Search Results (725)

Image-level monitoring and variety-level detection of three specialty tomato cultivars, Kiss, Millennium, and White Jade, remain challenging in unstructured orchards because of foliage occlusion, overlapping fruit clusters, and variable illumination. Conventional downsampling may weaken fine spatial details of small targets, whereas larger detectors can impose computational demands that are unsuitable for mobile or edge-based agricultural platforms. To address these limitations, we propose WCAF-YOLO, a lightweight two-dimensional tomato detector based on a modified YOLOv26n architecture. The model replaces the P3 backbone downsampling operation with space-to-depth convolution (SPD-Conv) to retain fine-grained spatial information. Its weighted channel-aware fusion (WCAF) neck combines learnable branch weighting with parameter-free three-dimensional attention to refine fused features. Bounding-box regression uses focaler-minimum point distance intersection over union (Focaler-MPDIoU). Across five random seed runs on the internal held-out test subset of a custom single-site orchard dataset, WCAF-YOLO obtained a mean ${\mathrm{mAP}}_{50–95}$ of $0.9048\pm 0.0013$ and a mean recall of $0.9280\pm 0.0019$. The corresponding mean improvements over the YOLOv26n baseline were 2.14 and 3.42 percentage points, respectively. The model contained 2.36 M parameters and required 6.36 GFLOPs. Under the evaluated protocol, the model combined a compact parameter count with higher mean detection metrics than the YOLOv26n baseline. The detector outputs two-dimensional bounding boxes and variety labels for image-level orchard monitoring and variety-level assessment. Integration into agricultural field platforms remains to be validated. Full article

Economically motivated adulteration of olive oil, coffee and fruit juice is a persistent food-fraud problem for which Fourier-transform infrared (FTIR) spectroscopy with chemometrics offers rapid screening. Linear partial least squares (PLS) is interpretable but cannot capture non-linear mixing; neural networks add flexibility at the cost of becoming black boxes. We evaluated a Kolmogorov–Arnold network (KAN), which places learnable univariate functions on its edges and is therefore intrinsically interpretable, against PLS, support-vector regression, random forests, a multilayer perceptron and a one-dimensional convolutional network on three attenuated total reflectance (ATR)–FTIR datasets (olive oil + sunflower oil, coffee + malt flour, orange juice + apple juice; approximately 350, 400 and 400 spectra). All models were compared under identical, leakage-free validation that splits spectra by physical sample. The compact KAN was consistently competitive (cross-validated coefficients of determination (R²) = 0.86, 0.93 and 0.69) and yielded closed-form equations whose variables map to recognised vibrational bands and whose importance ranking agrees with SHapley Additive exPlanations (SHAP; Spearman ρ = 0.86–0.90); symbolic conversion costs no accuracy. We also report the following limits: PLS was strongest where the chemistry was linear (coffee) and the multilayer perceptron was strongest on fruit juice, whose equation is the weakest (R² = 0.47–0.75 across seeds); a parameter-matched perceptron matched the KAN’s accuracy; and leave-one-brand-out validation degraded every model. The KAN is therefore a promising, compact and genuinely transparent alternative under controlled multi-matrix conditions, not a deployment-ready method. Full article

Manual fruit thinning is labor-intensive and inefficient, making the development of intelligent visual detection systems a crucial approach for improving the quality and production efficiency of the pear industry. However, in natural orchard environments, young pear fruits are small in size with slender fruit stalks, and their texture and color characteristics are highly similar to those of tender branches. Furthermore, variations in illumination and occlusions caused by branches and leaves make it difficult for existing detection models to simultaneously and accurately identify fruits and fruit stalks, limiting their application in automated thinning equipment. In this study, Yuluxiang pear was selected as the research object, and image data were collected under diverse field conditions, including forward-lighting, backlighting, close-range shooting, long-range shooting and fruit overlapping. A dedicated dataset containing 3057 images was established. Based on the RT-DETR-R18 network, a lightweight and high-precision fruit–stalk synchronous detection model was proposed. Specifically, the backbone network was reconstructed by integrating GCConv with C2f modules to enhance global feature extraction for slender fruit stalks. The bottleneck structure was optimized using GCConvC3 to reduce feature degradation under occlusion conditions, and an additional 4× down-sampling P2 detection head was introduced to improve the detection capability for small targets. To fully validate the model performance and stability, three types of experiments were conducted in this study: ablation experiments, repeated experiments with different random seeds, and comparative experiments. Ablation experiments verified the cumulative performance improvements brought by the introduced modules. Repeated experiments with different random seeds were performed to explore training randomness-induced performance fluctuations, and the results demonstrated that the proposed model maintains stable overall detection accuracy with minor metric fluctuations. Comparative experiments demonstrated that the proposed model achieved a compact parameter size of only 15.97 M, with a precision of 95.0% for young pear fruit detection and an mAP50 of 83.0% for fruit stalk detection, outperforming all comparative models in overall mAP50. The training convergence curves and Grad-CAM++ visualization results further confirmed the stable optimization process and enhanced feature attention capability of the proposed model. By achieving a favorable balance between detection accuracy and model lightweightness, this approach provides effective technical support for the development of intelligent fruit thinning equipment and vision-based systems for smart pear orchards. Full article

Jujube (Ziziphus jujuba Mill.) is an important functional fruit with high levels of bioactive compounds. However, its high moisture content limits its postharvest shelf life. The biochemical properties of jujube fruits influenced by hot-air drying at 50, 60, and 70 °C were studied and predictive models were evaluated to optimize drying. The drying kinetics were modeled using the six classical thin-layer models and artificial neural network (ANN) approach. The Midilli–Kucuk model was the best classical model, and the ANN model showed the best predictive performance, with the RMSE values improved up to 19.52% in the independent validation dataset. The drying temperature significantly influenced the color, composition of sugars, organic acids, total phenolic content, and antioxidant activity. Color difference (ΔE = 5.88) and browning index of the 70 °C treatment showed less color change, indicating better color retention. Sugar and organic acid concentrations were significantly higher in dried fruits than in fresh fruits. The highest total phenolic content (5393.43 mg GAE 100 g⁻¹) and DPPH radical scavenging activity (73.56%) were found at 50 °C and progressively decreased with increasing drying temperature. Multivariate analyses further showed clear biochemical differentiation between fresh and dried samples according to drying temperature. Generally, the drying temperature had a significant effect on the nutritional and functional quality of jujube fruits. Although 50 °C was the most effective for maintaining phenolic compounds and antioxidant activity, 70 °C offered better color retention and shorter drying time. The ANN method was a useful tool to predict the drying behavior and optimize processing conditions in the production of jujube fruit. Full article

Understanding how animals operationally identify and exploit resource patches is central to foraging ecology. The “patch”—a discrete area of resource concentration—is the fundamental unit of individual foraging behavior; however, standardized operational definitions of the criteria for patch identification across taxa, habitats, and biomes are still scarce. Following PRISMA 2020 guidelines, we reviewed 87 empirical studies encompassing 133 species, classifying patch identification methods across eight macro-categories of the European Nature Information System (EUNIS)—a structurally detailed habitat classification that, despite its European origin, we apply here as a general structural template for habitat architecture rather than as a claim of biogeographic universality, given that our dataset also spans non-European taxa and biomes. Patches are operationally identified through two approaches: observational delineation of natural resource structures (e.g., fruiting trees, prey aggregations, vegetation clusters) and experimental manipulation via artificial depletable units. We demonstrate that habitat structural complexity and species trophic preferences jointly govern the operational method by which patches are identified: habitat architecture determines which operational approach is used for patch delineation—observational or experimental—while a four-category Feeding Guild classification (Granivore, Herbivore, Omnivore, Carnivore) reveals that resource discretizability—the capacity to standardize prey into countable, depletable units—operates as the primary species-level predictor of patch identification method selection, an association that remains robust after formally controlling for phylogenetic non-independence and body mass in a mixed-effects model. The resulting framework provides, for the first time, a standardized operational guide for calibrating patch identification methods to habitat type and species trophic preferences and searching behavior across taxa and biomes. Full article

In complex and unstructured orchard environments, accurate fruit detection is essential for yield estimation and robotic harvesting in precision agriculture. However, single-model detectors often suffer from reduced robustness and high miss rates under drastic illumination changes, severe occlusions, and dense fruit overlap. To address these challenges, this study proposes a multi-model fusion framework for robust mango detection in complex orchard environments. The proposed method employs YOLOv8n, YOLOv8s, and YOLOv8m as base detectors and applies multi-scale test-time augmentation (TTA) to obtain predictions from different augmented views. After mapping the predicted bounding boxes back to the original image coordinate system, predictions corresponding to the same target across different TTA views of each base detector are matched based on the intersection over union (IoU), yielding model-specific prediction results. Weighted Box Fusion (WBF) is then applied to determine the fused bounding-box coordinates. For candidate targets jointly detected by multiple base detectors, the confidence scores provided by the individual models are combined using Noisy-OR to obtain the fused confidence score. Finally, Gaussian Soft-NMS is applied to decay the scores of overlapping candidate boxes, thereby reducing the risk of incorrectly suppressing adjacent mangoes in densely clustered scenes. Experiments on two complementary datasets under within-dataset evaluation protocols demonstrate the effectiveness of the proposed method. On the standard dataset (Data1), Recall and mAP@0.5 reach 95.52% and 98.60%, respectively. Across five repeated random holdout splits of Data2, the proposed framework increased the mean Recall from 82.79% to 84.90% and the mean mAP@0.5 from 90.27% to 91.23%. These results indicate that the proposed framework improves detection robustness and completeness compared with single-model detectors in complex orchard environments, demonstrating its potential for offline yield estimation and orchard phenotyping. Full article

This study investigated how detection distance affects spectral models for soluble solids content (SSC) and firmness evaluation in Korla fragrant pears and provides a reference for calibrating standardized indoor non-destructive detection equipment. Two hundred visually intact fruit samples at the early-ripening stage were collected from the Korla production area in Xinjiang. An FS-640 multispectral camera system equipped with a VS-SWR fixed-focus industrial lens (16 mm focal length, F1.8 maximum aperture, 1/2-inch sensor format) was used to acquire fruit reflectance spectra at seven vertical lens-to-fruit-surface distances of 90, 100, 110, 120, 130, 140, and 150 cm. A 625-pixel region of interest (ROI) was selected using ENVI at an undamaged equatorial or near-equatorial position of each fruit, and the regional mean spectrum was used as the spectral feature of one fruit sample. The sample-set partitioning based on joint X–Y distances (SPXY) algorithm was used to divide the calibration and prediction sets at a 3:1 ratio after outlier removal via a residual-threshold method. Four preprocessing methods, namely LOESS smoothing, standardization, vector normalization, and Savitzky–Golay (SG) smoothing, were compared. Competitive adaptive reweighted sampling (CARS) was performed with 50 Monte-Carlo sampling runs, a maximum of 30 principal components, and 10-fold cross-validation, yielding 99 characteristic wavelengths. Partial least squares regression (PLSR), support vector regression (SVR), random forest (RF), and artificial neural network (ANN) models were then established using identical input variables and sample partitions. Model performance was evaluated using the coefficient of determination for calibration ($R_c^2$), coefficient of determination for prediction ($R_P^2$), root-mean-square error of calibration (RMSEC), root-mean-square error of prediction (RMSEP), relative prediction deviation (RPD), and ratio of performance to interquartile distance (RPIQ). Under the static laboratory acquisition conditions in this work, the SSC model achieved the best prediction performance at 110 cm with SG smoothing ($R_P^2$) = 0.8949, RPD = 3.0633, RPIQ = 5.8661), whereas the firmness model obtained optimal prediction performance at 140 cm with standardization ($R_P^2$) = 0.7460, RPD = 1.9425, RPIQ = 3.2867). Changes in detection distance altered illumination uniformity, effective reflected signal, photon-scattering paths, and background-noise proportion. These effects may partially explain why the chemical-absorption-dominated SSC index and the tissue-scattering-dominated firmness index responded differently to detection distance. The results provide a reference for setting spectral detection parameters for Korla fragrant pears; however, samples were obtained from only a single producing region, harvest season, and maturity stage, and no independent external validation dataset was used. Therefore, the generalization ability of the developed models needs to be further verified using cross-season and cross-orchard sample sets. Full article

Hidden adulteration of stale berries in dried goji berry batches is difficult to detect by manual inspection or batch-level quality assessment. This study developed a high-throughput method for kernel-level spoilage adulteration quantification in dried goji berries. It addressed three practical challenges in the image processing of densely arranged dried-fruits, including scalable label generation for deep learning segmentation without pixel-level manual annotation, separation of densely touching small berries, and full-size quality level distribution map reconstruction. SAM-assisted pseudo-label generation combined with multi-scale image cropping was used to overcome the limitation of manual pixel-level annotation, while YOLO-based instance segmentation was further employed for efficient berry localization in dense scenes. The freshness labels of segmented single berries were assigned by a statistical RGB-HSV grading rule. Specifically, adaptive multi-scale image cropping for segmentation was applied to improve local separability of berries under dense adhesion and occlusion conditions. The crop-level segmentation and grading outputs were subsequently reconstructed into the original image coordinate system to generate complete quality distribution maps. Results showed that YOLO models trained based on the pseudo-labels achieved a precision of 0.953, a recall of 0.951, an mAP₅₀ of 0.960, and an mAP_50-95 of 0.846. The full-size grading map reconstruction method produced a mean duplicate-suppression rate of 4.31%. In the full freshness-grading test dataset, 4850 berries were detected, including 449 stale berries. The mean absolute counting error was 1.61%. The proposed framework reduces manual annotation requirements while enabling berry-level freshness classification and quantitative stale-berry proportion estimation, providing objective information for dried fruit quality screening and adulteration control. Full article

In complex orchard environments, sweet persimmons tend to grow in dense clusters and display similar coloration across different maturity stages, leading to heavy occlusion and poor inter-class color discriminability. To address these challenges, this paper presents RCPNet, a detection network tailored for such field conditions. The model integrates a Rectangular Self-Calibration Module (RCM) and a Context Feature Calibration Gating (CFCG) module. RCM strengthens axial context capture, while CFCG improves feature calibration; together they reduce local feature ambiguity and help reconstruct missing information in occluded regions. For distinguishing fruits at different ripening stages that share similar colors, a Parallelized Patch-aware Attention (PPA) detection head is adopted. By leveraging self-attention and multi-branch strategies, this head suppresses feature degradation and notably enhances sensitivity to color contrast. Experiments on sweet persimmon images show that RCPNet improves mean Average Precision (mAP) by 2.7 percentage points and mAP@0.5:0.95 by 3.7 percentage points over the baseline, reaching 93.6% detection accuracy for immature fruits. Ablation studies and comparisons with mainstream detectors indicate that the proposed model, though slightly heavier than lightweight detectors of analogous capacity, surpasses the accuracy of a larger small-scale counterpart and exhibits satisfactory robustness. Strong performance on a self-collected flat jujube dataset further confirms its generalization ability. The method delivers highly accurate detection for occluded and near-color fruits, providing technical support for precise fruit recognition and automated picking. Full article

Technology-critical elements are increasingly used in electronics, renewable-energy systems, high-performance alloys and other strategic technologies, yet their occurrence in wild fungi remains poorly characterised. This study evaluated gallium (Ga), germanium (Ge), niobium (Nb), tantalum (Ta) and tungsten (W) in wild mushrooms collected from Leicester and Leicestershire, UK. The dataset was reconstructed at fruiting-body level to avoid pseudo-replication from paired cap and stem records, yielding 121 analytical units. Results were corrected using digestion-date acid blanks, interpreted against analytical-portion-specific working limits of detection and classified as fully quantified (FQ), partially censored (PC) or fully censored (FC). Ga was FQ in 106/121 units and was the only element with sufficient quantitative coverage for continuous distributional estimation; its regression-on-order-statistics median was 475.48 ng g⁻¹ dw (IQR: 278.70–941.63). The corresponding FQ counts were 16 for Ge, 33 for Nb, 10 for Ta and 24 for W, with 7, 4 and 4 additional PC observations for Nb, Ta and W, respectively. In 22 paired Agaricus bitorquis fruiting bodies, Ga concentrations were higher in stems than caps (Wilcoxon p = 5.25 × 10⁻⁵; BH-FDR = 1.05 × 10⁻⁴). Composite topsoils from 26 monitored sites provided park-scale geochemical context only and were not used to infer bioaccumulation or direct soil-to-fungus transfer. These findings establish a blank-corrected and censoring-aware occurrence baseline and suggest exploratory, site-specific potential for wild mushrooms as complementary biomonitoring matrices for selected technology-critical elements, while demonstrating that their usefulness is element-, taxon- and design-dependent. Full article

Loquat (Eriobotrya japonica), an economically important evergreen fruit tree, exhibits an unusual reproductive cycle characterized by autumn–winter flowering and spring fruit maturation. However, the endogenous hormonal basis underlying this distinctive developmental pattern remains poorly understood, partly due to the lack of reliable multi-hormone analytical platforms for complex perennial fruit tissues. In this study, we established a robust targeted UHPLC-QQQ-MS/MS method for the simultaneous quantification of 11 endogenous phytohormones and related metabolites, including indole-3-acetic acid (IAA), abscisic acid (ABA), gibberellins (GA₁, GA₃, GA₄, and GA₇), and cytokinins (tZR, iPR, DHZ, iP, and zeatin). Sample preparation and chromatographic conditions were systematically optimized to improve extraction efficiency, to reduce matrix interference, and to maximize detection sensitivity. The method exhibited high linearity (R² > 0.990), satisfactory recoveries (80.02–120.09%), low limits of detection, and high repeatability. Application of this platform to eight different loquat tissues revealed pronounced tissue-specific patterns of phytohormone distribution. Seeds contained exceptionally high levels of GA₃ and zeatin, whereas stamens exhibited marked enrichment of IAA. Mature leaves showed relatively high ABA levels, whereas fruit flesh retained substantial cytokinin pools. These findings provide a reliable analytical workflow for phytohormone quantification in perennial fruit crops and establish a foundational dataset of tissue-specific hormone distribution in loquat, which may serve as a valuable reference for future studies on reproductive development and horticultural trait improvement. Full article

This article presents a systematic, evidence-weighted review of Deep Learning (DL) in farming, with a primary emphasis on the agricultural production stage and on four operational domains: precision crop management, precision livestock farming, soil and water resource management, and autonomous agricultural systems. Following a PRISMA 2020-oriented protocol, 56 primary studies (42 with quantitative results) were retained from an initial pool of 18,731 records. The reviewed literature reports applications in plant disease detection, weed recognition, yield prediction, fruit detection, livestock identification and health monitoring, soil-property estimation, crop-water-stress assessment, and robotic perception. High performance on controlled datasets, however, is frequently reported without external, temporal, or cross-site validation, making practical generalisation difficult to establish: in one widely cited benchmark, disease-classification accuracy fell from above 99% on held-out laboratory images to 31.4% on field-acquired images of the same classes. Persistent weaknesses include the limited availability of public benchmarks, inconsistent validation protocols, limited interpretability, fragmented data governance, and insufficient techno-economic analysis. The review argues that the next stage of agricultural AI should be judged less by isolated benchmark scores and more by field realism, reproducibility, deployment maturity, and practical usefulness. Unlike broad surveys that mainly catalogue architectures and applications, this review interprets the literature according to dataset representativeness, validation protocols, benchmarking transparency, deployment realism, and reproducibility, distinguishing algorithmic performance under controlled conditions from practical readiness for real farming environments. The most promising research directions include self-supervised and multimodal learning, explainable and privacy-preserving AI, edge-aware deployment, and hybrid process-informed models. Full article

To address the visual perception challenges in automated harvesting of the ‘Sucui No.1 Pear’, this study proposes a fast detection and 3D localization method based on an improved YOLOv11 architecture and an RGB-D camera. First, a multi-scene ‘Sucui No.1 Pear’ dataset containing 5842 RGB-D image pairs and 31,559 labeled instances was constructed. Second, a lightweight YOLOv11-pear detection model optimized for pear fruit was developed; by reconstructing the feature pyramid network and introducing a global attention mechanism, using K-means++ clustering to optimize prior anchor boxes, and designing a composite loss function (Varifocal Loss + CIoU Loss + DFL), the detection accuracy was improved while maintaining lightweight design. The model adopts a “detect first, then fuse” strategy, achieving robust 3D coordinate calculation based on the median depth of the bottom region of the detection box. Experimental results show that YOLOv11-pear achieves 93.8% mAP@0.5 and 65.5% mAP@0.5:0.95 on the independent test set, with a precision of 94.2% and a recall of 91.5%. The model has only 5.8 M parameters and achieves real-time inference at 38.7 FPS on the Jetson Orin NX edge platform, with a mean absolute error of 9.9 mm for 3D localization. In severely occluded and complex lighting scenarios, the mAP@0.5 reaches 80.9% and 87.5%, respectively. After integrating the vision system into the harvesting robot platform, end-to-end closed-loop testing in a real orchard achieved an 88% harvesting success rate and 5% fruit damage rate. The average time for the visual perception stage was 1.3 s, accounting for 9.4% of the harvesting cycle. This research provides a high-precision, lightweight, and deployable vision solution for automated harvesting of the ‘Sucui No.1 Pear’, and has important reference value for promoting the development of intelligent fruit harvesting technology. Full article

This scoping review synthesizes 35 studies on sensor technologies and artificial intelligence for fruit detection, classification, and quality assessment, contrasting Latin American and international research traditions. The analysis suggests notable differences in technological approaches between the included Latin American and international studies: Latin American research tends to emphasize in developing accessible, practical solutions using classical computer vision and low-cost hardware, while international studies more frequently employ through deep learning architectures, multi-modal sensing, and complete robotic automation systems. Across the included studies, relatively limited attention was given to AI-assisted decision support for agricultural practitioners, insufficient consideration of inclusivity, and the scarce integration of environmental sustainability into intelligent sensing system design. The review identifies that only 8 of 35 studies originate from Latin America, suggesting an uneven geographical distribution of the available evidence. The included studies generally reported high accuracy values, yet these findings must be interpreted with caution given the reliance on curated datasets that may not represent real-world variability. The reviewed evidence suggests that future research may benefit not from one approach dominating the other, but from a thoughtful integration of complementary strategies, including knowledge transfer, edge computing democratization, and human-centered design. Overall, this review suggests that the ultimate goal extends beyond accuracy metrics to the transformation of agricultural practices that enhance food security, economic development, and environmental sustainability across the global agricultural landscape. It is important to note that this work does not propose or validate a new fruit detection algorithm but rather synthesizes and critically evaluates existing scientific evidence regarding sensor technologies and artificial intelligence applied to fruit detection and quality assessment. Full article

Binocular stereo vision is a low-cost and scalable 3D perception technology that shows strong potential in agricultural phenotyping and smart agriculture. By estimating depth from multi-view RGB images, it enables non-contact, high-precision sensing of crop structure, canopy morphology, growth dynamics, and livestock traits, providing essential support for digital and intelligent agricultural production. With recent advances in deep learning-based stereo matching, multimodal sensor fusion, and 3D reconstruction, its robustness and accuracy in complex field environments have been significantly improved. This paper systematically reviews recent progress in agricultural applications of binocular stereo vision, covering system architectures, traditional and deep learning-based stereo matching methods, point cloud reconstruction techniques, and emerging supervision strategies such as 3D Gaussian splatting. It further summarizes key applications, including high-throughput phenotyping, fruit localization and robotic harvesting, weed detection and precision spraying, autonomous navigation, and livestock body condition assessment, highlighting its role in multi-task agricultural perception systems. Finally, the paper discusses major challenges, including low-texture matching difficulty, occlusions in complex environments, cross-domain generalization, real-time lightweight deployment, and limited dataset availability. Future directions are outlined in foundation model-based visual perception, self- and weakly supervised learning, multimodal fusion, and edge-efficient model design, aiming to support large-scale deployment in smart agriculture. Full article