Abstract
Fine-grained image classification is a challenging task because of the difficulty in identifying discriminant features, it is not easy to find the subtle features that fully represent the object. In the fine-grained classification of crop disease, visual disturbances such as light, fog, overlap, and jitter are frequently encountered. To explore the influence of the features of crop leaf images on the classification results, a classification model should focus on the more discriminative regions of the image while improving the classification accuracy of the model in complex scenes. This paper proposes a novel attention mechanism that effectively utilizes the informative regions of an image, and describes the use of transfer learning to quickly construct several fine-grained image classification models of crop disease based on this attention mechanism. This study uses 58,200 crop leaf images as a dataset, including 14 different crops and 37 different categories of healthy/diseased crops. Among them, different diseases of the same crop have strong similarities. The NASNetLarge fine-grained classification model based on the proposed attention mechanism achieves the best classification effect, with an F1 score of up to 93.05%. The results show that the proposed attention mechanism effectively improves the fine-grained classification of crop disease images.
Highlights
Outbreaks of crop disease have a significant impact on the yield of agricultural production
We propose a novel attention mechanism and use transfer learning to quickly build several fine-grained image classification models of crop diseases based on the attention mechanism, so as to solve the problem that the accuracy of convolutional neural network (CNN) model in complex scenes is low due to visual interference in practical applications
Proposed Model Based on the pre-trained model described in Section transfer learning and the attention mechanism introduced in Section attention mechanism, this paper proposes a fine-grained classification model based on the attention mechanism (Figure 4)
Summary
Outbreaks of crop disease have a significant impact on the yield of agricultural production. Large-scale disease outbreaks destroy crops that have taken considerable efforts to grow, causing irreparable damage. Even without large-scale disease outbreaks, small-scale emergence can cause serious losses to crop yield and quality (Mutka and Bart, 2015). With advances in image classification technology, researchers in the field of crop disease have gradually come to use deep learning approaches (Ramcharan et al, 2017; Fuentes et al, 2018; Liu B. et al, 2020). Research on the general classification of crop diseases has made several remarkable achievements in terms of better classification. For some fine-grained crop leaf diseases, there are still many difficulties
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