Multi-Scale Feature Fusion Based RT-DETR for Tomato Leaf Disease Detection in Complex Backgrounds.

Abstract

In this study, we propose a multi-scale feature fusion network based on an improved RT-DETR model for the efficient detection of tomato leaf disease. Our model combines the multi-scale extended residual module by capturing contextual information at various scales and the multi-scale feature pyramid network by integrating feature information from different levels, which improves feature extraction capability and reduces the interference of complex backgrounds on feature extraction, thereby improving information transmission efficiency and the accuracy of the model. In addition, the novel loss function called adaptive focal loss (AFL) was used, which is based on traditional focal loss with the introduction of attenuation factors to focus the model's attention to high-loss features to alleviate overfitting and of dynamic weight adjustment mechanisms to focus on the more important features during the training process to improve the overall learning performance. Another significant advantage of AFL is that it can more efficiently improve the detection accuracy on imbalanced datasets than on balanced datasets. These innovations optimized the learning strategy of the model, making AP@0.50 up to 97.9% on detecting the categories of tomato diseases. In addition, this model also achieves the high detection accuracy of 85.4% on other crop diseases. These results provide valuable references for agriculture applications.

Plant phenotyping relevance

トマト葉の病害状態を画像から検出する深層学習手法を提案・改良しており、植物病害表現型の抽出法が研究の中心である。

Code and data availability

The paper constructs its LAB and ENV tomato leaf disease detection datasets by selecting and adjusting samples from two public Roboflow Universe datasets (cited as refs 30 and 31), which are public image inputs directly underlying this paper's phenotyping measurements. Both Roboflow URLs are given in the reference list