Search Results (334)

More recently, artificial intelligence (AI)-generated content (AIGC) is fundamentally transforming multiple sectors, including materials discovery, healthcare, education, scientific research, and industrial manufacturing. As for the complexities and challenges of intelligent robot design, AIGC has the potential to offer a new paradigm, assisting in conceptual and technical design, functional module design, and the training of the perception ability to accelerate prototyping. Taking the design of an apple-harvesting robot, for example, we demonstrate a basic framework of the AIGC-assisted robot design methodology, leveraging the generation capabilities of available multimodal large language models, as well as the human intervention to alleviate AI hallucination and hidden risks. Second, we study the enhancement effect on the robot perception system using the generated apple images based on the large vision-language models to expand the actual apple images dataset. Further, an apple-harvesting robot prototype based on an AIGC-aided design is demonstrated and a pick-up experiment in a simulated scene indicates that it achieves a harvesting success rate of 92.2% and good terrain traversability with a maximum climbing angle of 32°. According to the tentative research, although not an autonomous design agent, the AIGC-driven design workflow can alleviate the significant complexities and challenges of intelligent robot design, especially for beginners or young engineers. Full article

With the advancement of agricultural automation technologies, apple-harvesting robots have gradually become a focus of research. As their “perceptual core,” machine vision systems directly determine picking success rates and operational efficiency. However, existing vision systems still exhibit significant shortcomings in target detection and positioning accuracy in complex orchard environments (e.g., uneven illumination, foliage occlusion, and fruit overlap), which hinders practical applications. This study proposes a visual system for apple-harvesting robots based on improved Mask R-CNN and binocular vision to achieve more precise fruit positioning. The binocular camera (ZED2i) carried by the robot acquires dual-channel apple images. An improved Mask R-CNN is employed to implement instance segmentation of apple targets in binocular images, followed by a template-matching algorithm with parallel epipolar constraints for stereo matching. Four pairs of feature points from corresponding apples in binocular images are selected to calculate disparity and depth. Experimental results demonstrate average coefficients of variation and positioning accuracy of 5.09% and 99.61%, respectively, in binocular positioning. During harvesting operations with a self-designed apple-picking robot, the single-image processing time was 0.36 s, the average single harvesting cycle duration reached 7.7 s, and the comprehensive harvesting success rate achieved 94.3%. This work presents a novel high-precision visual positioning method for apple-harvesting robots. Full article

Object detection is crucial for smart apple orchard management using agricultural machinery to avoid obstacles. The objective of this study was to detect apple trees and other objects in an apple orchard using LiDAR and the YOLOv5 algorithm. A commercial LiDAR was attached to a tripod to collect apple tree trunk data, which were then pre-processed and converted into PNG images. A pre-processed set of 1500 images was manually annotated with bounding boxes and class labels (trees, water tanks, and others) to train and validate the YOLOv5 object detection algorithm. The model, trained over 100 epochs, resulted in 90% precision, 87% recall, mAP@0.5 of 0.89, and mAP@0.5:0.95 of 0.48. The accuracy reached 89% with a low classification loss of 0.001. Class-wise accuracy was high for water tanks (96%) and trees (95%), while the “others” category had lower accuracy (82%) due to inter-class similarity. Accurate object detection is challenging since the apple orchard environment is complex and unstructured. Background misclassifications highlight the need for improved dataset balance, better feature discrimination, and refinement in detecting ambiguous objects. Full article

In order to maintain adequate hydration of apple rootstock softwood cuttings during the initial stage of cutting, a drought stress grading model based on machine vision was designed. This model was optimized based on the U-Net (U-shaped Neural Network), and the petiole morphology of the cuttings was used as the basis for classifying the drought stress levels. For the CU-ICA-Net model, which is obtained by improving U-Net with the ICA (Improved Coordinate Attention) module designed using a cascaded structure and dynamic convolution, the average accuracy rate of the predictions for the three parts of the cuttings, namely the leaf, stem, and petiole, is 93.37%. The R² values of the prediction results for the petiole curvature k and the angle α between the petiole and the stem are 0.8109 and 0.8123, respectively. The dataset used for model training consists of 1200 RGB images of cuttings under different grades of drought stress. The ratio of the training set to the test set is 1:0.7. A humidification test was carried out using an automatic humidification system equipped with this model. The MIoU (Mean Intersection over Union) value is 0.913, and the FPS (Frames Per Second) value is 31.90. The test results prove that the improved U-Net model has excellent performance, providing a method for the design of an automatic humidification control system for industrialized cutting propagation of apple rootstocks. Full article

Accurately extracting large-scale apple orchards from remote sensing imagery is of importance for orchard management. Most studies lack large-scale, high-resolution apple orchard maps due to sparse orchard distribution and similar crops, making mapping difficult. Using phenological information and multi-temporal feature-selected imagery, this paper proposed a large-scale apple orchard mapping method based on the AOCF-SegNet model. First, to distinguish apples from other crops, phenological information was used to divide time periods and select optimal phases for each spectral feature, thereby obtaining spectral features integrating phenological and temporal information. Second, semantic segmentation models (FCN-8s, SegNet, U-Net) were com-pared, and SegNet was chosen as the base model for apple orchard identification. Finally, to address the issue of the low proportion of apple orchards in remote sensing images, a Convolutional Block Attention Module (CBAM) and Focal Loss function were integrated into the SegNet model, followed by hyperparameter optimization, resulting in AOCF-SegNet. The results from mapping the Yantai apple orchards indicate that AOCF-SegNet achieved strong segmentation performance, with an overall accuracy of 89.34%. Compared to the SegNet, U-Net, and FCN-8s models, AOCF-SegNet achieved an improvement in overall accuracy by 3%, 6.1%, and 9.6%, respectively. The predicted orchard area exhibited an approximate area consistency of 71.97% with the official statistics. Full article

The chlorophyll content is an important index reflecting the growth status and nutritional level of plants. The rapid, accurate and nondestructive monitoring of the SPAD content of apple trees can provide a basis for large-scale monitoring and scientific management of the growth status of apple trees. In this study, the canopy leaves of apple trees at different growth stages in the same year were taken as the research object, and remote sensing images of fruit trees in different growth stages (flower-falling stage, fruit-setting stage, fruit expansion stage, fruit-coloring stage and fruit-maturing stage) were acquired via a DJI MAVIC 3 multispectral unmanned aerial vehicle (UAV). Then, the spectral reflectance was extracted to calculate 15 common vegetation indexes as eigenvalues, the 5 vegetation indexes with the highest correlation were screened out through Pearson correlation analysis as the feature combination, and the measured SPAD values in the leaves of the fruit trees were gained using a handheld chlorophyll meter in the same stages. The estimation models for the SPAD values in different growth stages were, respectively, established through five machine learning algorithms: multiple linear regression (MLR), partial least squares regression (PLSR), support vector regression (SVR), random forest (RF) and extreme gradient boosting (XGBoost). Additionally, the model performance was assessed by selecting the coefficient of determination (R²), root mean square error (RMSE) and mean absolute error (MAE). The results show that the SPAD estimation results vary from stage to stage, where the best estimation model for the flower-falling stage, fruit-setting stage and fruit-maturing stage is RF and those for the fruit expansion stage and fruit-coloring stage are PLSR and MLR, respectively. Among the estimation models in the different growth stages, the model accuracy for the fruit expansion stage is the highest, with R² = 0.787, RMSE = 0.87 and MAE = 0.644. The RF model, which outperforms the other models in terms of the prediction effect in multiple growth stages, can effectively predict the SPAD value in the leaves of apple trees and provide a reference for the growth status monitoring and precise management of orchards. Full article

Background and Clinical Significance: Mitral valve prolapse (MVP) is commonly benign, but may result in life-threatening arrhythmias and sudden cardiac death (SCD). Mitral annular disjunction (MAD) often coexists with mitral valve prolapse (MVP) and has been implicated in the development of ventricular arrhythmias through myocardial stretch and fibrosis. Case Presentation: Here, we present a case that highlights the diagnostic value of multimodal imaging in evaluating ventricular ectopy in the context of MVP and MAD. A 72-year-old male presented to the cardiology clinic with palpitations and fatigue, compounded by an arrhythmia identified by his Apple Watch. Holter monitoring revealed premature ventricular contractions (PVCs), with cardiac magnetic resonance imaging (CMR) demonstrating MAD and basal inferolateral scarring. Despite minimal symptoms and normal echocardiographic imaging, CMR findings highlight the utility of advanced cardiovascular imaging in patients with newly detected ventricular arrhythmias. Conclusion: This case highlights the importance of integrating consumer wearables and advanced imaging in evaluating ventricular ectopy and its evolving role in risk stratification for patients with MVP, even in the absence of overt symptoms. Full article

Sugar apple (Annona squamosa) is prized for its excellent taste, rich nutrition, and diverse uses, making it valuable for both fresh consumption and medicinal purposes. Predominantly found in tropical regions of the Americas and Asia, its harvesting remains labor-intensive in orchard settings, resulting in low efficiency and high costs. This study investigates the use of computer vision for sugar apple instance segmentation and introduces an improved deep learning model, GCE-YOLOv9-seg, specifically designed for orchard conditions. The model incorporates Gamma Correction (GC) to enhance image brightness and contrast, improving target region identification and feature extraction in orchard settings. An Efficient Multiscale Attention (EMA) mechanism was added to strengthen feature representation across scales, addressing sugar apple variability and maturity differences. Additionally, a Convolutional Block Attention Module (CBAM) refined the focus on key regions and deep semantic features. The model’s performance was evaluated on a self-constructed dataset of sugar apple instance segmentation images captured under natural orchard conditions. The experimental results demonstrate that the proposed GCE-YOLOv9-seg model achieved an F1 score (F1) of 90.0%, a precision (P) of 89.6%, a recall (R) level of 93.4%, a mAP@0.5 of 73.2%, and a mAP@[0.5:0.95] of 73.2%. Compared to the original YOLOv9-seg model, the proposed GCE-YOLOv9-seg showed improvements of 1.5% in the F1 score and 3.0% in recall for object detection, while the segmentation task exhibited increases of 0.3% in mAP@0.5 and 1.0% in mAP@[0.5:0.95]. Furthermore, when compared to the latest model YOLOv12-seg, the proposed GCE-YOLOv9-seg still outperformed with an F1 score increase of 2.8%, a precision (P) improvement of 0.4%, and a substantial recall (R) boost of 5.0%. In the segmentation task, mAP@0.5 rose by 3.8%, while mAP@[0.5:0.95] demonstrated a significant enhancement of 7.9%. This method may be directly applied to sugar apple instance segmentation, providing a promising solution for automated sugar apple detection in natural orchard environments. Full article

Accurate fruit target identification is crucial for autonomous harvesting robots in complex orchards, where image segmentation using deep learning networks plays a key role. To address the trade-off between segmentation accuracy and inference efficiency, this study proposes LGVM-YOLOv8n, a lightweight instance segmentation model based on YOLOv8n-seg. LGVM is an acronym for lightweight, GSConv, VoVGSCSP, and $MPDIoU$, highlighting the key improvements incorporated into the model. The proposed model integrates three key improvements: (1) the GSConv module, which enhances feature interaction and reduces computational cost; (2) the VoVGSCSP module, which optimizes multi-scale feature representation for small objects; and (3) the $MPDIoU$ loss function, which improves target localization accuracy, particularly for occluded fruits. Experimental results show that LGVM-YOLOv8n reduces computational cost by 9.17%, decreases model weight by 7.89%, and improves inference speed by 16.9% compared to the original YOLOv8n-seg. Additionally, segmentation accuracy under challenging conditions (front-light, back-light, and occlusion) improves by 3.28% to 4.31%. Deployment tests on an edge computing platform demonstrate real-time performance, with inference speed accelerated to 0.084 s per image and frame rate increased to 28.73 FPS. These results validated the model’s robustness and adaptability, providing a practical solution for apple-picking robots in complex orchard environments. Full article

Utilizing remote sensing models to monitor apple orchards facilitates the industrialization of agriculture and the sustainable development of rural land resources. This study enhanced the DeepLabv3+ model to achieve superior performance in apple orchard identification by incorporating ResNet, optimizing the algorithm, and adjusting hyperparameter configuration using the PIE-Engine cloud platform. GF-6 PMS images were used as the data source, and Qixia City was selected as the case study area for demonstration. The results indicate that the accuracies of apple orchard identification using the proposed DeepLabv3+_34, DeepLabv3+_50, and DeepLabv3+_101 reached 91.17%, 92.55%, and 94.37%, respectively. DeepLabv3+_101 demonstrated superior identification performance for apple orchards compared with ResU-Net and LinkNet, with an average accuracy improvement of over 3%. The identified area of apple orchards using the DeepLabv3+_101 model was 629.32 km², accounting for 31.20% of Qixia City’s total area; apple orchards were mainly located in the western part of the study area. The innovation of this research lies in combining image annotation and object-oriented methods during training, improving annotation efficiency and accuracy. Additionally, an enhanced DeepLabv3+ model was constructed based on GF-6 satellite images and the PIE-Engine cloud platform, exhibiting superior performance in feature expression compared with conventional machine learning classification and recognition algorithms. Full article

The growing demand for natural preservatives in the food industry has highlighted the importance of essential oils (EOs), despite their limitations related to volatility and oxidative instability. This study addresses these challenges by developing pectin-based microcapsules for encapsulating lemon essential oil (LEO) using ultrasound-assisted ionotropic gelation. The EO, extracted from Citrus limon (Eureka variety), exhibited a high limonene content (56.18%) and demonstrated significant antioxidant (DPPH IC50: 28.43 ± 0.14 µg/mL; ABTS IC50: 35.01 ± 0.11 µg/mL) and antifungal activities, particularly against A. niger and Botrytis spp. Encapsulation efficiency improved to 82.3% with ultrasound pretreatment, and SEM imaging confirmed spherical, uniform capsules. When applied to fresh-cut apples, LEO-loaded capsules significantly reduced browning (browning score: 1.2 ± 0.3 vs. 2.8 ± 0.2 in control), microbial load (4.9 ± 0.2 vs. 6.5 ± 0.4 log CFU/g), and weight loss (4.2% vs. 6.4%) after 10 days of storage at 4 °C. These results underscore the potential of ultrasound-enhanced pectin encapsulation for improving EO stability and efficacy in food preservation systems. Full article

This study developed non-destructive technology for predicting apple size to determine optimal harvest timing of field-grown apples. RGBD images were collected in field environments with fluctuating light conditions, and deep learning techniques were integrated to analyze morphometric parameters. After training various models, the EfficientDet D4 and Mask R-CNN ResNet101 models demonstrated the highest detection accuracy. Morphometric metrics were measured by linking boundary box information with 3D depth information to determine horizontal and vertical diameters. Without occlusion, mean absolute percentage error (MAPE) using boundary box-based methods was 6.201% and 5.164% for horizontal and vertical diameters, respectively, while mask-based methods achieved improved accuracy with MAPE of 5.667% and 4.921%. Volume and weight predictions showed MAPE of 7.183% and 6.571%, respectively. For partially occluded apples, amodal segmentation was applied to analyze morphometric parameters according to occlusion rates. While conventional models showed increasing MAPE with higher occlusion rates, the amodal segmentation-based model maintained consistent accuracy regardless of occlusion rate, demonstrating potential for automated harvest systems where fruits are frequently partially obscured by leaves and branches. Full article

Vision–language models (VLMs) have achieved remarkable success in natural image domains, yet their potential remains underexplored in agriculture due to the lack of high-quality, joint image–text datasets. To address this limitation, we introduce VL-PAW (Vision–Language dataset for Pear, Apple, and Weed), a dataset comprising 3.9 K image–caption pairs for two key agricultural tasks: weed species classification and fruit inspection. We fine-tune the CLIP model on VL-PAW and gain several insights. First, the model demonstrates impressive zero-shot performance, achieving 98.21% accuracy in classifying coarse labels. Second, for fine-grained categories, the vision–language model outperforms vision-only models in both few-shot settings and entire dataset training (1-shot: 56.79%; 2-shot: 72.82%; 3-shot: 74.49%; 10-shot: 83.85%). Third, using intuitive captions enhances fine-grained fruit inspection performance compared to using class names alone. These findings demonstrate the applicability of VLMs in future agricultural querying systems. Full article

Accurate object detection is a fundamental component of autonomous apple-picking systems. In response to the insufficient recognition performance and poor generalization capacity of existing detection algorithms under unstructured orchard scenarios, we constructed a customized apple image dataset captured under varying illumination conditions and introduced an improved detection architecture, YOLO11-ARAF, derived from YOLO11. First, to enhance the model’s ability to capture apple-specific features, we replaced the original C3k2 module with the CARConv convolutional layer. Second, to reinforce feature learning in visually challenging orchard environments, the enhanced attention module AFGCAM was embedded into the model architecture. Third, we applied knowledge distillation to transfer the enhanced model to a compact YOLO11n framework, maintaining high detection efficiency while reducing computational cost, and optimizing it for deployment on devices with limited computational resources. To assess our method’s performance, we conducted comparative experiments on the constructed apple image dataset. The improved YOLO11-ARAF model attained 89.4% accuracy, 86% recall, 92.3% mAP@50, and 64.4% mAP@50:95 in our experiments, which are 0.3%, 1.1%, 0.72%, and 2% higher than YOLO11, respectively. Furthermore, the distilled model significantly reduces parameters and doubles the inference speed (FPS), enabling rapid and precise apple detection in challenging orchard settings with limited computational resources. Full article

In order to improve the perception accuracy of the apple tree fruit recognition model and to reduce the model size, a lightweight apple target recognition method based on an improved YOLOv5s artificial intelligence algorithm was proposed, and relevant experiments were designed. The Depthwise Separable Convolution (DWConv) module has many advantages: (1) It has high computational efficiency, reducing the number of parameters and calculations in the model; (2) It makes the model lightweight and easy to deploy in hardware; (3) DWConv can be combined with other modules to enhance the multi-scale feature extraction capability of the detection network and improve the ability to capture multi-scale information; (4) It balances the detection accuracy and speed of the model; (5) DWConv can flexibly adapt to different network structures. Because of its efficient computing modes, lightweight design, and flexible structural adaptation, the DWConv module has significant advantages in multi-scale feature extraction, real-time performance improvement, and small-object detection. Therefore, this method improves the original YOLOv5s network architecture by replacing the embedded Depthwise Separable Convolution in its Backbone network, which reduces the size and parameter count of the model while ensuring detection accuracy. The experimental results show that for the test-set images, the proposed improved model has an average recognition accuracy of 92.3% for apple targets, a recognition time of 0.033 s for a single image, and a model volume of 11.1 MB. Compared with the original YOLOv5s model, the average recognition accuracy was increased by 0.8%, the recognition speed was increased by 23.3%, and the model volume was compressed by 20.7%, effectively achieving lightweight improvement of the apple detection model and improving the accuracy and speed of detection. The detection algorithm proposed in the study can be extended to the intelligent measurement of apple biological and physical characteristics, including for size measurement, shape analysis, and color analysis. The proposed method can improve the intelligence level of orchard management and horticultural technology, reduce labor costs, assist precision agriculture technology, and promote the transformation of the horticultural industry toward sustainable development. Full article