Search Results (265)

Mechanical pruning has emerged as a viable alternative to traditional hand pruning in apple orchards in labor-constrained and aging population workforces. While mechanical pruning reduces labor demand and enhances operational efficiency, their effects on tree physiology and fruit development remain poorly understood. In this study, we examined the physiological and transcriptional responses of apple trees to mechanical pruning (MP) and hand pruning (HP), with a focus on hormone metabolism, photosynthetic activity, and stress adaptation. Pruning treatments were applied in an orchard using a tractor-mounted mechanical pruner and manual shears, and distinct metabolic responses after pruning were assessed over multiple time points using transcriptomic analysis. At 168 h after MP, trees exhibited downregulation of MdLhcb genes, indicating a reduction in light harvesting capacity. In addition, MdDFR, a key gene in flavonoid biosynthesis, was also downregulated, suggesting a suppression of secondary metabolism and a distinct physiological response to MP. In addition, stress-responsive genes such as MdNHL3 were rather upregulated, indicating the activation of adaptive signaling networks. Conversely, HP trees showed relatively moderate responses in the same pathways, suggesting pruning method-specific regulatory mechanisms. These findings highlight how pruning methods distinctly influence tree recovery and gene expression dynamics, offering insights into optimizing pruning systems for sustainable and high-quality apple production under labor-constrained conditions. Full article

To address the challenge of nondestructively identifying watercore disease in apples during growth and maturation, a portable device was developed for real-time grading of apple watercore using visible/near-infrared (Vis/NIR) spectroscopy combined with a one-dimensional quadratic convolutional neural network (1DQCNN). The instrument enables rapid, nondestructive, and accurate detection of apple watercore grades. The AI-OX2000-13 micro-spectrometer is used as the core data acquisition unit, and an ARM processing system is built with the STM32F103VET6 as the main control chip. A 4G wireless communication module enables efficient and stable data transmission between the processor and computer, meeting the real-time detection needs of apple watercore content in orchard environments. To improve the scientific and accurate classification of watercore grades, this paper combines the BiSeNet and RIFE algorithms to construct a 3D model of apple watercore, allowing quantification of the degree of watercore and classification into four levels. Based on this, quadratic convolution operations are incorporated into a one-dimensional convolutional neural network (1DCNN), leading to the development of the 1D quadratic convolutional neural network (1DQCNN) model for watercore grade classification. Experimental results indicate that the model achieves a classification accuracy of 98.05%, outperforming traditional methods and conventional CNN models. The designed portable instrument demonstrates excellent accuracy and practicality in real-world applications. Full article

Global warming has increasingly reduced winter chill accumulation in traditional fruit-growing regions, disrupting dormancy release and bloom synchrony in deciduous fruit crops such as peach (Prunus persica). To evaluate adaptation potential under subtropical conditions, a three-year field study was conducted in central Taiwan using two low-chill cultivars, ‘Tainung No.4 Ruby’ (~100 chilling units, CU) and ‘Tainung No. 7 HongLing’ (~77 CU). Our results demonstrate that both cultivars produced long shoots (>34 nodes), completed vegetative growth by October, and reached natural leaf fall by mid-November. Nonlinear Gompertz and Logistic models accurately described shoot elongation dynamics and growth cessation. Flowering began in mid-January for ‘Tainung No. 7 HongLing’ and mid-February for ‘Tainung No. 4 Ruby’. Seasonal chill accumulation strongly influenced the onset of flower budbreak between apical and basal buds: in the milder 2023–2024 winter (~120 CU), apical–basal onset lags were wider (22 days in ‘Tainung No. 7 HongLing’), whereas in the colder 2024–2025 winter (~280 CU), these lags shortened (14 days). Notably, ‘Tainung No. 4 Ruby’ maintained a consistent apical–basal onset lag between seasons, indicating greater positional stability under variable chilling. Field-estimated CU thresholds for flower budbreak exceeded the reported chilling requirements, suggesting reduced chilling efficiency under fluctuating subtropical winter temperatures. These results demonstrate that integrating shoot growth, leaf fall timing, and chill–heat accumulation provides a phenology-informed framework for cultivar selection and orchard scheduling, thereby enhancing climate resilience of peach production in warm-winter regions. Full article

Chestnut (Castanea sativa Mill.) cultivation holds significant ecological and economic importance in Greece and other Mediterranean regions, where it represents a traditional crop with growing commercial demand in mountainous areas. Irrigation is critical for maintaining orchard productivity, especially under Mediterranean conditions where present climate conditions intensify heat stress and late-summer drought. In this study, the effects of different irrigation regimes—full irrigation (FI), deficit irrigation (DI), and no irrigation (NI)—were evaluated over two consecutive years (2017–2018) in an intensively managed chestnut orchard in Greece. FI enhanced fruit yield, nut size, and edible fraction, whereas DI and NI significantly reduced production and fruit set, while increasing nut dry matter and perisperm proportion of chestnuts. Plant physiological parameters, including midday stem water potential and chlorophyll fluorescence, confirmed the strong sensitivity of chestnut trees to water stress. Leaf dry matter, specific leaf weight, and total leaf chlorophyll content demonstrated either steady trends or slight reductions across years and treatments. Year-to-year variation was considerable, driven primarily by different summer temperatures, June to September rainfall, and the number of nuts per tree. Supplemental irrigation during nut development is essential for commercial chestnut production in the Mediterranean increasingly affected by climate. Full article

The application of autonomous navigation in intelligent agriculture is becoming more and more extensive. Traditional navigation schemes in greenhouses, orchards, and other agricultural environments often have problems such as the inability to deal with an uneven illumination distribution, complex layout, highly repetitive and similar structures, and difficulty in receiving GNSS (Global Navigation Satellite System) signals. In order to solve this problem, this paper proposes a new tightly coupled LiDAR (Light Detection and Ranging) inertial odometry SLAM (LIO-SAM) framework named April-LIO-SAM. The framework innovatively uses Apriltag, a two-dimensional bar code widely used for precise positioning, pose estimation, and scene recognition of objects as a global positioning beacon to replace GNSS to provide absolute pose observation. The system uses three-dimensional LiDAR (VLP-16) and IMU (inertial measurement unit) to collect environmental data and uses Apriltag as absolute coordinates instead of GNSS to solve the problem of unreliable GNSS signal reception in greenhouses, orchards, and other agricultural environments. The SLAM trajectories and navigation performance were validated in a carefully built greenhouse and orchard environment. The experimental results show that the navigation map developed by the April-LIO-SAM yields a root mean square error of 0.057 m. The average positioning errors are 0.041 m, 0.049 m, 0.056 m, and 0.070 m, respectively, when the density of Apriltag is 3 m, 5 m, and 7 m. The navigation experimental results indicate that, at speeds of 0.4, 0.3, and 0.2 m/s, the average lateral deviation is less than 0.053 m, with a standard deviation below 0.034 m. The average heading deviation is less than 2.3°, with a standard deviation below 1.6°. The positioning stability experiments under interference conditions such as illumination and occlusion were carried out. It was verified that the system maintained a good stability under complex external conditions, and the positioning error fluctuation was within 3.0 mm. The results confirm that the robot positioning and navigation accuracy of mobile robots satisfy the continuity in the facility. Full article

Chile, the leading exporter of cherries (Prunus avium L.) in the southern hemisphere, faces sustained variations in precipitation patterns and high evaporative demand in its productive areas. The low availability of water during the period of highest environmental demand makes it essential to reduce or suspend irrigation applications. In this scenario, regulated deficit irrigation (RDI) after harvest is an efficient strategy for optimizing water use without compromising orchard yields. This study was conducted over three consecutive seasons in a traditional commercial orchard of ‘Santina’ cherry trees grafted onto Colt rootstock, evaluating the effect of two levels of RDI, moderate (MDI) and severe (SDI), on productive and ecophysiological parameters. Both treatments resulted in water savings of between 10% and 28%, without negatively affecting yield or fruit quality. The SDI treatment, despite reaching higher levels of cumulative water stress, improved intrinsic water use efficiency while maintaining stable photosynthetic efficiency. In addition, an increase in the abundance of fine roots and beneficial rhizosphere bacteria populations, such as Azospirillum and Bacillus, was observed, suggesting the activation of water resilience mechanisms mediated by plant–microbiota interaction, possibly associated with stress-induced ecological memory and microbial legacy effects. These results position after-harvest RDI as a sustainable tool for coping with climate variability and water scarcity in commercial cherry orchards. Full article

This study developed and evaluated a LiDAR-based variable-rate orchard sprayer to address the inefficiency of traditional constant-rate application. The system dynamically adjusts pesticide output in real-time using a canopy volume calculation model and an adaptive delayed-spray mechanism, synchronized with LiDAR scans and travel speed. Experimental results demonstrated effective performance: the canopy volume estimation achieved a low overall error of 2.84%, enabling precise spray decision-making. The dosage control system showed an average error of 8.78%, and the adaptive system responded within 160 ms, distinguishing target gaps as small as 75 mm. Deposition tests confirmed uniform coverage within the canopy and minimal drift. The system proves to be a practical solution for significantly reducing pesticide use, operational costs, and environmental impact, marking a substantial advancement in precision orchard management. Full article

Accurate detection of apple blossoms is critical for monitoring flowering status and optimizing agricultural management. Traditional methods often fail to address challenges such as overlapping petals and environmental variability, leading to inefficiency and inaccuracy. In this paper, LEAF-Net, a modified YOLOv11-based target detection model, is proposed. The original C3k2 module in YOLOv11 lacks a targeted attention mechanism and exhibits insufficient enhancement of key features such as petal edges. Therefore, we propose our model, LEAF-Net, which incorporates a Multi-scale Attention Enhanced Block (MAEB) that enhances edge feature extraction through a hierarchical attention mechanism and reconstructs the C3k2 module. A Frequency-aware Feature Pyramid Network (Freq-FPN) that optimizes multi-scale feature fusion while preserving high-frequency details; and a comprehensive apple blossom dataset capturing diverse growth stages and environmental conditions. To address the dataset deficiencies, a specialized apple blossom dataset with complex backgrounds is constructed. Experimental results demonstrate state-of-the-art performance, with LEAF-Net achieving 90.4% mAP50 and 70.4% mAP50-95, significantly outperforming existing benchmarks. The framework’s computational efficiency (7.1 GFLOPs) and adaptability make it suitable for real-time deployment in precision agriculture. These advancements provide an extensible framework for precision orchard surveillance, thereby paving the way for their adaptive deployment in diverse agricultural automation contexts. Full article

Phage therapy has garnered significant attention due to the rise of life-threatening multidrug-resistant pathogenic bacteria and the growing awareness of the transfer of resistance genes between pathogens. Considering this, phage therapy applications are being extended to target plant pathogenic bacteria, such as Erwinia amylovora, which causes fire blight in apple and pear orchards. Understanding the mechanisms involved in phage resistance is crucial for enhancing the effectiveness of phage therapy. Despite the challenges of naturally developing a bacteriophage-insensitive mutant (BIM) of E. amylovora (without traditional mutagenesis methods), this study successfully created a BIM against the podovirus ϕEa46-1-A1. The parent strain, E. amylovora D7, and the BIM B6-2 were extensively compared at genomic, transcriptomic, and phenotypic levels. The phenotypic comparison included the metabolic behavior, biofilm formation, and in planta evaluations of pathogenicity. The results revealed a mutation in strain B6-2 in the rcsB gene, which encodes a second regulator in the Rcs two-component phosphorelay system (TCS). This mutation resulted in significant changes in the B6-2 BIM, including downregulation of amylovoran gene expression (e.g., an average log₂ fold change of −4.35 across amsA-L), visible alterations in biofilm formation, increased sensitivity to antibiotics (22.4% more sensitive to streptomycin), and a loss of pathogenicity as assessed in an apple seedling virulence model in comparison to the wildtype strain. The findings presented in this study highlight the critical role of the Rcs phosphorelay system in phage resistance in E. amylovora. Based on these findings, we have proposed a model that explains the effect of the B6-2 rcsB mutation on the Rcs phosphorelay system and its contribution to the development of phage resistance in E. amylovora. Full article

Aiming at the problems of complex image background and numerous interference factors faced by visual navigation systems in orchard environments, this paper proposes an orchard navigation line recognition method based on U-Net. Firstly, the drivable areas in the collected images are labeled using Labelme (a graphical tool for image annotation) to create an orchard dataset. Then, the Spatial Attention (SA) mechanism is inserted into the downsampling stage of the traditional U-Net semantic segmentation method, and the Coordinate Attention (CA) mechanism is added to the skip connection stage to obtain complete context information and optimize the feature restoration process of the drivable area in the field, thereby improving the overall segmentation accuracy of the model. Subsequently, the improved U-Net network is trained using the enhanced dataset to obtain the drivable area segmentation model. Based on the detected drivable area segmentation mask, the navigation line information is extracted, and the geometric center points are calculated row by row. After performing sliding window processing and bidirectional interpolation filling on the center points, the navigation line is generated through spline interpolation. Finally, the proposed method is compared and verified with U-Net, SegViT, SE-Net, and DeepLabv3+ networks. The results show that the improved drivable area segmentation model has a Recall of 90.23%, a Precision of 91.71%, a mean pixel accuracy (mPA) of 87.75%, and a mean intersection over union (mIoU) of 84.84%. Moreover, when comparing the recognized navigation line with the actual center line, the average distance error of the extracted navigation line is 56 mm, which can provide an effective reference for visual autonomous navigation in orchard environments. Full article

Climate change is expected to negatively impact agricultural production, leading to phenological and metabolic changes, increased water demands, diminished yields, and changed organoleptic characteristics, restricting the positive geographic productivity potential. As an adaptive strategy, agriculture in mountainous regions has gained prominence despite the fact that it entails new challenges. Indeed, mountain-specific conditions and limitations need to be considered, compared to the traditional productive regions. Consequently, there is a lack of information about the most suitable locations because the new conditions and limitations need to be accounted for. This study provides a crop suitability assessment approach to be used in mountainous regions where data about crop yield or development is scarce or nonexistent. Specifically, we evaluated the suitability of vineyards and apple orchards in the southern Pyrenees and Pre-Pyrenees. Using Geographical Information System (GIS) techniques, integrated with fuzzy logic and the Analytic Hierarchy Process (AHP), we combined traditional climatic, soil, and topographic indicators with factors relevant to mountainous regions. Our results indicated that the most suitable areas were primarily in lower basins and sunny hillsides, with smaller water needs. Vineyards would benefit from a very low risk of late spring frosts and elevated solar radiation, whereas apple orchards from a reduced risk of hailstorms, a very low risk of late spring frosts, and mild slopes. The fuzzy membership functions combined with the AHP facilitated the integration of indicators, effectively identifying areas with high potential for crop development. This approach contributes to landscape management and planning by offering a modifiable tool for assessing crop suitability in mountainous regions. Full article

Southern highbush blueberry (SHB, Vaccinium corymbosum, Ericaceae) enables production in warm, low-chill regions, where breeding success depends on precisely timed pollinations. To support breeding in non-traditional environments, we characterized floral staging, anther wall ontogeny, tubule formation, and pollen development in two SHB cultivars (‘Emerald’, ‘Snowchaser’) grown in commercial orchards. Floral development was divided into seven stages: dormant buds (db), five successive floral-bud stages (botA–botE), and anthesis, based on bud size, corolla exposure and pigmentation, and anther/tubule coloration. Internal events were documented by light, confocal, and scanning electron microscopy. External cues reliably separated stages and tracked male-gametophyte phases: meiosis at botB; callose-encased tetrads at botC; permanent tetrahedral tetrads after callose dissolution at botD; bicellular tetrads from botE to anthesis, released intact via poricidal dehiscence. Anther-wall differentiation followed a consistent sequence and lacked a fibrous, lignified endothecium. We therefore propose a new Ericaceous pattern for blueberry anthers, defined by a transient non-lignified subepidermal stratum. Tubules originated apically as solid outgrowths, hollowed centrifugally to a beveled pore, developed a dorsal supportive zone, and mediated poricidal release of permanent tetrads. No qualitative cultivar differences were detected. The staging framework defines operational windows for pollination, emasculation, and pollen handling in low-chill systems. Full article

Soil erosion is a major threat to land productivity and environmental sustainability in Mediterranean regions, where sloping terrain, intense seasonal rainfall, and traditional agricultural practices accelerate soil loss. Olive orchards, which dominate much of the Mediterranean landscape, are particularly vulnerable. As climate change increases the frequency of extreme weather events, understanding and controlling erosion becomes even more critical. This study investigates soil erosion dynamics in a representative olive-growing watershed in Messenia, Greece, by combining field monitoring with erosion modeling using the Revised Universal Soil Loss Equation (RUSLE). A field experiment was carried out during the 2024–2025 wet season, using runoff plots installed on a 16% slope to directly measure sediment loss from natural rainfall events. The observed erosion data served as a basis for calibrating a GIS-based RUSLE model applied across the 60 km² watershed. Model predictions showed strong agreement with field measurements, with estimated soil loss closely matching the observed seasonal total (~0.6 t/ha). This consistency demonstrates the reliability of the RUSLE model when supported by localized data. The spatial analysis further revealed that erosion risk varies widely across the landscape, with steep, poorly vegetated areas being most at risk. The results highlight the importance of local field measurements for improving model accuracy and guiding sustainable land management. Continuous monitoring and targeted erosion control strategies are essential to protect soil resources, maintain agricultural productivity, and reduce downstream environmental impacts under increasing climate pressures. Full article

Crop row detection technology, as one of the key technologies for agricultural robots to achieve autonomous navigation and precise operations, is related to the precision and stability of agricultural machinery operations. Its research and development will also significantly determine the development process of intelligent agriculture. The paper first summarizes the mainstream technical methods, performance evaluation systems, and adaptability analysis of typical agricultural scenes for crop row detection. The paper also summarizes and explains the technical principles and characteristics of traditional methods based on visual sensors, point cloud preprocessing based on LiDAR, line structure extraction and 3D feature calculation methods, and multi-sensor fusion methods. Secondly, a review was conducted on performance evaluation criteria such as accuracy, efficiency, robustness, and practicality, analyzing and comparing the applicability of different methods in typical scenarios such as open fields, facility agriculture, orchards, and special terrains. Based on the multidimensional analysis above, it is concluded that a single technology has specific environmental adaptability limitations. Multi-sensor fusion can help improve robustness in complex scenarios, and the fusion advantage will gradually increase with the increase in the number of sensors. Suggestions on the development of agricultural robot navigation technology are made based on the current status of technological applications in the past five years and the needs for future development. This review systematically summarizes crop row detection technology, providing a clear technical framework and scenario adaptation reference for research in this field, and striving to promote the development of precision and efficiency in agricultural production. Full article

With global climate change, urbanization, and agricultural resource limitations, precision agriculture and crop monitoring are crucial worldwide. Integrating multi-source remote sensing data with deep learning enables accurate crop mapping, but selecting optimal network architectures remains challenging. To improve remote sensing-based fruit planting classification and support orchard management and rural revitalization, this study explored feature selection and network optimization. We proposed an improved CF-EfficientNet model (incorporating FGMF and CGAR modules) for fruit planting classification. Multi-source remote sensing data (Sentinel-1, Sentinel-2, and SRTM) were used to extract spectral, vegetation, polarization, terrain, and texture features, thereby constructing a high-dimensional feature space. Feature selection identified 13 highly discriminative bands, forming an optimal dataset, namely the preferred bands (PBs). At the same time, two classification datasets—multi-spectral bands (MS) and preferred bands (PBs)—were constructed, and five typical deep learning models were introduced to compare performance: (1) EfficientNetB0, (2) AlexNet, (3) VGG16, (4) ResNet18, (5) RepVGG. The experimental results showed that the EfficientNetB0 model based on the preferred band performed best in terms of overall accuracy (87.1%) and Kappa coefficient (0.677). Furthermore, a Fine-Grained Multi-scale Fusion (FGMF) and a Condition-Guided Attention Refinement (CGAR) were incorporated into EfficientNetB0, and the traditional SGD optimizer was replaced with Adam to construct the CF-EfficientNet architecture. The results indicated that the improved CF-EfficientNet model achieved high performance in crop classification, with an overall accuracy of 92.6% and a Kappa coefficient of 0.830. These represent improvements of 5.5 percentage points and 0.153, compared with the baseline model, demonstrating superiority in both classification accuracy and stability. Full article