Abstract
Natural rubber is an essential raw material for industrial products and plays an important role in social development. A variety of diseases can affect the growth of rubber trees, reducing the production and quality of natural rubber. Therefore, it is of great significance to automatically identify rubber leaf disease. However, in practice, different diseases have complex morphological characteristics of spots and symptoms at different stages and scales, and there are subtle interclass differences and large intraclass variation between the symptoms of diseases. To tackle these challenges, a group multi-scale attention network (GMA-Net) was proposed for rubber leaf disease image recognition. The key idea of our method is to develop a group multi-scale dilated convolution (GMDC) module for multi-scale feature extraction as well as a cross-scale attention feature fusion (CAFF) module for multi-scale attention feature fusion. Specifically, the model uses a group convolution structure to reduce model parameters and provide multiple branches and then embeds multiple dilated convolutions to improve the model’s adaptability to the scale variability of disease spots. Furthermore, the CAFF module is further designed to drive the network to learn the attentional features of multi-scale diseases and strengthen the disease features fusion at different scales. In this article, a dataset of rubber leaf diseases was constructed, including 2,788 images of four rubber leaf diseases and healthy leaves. Experimental results show that the accuracy of the model is 98.06%, which was better than other state-of-the-art approaches. Moreover, the model parameters of GMA-Net are only 0.65 M, and the model size is only 5.62 MB. Compared with MobileNetV1, V2, and ShuffleNetV1, V2 lightweight models, the model parameters and size are reduced by more than half, but the recognition accuracy is also improved by 3.86–6.1%. In addition, to verify the robustness of this model, we have also verified it on the PlantVillage public dataset. The experimental results show that the recognition accuracy of our proposed model is 99.43% on the PlantVillage dataset, which is also better than other state-of-the-art approaches. The effectiveness of the proposed method is verified, and it can be used for plant disease recognition.
Highlights
The rubber tree is one of the most important economic crops in the tropics, and the planting area of rubber trees in China is more than 1.16 million hectares, more than half of which are planted in Hainan Province (Ali et al, 2020; Li and Zhang, 2020)
Inspired by the above research, we proposed a deep neural network model, namely, group multi-scale attention network (GMA-Net)
The enhanced rubber disease dataset and PlantVillage (Hughes and Salathe, 2015) public dataset are divided into three groups, namely, the training set (60%), the validation set (20%), and the test set (20%)
Summary
The rubber tree is one of the most important economic crops in the tropics, and the planting area of rubber trees in China is more than 1.16 million hectares, more than half of which are planted in Hainan Province (Ali et al, 2020; Li and Zhang, 2020). The milky latex extracted from the tree is the primary source of natural rubber, which is an essential raw material for industrial products. Rubber leaf diseases cause annual losses of approximately 25% of the total yield of natural rubber and cause significant economic losses. A variety of diseases can affect the growth of rubber trees, reducing the production of natural rubber and seriously hindering the development of the natural rubber industry. The identification and diagnosis of rubber leaf diseases (e.g., powdery mildew disease, rubber tree anthracnose, periconla leaf spot disease, and Abnormal Leaf Fall Disease) are of great significance for increasing the yield of natural rubber and have received extensive attention from rubber planting workers and experts on disease and pest control. Manual identification and diagnosis are time-consuming and laborious in practice, and the recognition accuracy does not satisfy the requirement
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