In the realm of automated apple picking operations, the real-time monitoring of apple maturity and diameter characteristics is of paramount importance. Given the constraints associated with feature detection of apples in automated harvesting, this study proposes a machine vision-based methodology for the accurate identification of Fuji apples’ maturity and diameter. Firstly, maturity level detection employed an improved YOLOv5s object detection model. The feature fusion section of the YOLOv5s network was optimized by introducing the cross-level partial network module VoVGSCSP and lightweight convolution GSConv. This optimization aimed to improve the model’s multiscale feature information fusion ability while accelerating inference speed and reducing parameter count. Within the enhanced feature fusion network, a dual attention mechanism combining channel and spatial attention (GAM) was introduced to refine the color and texture feature information of apples and to increase spatial position feature weights. In terms of diameter determination, the contours of apples are obtained by integrating the dual features of color and depth images within the target boxes acquired using the maturity detection model. Subsequently, the actual area of the apple contour is determined by calculating the conversion relationship between pixel area and real area at the current depth value, thereby obtaining the diameter of the apples. Experimental results showed that the improved YOLOv5s model achieved an average maturity level detection precision of 98.7%. Particularly noteworthy was the detection accuracy for low maturity apples, reaching 97.4%, surpassing Faster R-CNN, Mask R-CNN, YOLOv7, and YOLOv5s models by 6.6%, 5.5%, 10.1%, and 11.0% with a real-time detection frame rate of 155 FPS. Diameter detection achieved a success rate of 93.3% with a real-time detection frame rate of 56 FPS and an average diameter deviation of 0.878 mm for 10 apple targets across three trials. Finally, the proposed method achieved an average precision of 98.7% for online detection of apple maturity level and 93.3% for fruit diameter features. The overall real-time inference speed was approximately 56 frames per second. These findings indicated that the method met the requirements of real-time mechanical harvesting operations, offering practical importance for the advancement of the apple industry.

Aiming at difficult image acquisition and low recognition accuracy of two rice canopy pests, rice stem borer and rice leaf roller, we constructed a GA-Mask R-CNN (Generative Adversarial Based Mask Region Convolutional Neural Network) intelligent recognition model for rice stem borer and rice leaf roller, and we combined it with field monitoring equipment for them. Firstly, based on the biological habits of rice canopy pests, a variety of rice pest collection methods were used to obtain the images of rice stem borer and rice leaf roller pests. Based on different segmentation algorithms, the rice pest images were segmented to extract single pest samples. Secondly, the bug generator based on a generative adversarial network strategy improves the sensitivity of the classification network to the bug information, generates the pest information images in the real environment, and obtains the sample dataset for deep learning through multi-way augmentation. Then, through adding channel attention ECA module in Mask R-CNN and improving the connection of residual blocks in the backbone network ResNet101, the recognition accuracy of the model is improved. Finally, the GA-Mask R-CNN model was tested on a multi-source dataset with an average precision (AP) of 92.71%, recall (R) of 89.28% and a balanced score F1 of 90.96%. The average precision, recall, and balanced score F1 are improved by 7.07, 7.65, and 8.83%, respectively, compared to the original Mask R-CNN. The results show that the GA-Mask R-CNN model performance indexes are all better than the Mask R-CNN, the Faster R-CNN, the SSD, the YOLOv5, and other network models, which can provide technical support for remote intelligent monitoring of rice pests.

Currently, intelligent pest monitoring systems transmit entire monitoring images to cloud servers for analysis. This approach not only consumes significant bandwidth and increases monitoring costs, but also struggles with accurately recognizing small-target and overlapping pests. To overcome these challenges, this paper introduces a two-stage multi-feature fusion small-target pest detection algorithm based on edge computing devices, termed TFEMRNet. The algorithm initially conducts semantic segmentation on an edge processor, followed by uploading the segmented images to a cloud server for target identification. Specifically, the semantic segmentation model TFENet incorporates a Multi-Attention Channel Aggregation (MACA) module, which integrates semantic features from EfficientNet-Pest and Swin Transformer, thereby enhancing the model’s ability to extract features of small-target pests. Experimental results demonstrate that TFEMRNet surpasses models such as YOLOv11, Fast R-CNN, and Mask R-CNN on small-target pest datasets, achieving precision of 96.75%, recall of 96.45%, and an F1 score of 95.60%. Notably, the TFENet model within TFEMRNet attains an IoU of 91.63% in semantic segmentation accuracy, outperforming other segmentation models such as U-Net and PSPNet. These findings affirm TFEMRNet’s superior efficacy in small-target pest detection, offering an effective solution for agricultural pest monitoring.

The segmentation of individual pests is a prerequisite for pest feature extraction and identification. To address the issue of pest adhesion in the apple orchard pest identification process, this research proposed a pest adhesion image segmentation method based on Gaussian Mixture Model with Density and Curvature Weighting (GMM-DC). First, in the HSV color space, an image was desaturated by adjusting the hue and inverting to mitigate threshold crossing points. Subsequently, threshold segmentation and contour selection methods were used to separate the image background. Next, a shape factor was introduced to determine the regions and quantities of adhering pests, thereby determining the number of model clustering clusters. Then, point cloud reconstruction was performed based on the color and spatial distribution features of the pests. To construct the GMM-DC segmentation model, a spatial density (SD) and spatial curvature (SC) information function were designed and embedded in the GMM. Finally, experimental analysis was conducted on the collected apple orchard pest images. The results showed that GMM-DC achieved an average accurate segmentation rate of 95.75%, an average over-segmentation rate of 2.83%, and an average under-segmentation rate of 1.42%. These results significantly outperformed traditional image segmentation methods. In addition, the original and improved Mask R-CNN models were used as recognition models, and the mean Average Precision was used as the evaluation metric. Recognition experiments were conducted on pest images with and without the proposed method. The results show the mean Average Precision for pest images segmented with the proposed method as 92.43% and 96.75%. This indicates an improvement of 13.01% and 12.18% in average recognition accuracy, respectively. The experimental results demonstrate that this method provides a theoretical and methodological foundation for accurate pest identification in orchards.

In the process of plant protection for fruit trees using rotary-wing UAVs, challenges such as droplet drift, insufficient canopy penetration, and low agrochemical utilization efficiency remain prominent. Among these, the uncertainty in the spatio-temporal distribution of downwash airflow is a key factor contributing to non-uniform droplet deposition and increased drift. To address this issue, we developed a wind field numerical simulation model based on an improved hierarchical leaf density model to clarify the spatio-temporal characteristics of downwash airflow, the scale of turbulence regions, and their effects on internal canopy airflow under varying flight altitudes and different rotor speeds. Field experiments were conducted in orchards to validate the accuracy of the model. Simulation results showed that the average error between the simulated and measured wind speeds inside the canopy was 8.4%, representing a 42.11% reduction compared to the non-hierarchical model and significantly improving the prediction accuracy. The coefficient of variation (CV) was 0.26 in the middle canopy layer and 0.29 in the lower layer, indicating a decreasing trend with an increasing canopy height. We systematically analyzed the variation in turbulence region scales under different flight conditions. This study provides theoretical support for optimizing UAV operation parameters to improve droplet deposition uniformity and enhance agrochemical utilization efficiency.