Existing disease detection models for deep learning-based monitoring and prevention of pepper diseases face challenges in accurately identifying and preventing diseases due to inter-crop occlusion and various complex backgrounds. To address this issue, we propose a modified YOLOv7-GCA model based on YOLOv7 for pepper disease detection, which can effectively overcome these challenges. The model introduces three key enhancements: Firstly, lightweight GhostNetV2 is used as the feature extraction network of the model to improve the detection speed. Secondly, the Cascading fusion network (CFNet) replaces the original feature fusion network, which improves the expression ability of the model in complex backgrounds and realizes multi-scale feature extraction and fusion. Finally, the Convolutional Block Attention Module (CBAM) is introduced to focus on the important features in the images and improve the accuracy and robustness of the model. This study uses the collected dataset, which was processed to construct a dataset of 1259 images with four types of pepper diseases: anthracnose, bacterial diseases, umbilical rot, and viral diseases. We applied data augmentation to the collected dataset, and then experimental verification was carried out on this dataset. The experimental results demonstrate that the YOLOv7-GCA model reduces the parameter count by 34.3% compared to the YOLOv7 original model while improving 13.4% in mAP and 124 frames/s in detection speed. Additionally, the model size was reduced from 74.8 MB to 46.9 MB, which facilitates the deployment of the model on mobile devices. When compared to the other seven mainstream detection models, it was indicated that the YOLOv7-GCA model achieved a balance between speed, model size, and accuracy. This model proves to be a high-performance and lightweight pepper disease detection solution that can provide accurate and timely diagnosis results for farmers and researchers.

Lychee is an economically important crop with widespread popularity. However, lychee diseases significantly impact both the yield and fruit quality of lychee. Existing lychee disease detection models face challenges such as large parameter sizes, slow processing speeds, and deployment complexities. To address these challenges, this paper proposes an improved lightweight network, named YOLOv7-MGPC (YOLOv7-Mosaic-GhostNet-Pruning-CBAM), that enables real-time lychee disease detection. In this study, we collected datasets of lychee diseases, covering four types of leaf diseases, and employed Mosaic data augmentation for data preprocessing. Building upon the YOLOv7 framework, we replaced the original backbone network with the lightweight GhostNetV1 and applied channel pruning to effectively reduce the parameter overhead. Subsequently, an attention mechanism called CBAM was incorporated to enhance the detection accuracy. The resultant model was then deployed to edge devices (Nvidia Jetson Nano) for real-world applications. Our experiments showed that our enhanced YOLOv7 variant outperforms the original model by a large margin, achieving a speed increase from 120 frames/s to 217 frames/s while maintaining an accuracy of 88.6%. Furthermore, the parameter size was substantially reduced from 36.5 M to 7.8 M, which firmly demonstrates the effectiveness of our methods in enabling model deployment on edge devices for lychee disease detection.

Rice lodging seriously affects rice quality and production. Traditional manual methods of detecting rice lodging are labour-intensive and can result in delayed action, leading to production loss. With the development of the Internet of Things (IoT), unmanned aerial vehicles (UAVs) provide imminent assistance for crop stress monitoring. In this paper, we proposed a novel lightweight detection system with UAVs for rice lodging. We leverage UAVs to acquire the distribution of rice growth, and then our proposed global attention network (GloAN) utilizes the acquisition to detect the lodging areas efficiently and accurately. Our methods aim to accelerate the processing of diagnosis and reduce production loss caused by lodging. The experimental results show that our GloAN can lead to a significant increase in accuracy with negligible computational costs. We further tested the generalization ability of our GloAN and the results show that the GloAN generalizes well in peers’ models (Xception, VGG, ResNet, and MobileNetV2) with knowledge distillation and obtains the optimal mean intersection over union (mIoU) of 92.85%. The experimental results show the flexibility of GloAN in rice lodging detection.

With computer vision booming ahead on the high-way, image classification and object detection have greatly improved, which are able to accurately classify large datasets even better than human beings. There are many fields in which computer vision gets involved, including terrestrial and aerial mapping of natural resources, crop monitoring, precision agriculture, quality control and classification of processing lines, and process automation. The application of computer vision in agriculture has increased considerably in recent years. However, traditional computer vision methods require complex preprocessing and algorithms, which is time-consuming and labor-intensive. In particular, the effectiveness of this method relies mainly on the accuracy of the artificial design features and the learning algorithm. In this paper, we propose a object detection algorithm based on Faster-RCNN to identify the stage of the cotton leaf(a period that cotton leaves grow). The algorithm consists of three main parts: convolutional neural network based on ResNet101, the extraction of candidate regions of the target based on RPNs, and the identification of detection object. Based on many experiments, the algorithm in this paper is able to identify the cotton leaf stage, and the precision surpasses 80%.