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[学术文献 ] TDDet: A novel lightweight and efficient tea disease detector 进入全文
Computers and Electronics in Agriculture 期刊
This paper proposes a lightweight and efficient detector called TDDet to quickly and accurately detect tea diseases. TDDet is mainly composed of two key innovations: feature extraction and feature aggregation. For feature extraction, we use lightweight depthwise separable convolution to reduce the computational load and enhance the ability to extract key local features in images of tea diseases. In addition, attention mechanisms including channel-, spatial-, and self-attentions, are employed to enable the model to focus on the most important parts of tea diseases, thereby improving the performance of the model. For feature aggregation, we propose a novel Cross-scale Feature Fusion (CFF) module to focus on tea disease areas, boosting the model’s sensitivity to feature details. Based on CFF, TDDet repeatedly fuses multiscale features of different levels in a top-down and bottom-up manner, enhancing feature representation capability. Besides, a lightweight and efficient upsampling module, called Dysample, is used to reduce computational costs and improve model performance by dynamically adjusting the sampling rate of feature maps. Experimental results demonstrate that TDDet with fewer parameters outperforms other state-of-the-art object detection models, enabling fast and accurate identification of tea diseases.
[学术文献 ] Research on the Yunnan Large-Leaf Tea Tree Disease Detection Model Based on the Improved YOLOv10 Network and UAV Remote Sensing 进入全文
Applied Sciences 期刊
In response to issues such as low resolution, severe occlusion, and insufficient fine-grained feature extraction in tea plantation disease detection, this study proposes an improved YOLOv10 network based on low-altitude unmanned aerial vehicle remote sensing for the detection of diseases in Yunnan large-leaf tea trees. Through the use of a Shape-IoU optimized loss function, a Wavelet Transform Convolution to enhance the network’s Backbone, and a Histogram Transformer to optimize the network’s Neck, the detection accuracy and localization precision of disease targets were significantly improved. Through testing of common diseases, the research results indicate that, for the improved YOLOv10 network, the Box Loss, Cls Loss, and DFL Loss were reduced by 15.94%, 13.16%, and 8.82%, respectively, in the One-to-Many Head, and by 14.58%, 17.72%, and 8.89%, respectively, in the One-to-One Head. Compared to the original YOLOv10 network, precision, recall, and F1 increased by 3.4%, 10.05%, and 6.75%, respectively. The improved YOLOv10 network not only effectively addresses phenomena such as blurry images, complex backgrounds, strong illumination, and occlusion in disease detection, but also demonstrates high levels of precision and recall, thereby providing robust technological support for precision agriculture and decision-making, and to a certain extent promoting the development of agricultural modernization.
[学术文献 ] WMC-RTDETR: a lightweight tea disease detection model 进入全文
Frontiers in Plant Science 期刊
Tea pest and disease detection is crucial in tea plantation management, however, challenges such as multi-target occlusion and complex background impact detection accuracy and efficiency. To address these issues, this paper proposes an improved lightweight model, WMC-RTDETR, based on the RT-DETR model. The model significantly enhances the ability to capture multi-scale features by introducing wavelet transform convolution, improving the feature extraction accuracy in complex backgrounds, and increasing detection efficiency while reducing the number of model parameters. Combined with multiscale multihead self-attention, global feature fusion across scales is realized, which effectively overcomes the shortcomings of traditional attention mechanisms in small target detection. Additionally, a context-guided spatial feature reconstruction feature pyramid network is designed to refine the target feature reconstruction through contextual information, thereby improving the robustness and accuracy of target detection in complex scenes. Experimental results show that the proposed model achieves 97.7% and 83.1% respectively in mAP50 and mAP50:95 indicators, which outperform the original model. In addition, the number of parameters and floating-point operations are reduced by 35.48% and 40.42% respectively, enabling highly efficient and accurate detection of pests and diseases in complex scenarios. Furthermore, this paper successfully deploys the lightweight model on the Raspberry Pi platform, which proves that it has good real-time performance in resource-constrained embedded environments, providing a practical solution for low-cost disease monitoring in agricultural scenarios.
[会议论文 ] TAME-Faster R-CNN model for Image-based Tea Diseases Detection 进入全文
CCECE 2024 会议
Tea, one of the most consumed non-alcoholic beverages in the world, plays an essential role in the agricultural economy. Nevertheless, it is threatened by various diseases, resulting in critical yields and economic losses. Nowadays, image processing techniques and machine vision algorithms are used to detect tea diseases. However, the existing techniques do not consider the varying lighting (such as shadow) conditions in the data set, which makes the techniques less efficient and robust. Therefore, this paper aims to address these issues by proposing an efficient technique called, TAME-Faster R-CNN. The proposed method combines a trainable Attention Mechanism for Explanations (TAME) module with the backbone network of the Faster R-CNN framework to detect three types of tea diseases (Anthracnose, Brown leaf spot, and Tea white scab). The experimental results and analysis show that the proposed algorithm achieved mAP values of 98.3% to detect Anthracnose, 64.8% to detect Brown leaf spots, and 75.6% to detect white scab diseases and performed better than the state-of-the-art technique. Total mAP is almost 10%, 23% and 1% higher compared to Yolov5, Yolov7 and original Faster R-CNN, respectively. Compared to the original framework of Faster R-CNN, TAME-Faster R-CNN with ResNet101 has improved the precision and F1 score on average by 6% and 4.3%, respectively. Hence, the integration of this technique can effectively detect tea lesion diseases.
[学术文献 ] TeaViTNet: Tea Disease and Pest Detection Model Based on Fused Multiscale Attention 进入全文
Agronomy-Basel 期刊
The tea industry, as one of the most globally important agricultural products, is characterized by pests and diseases that pose a serious threat to yield and quality. These diseases and pests often present different scales and morphologies, and some pest and disease target sizes can be tiny and difficult to detect. To solve these problems, we propose TeaViTNet, a multi-scale attention-based tea pest and disease detection model that combines CNNs and Transformers. First, MobileViT is used as the feature extraction backbone network. MobileViT captures and analyzes the tiny pest and disease features in the image via a self-attention mechanism and global feature extraction. Second, the EMA-PANet network is introduced to optimize the model’s learning and attention to the Apolygus lucorum and leaf blight regions via an efficient multi-scale attention module with cross-space learning, which improves the model’s ability to understand multi-scale information. In addition, RFBNet is embedded in the module to further expand the perceptual range and effectively capture the information of tiny features in tea leaf images. Finally, the ODCSPLayer convolutional block is introduced, aiming to focus on acquiring richer gradient flow information. The experimental results show that the TeaViTNet model proposed in this paper has an average accuracy of 89.1%, which is a significant improvement over the baseline network MobileViT and is capable of accurately detecting Apolygus lucorum and leaf blight of different scales and complexities.
[相关专利 ] A DEEP CONVOLUTIONAL NEURAL NETWORK MODEL FOR DETECTION OF DISEASE IN TEA PLANT 进入全文
印度专利
一种用于检测茶树病害的深度卷积神经网络系统和方法，该系统（100）包括：10个卷积层、6个平均池层、1个完全连接层、1个输出层、与1个或多个卷积层通信耦合的maxpool层；与1个或多个Max池层通信耦合的完全连接层；以及与1个或多个完全连接层通信耦合的输出层。在5867张增强茶叶图像上训练显示出91.49%的准确率。
