemiology and the role of environmental conditions in disease development. Field monitoring was conducted from 2022 to 2025 in three almond orchards located in Apulia (southern Italy) and characterized by different microclimatic conditions. A total of 39 cultivars, including Apulian local germplasm and international cultivars (‘Belona’, ‘Genco’, ‘Guara’, ‘Ferragnès’, ‘Filippo Ceo’, ‘Lauranne^® Avijor’, ‘Soleta’, and ‘Supernova’), were evaluated. Symptoms occurred from late spring to summer, resulting particularly severe on ‘Guara’ and ‘Lauranne^® Avijor’, whereas ‘Belona’, ‘Ferragnès’, ‘Genco’, and ‘Supernova’ exhibited the highest tolerance. To our knowledge, this is also the first report of RLB tolerance by ‘Filippo Ceo’, ‘Ficarazza’, ‘Centopezze’, and ‘Rachele piccola’ representing potential genetic resources for breeding programs. Moreover, these findings reinforced previous observations proving that RLB was less severe on medium-late and late cultivars. Disease incidence varied significantly among sites and years and was strongly associated with increased rainfall, higher relative humidity, and mild temperatures recorded in November, influencing disease occurrence in the following growing season. P. amygdalinum was consistently detected by qPCR in all RLB-affected tissues and, in some cases, from mixed early RLB + Pseudomonas-like symptoms. From some leaves with early RLB symptoms, P. amygdalinum was also successfully isolated in pure culture. Overall, our results provide clear evidence that P. amygdalinum is the sole fungal pathogen consistently associated with typical RLB symptoms in Apulia (southern Italy) and highlight important cultivar-dependent differences. Its frequent molecular detection in leaves showing atypical or mixed symptoms suggests unresolved epidemiological aspects requiring further investigation. Full article

Cross-ecosystem transmission of plant pathogens from crops to natural forests is increasingly recognized as a key factor in disease emergence and biodiversity loss. Agricultural systems serve as major sources of inoculum, with landscape interfaces—such as crop–forest edges, riparian zones, abandoned orchards, and nursery–wildland transitions—acting as active epidemiological gateways. Biological vectors, abiotic dispersal, and human activities collectively enable pathogen movement across these boundaries. Host-range expansion, recombination, and hybridization allow pathogens to infect both cultivated and wild hosts, leading to generalist and recombinant lineages that survive across diverse habitats. In natural ecosystems, such introductions can alter community composition, decrease resilience, and intensify the impacts of climate-driven stress. Advances in molecular diagnostics, genomic surveillance, environmental DNA, and remote sensing–GIS (Geographic Information System) approaches now enable high-resolution detection of pathogen flow across landscapes. Incorporating these tools into interface-focused monitoring frameworks offers a pathway to earlier detection, better risk assessment, and more effective mitigation. A One Health, landscape-based approach that treats agro–wild interfaces as key control points is essential for reducing spillover risk and safeguarding both agricultural productivity and the health of natural forest ecosystems. Full article

To address the challenging requirements of citrus detection in complex orchard environments, this paper proposes ACDNet (Adaptive Citrus Detection Network), a novel deep learning framework specifically designed for automated citrus harvesting. The proposed method introduces three key innovations: (1) Citrus-Adaptive Feature Extraction (CAFE) module that combines fruit-aware partial convolution with illumination-adaptive attention mechanisms to enhance feature representation with improved efficiency; (2) Dynamic Multi-Scale Sampling (DMS) operator that adaptively focuses sampling points on fruit regions while suppressing background interference through content-aware offset generation; and (3) Fruit-Shape Aware IoU (FSA-IoU) loss function that incorporates citrus morphological priors and occlusion patterns to improve localization accuracy. Extensive experiments on our newly constructed CitrusSet dataset, which comprises 2887 images capturing diverse lighting conditions, occlusion levels, and fruit overlapping scenarios, demonstrate that ACDNet achieves superior performance with mAP@0.5 of 97.5%, precision of 92.1%, and recall of 92.8%, while maintaining real-time inference at 55.6 FPS. Compared to the baseline YOLOv8n model, ACDNet achieves improvements of 1.7%, 3.4%, and 3.6% in mAP@0.5, precision, and recall, respectively, while reducing model parameters by 11% (to 2.67 M) and computational cost by 20% (to 6.5 G FLOPs), making it highly suitable for deployment in resource-constrained robotic harvesting systems. However, the current study is primarily validated on citrus fruits, and future work will focus on extending ACDNet to other spherical fruits and exploring its generalization under extreme weather conditions. Full article