Xiaoyue Duan
ABSTRACT
In recent years, the advancement of deep learning technology and convolutional neural networks has presented novel solutions for the swift and precise detection of crop diseases. To address issues such as low segmentation accuracy in plant leaf images amidst complex backgrounds, inadequacies in recognition and detection models, insufficient data sets, and sluggish training speed, we propose a method for potato leaf image segmentation and recognition based on an enhanced DeeplabV3+ network. Specifically, we substitute the model encoder's backbone feature extraction network with MobileNetV2 to significantly reduce computational requirements while enhancing calculation speed. Then, the attention mechanism module was incorporated into the backbone feature extraction network and decoder to further enhance the model's edge recognition capability and improve its segmentation accuracy. Simultaneously, this study devised a classification model to achieve potato leaf disease segmentation and detection amidst complex backgrounds. In the initial stage, an enhanced DeeplabV3+ was employed for segmenting potato leaves in intricate environments. Subsequently, ResNet101, ResNet50, and ShuffleNetV2 classification models were utilized in the second stage for accurately classifying potato leaves. Finally, the two-stage model was integrated to effectively segment and detect potato leaf diseases amidst intricate backgrounds. The experimental findings demonstrate that the enhanced DeeplabV3+ segmentation model achieves a remarkable Mean Intersection over Union (MIoU) of 82.6%, an impressive Average Pixel Accuracy (MPA) of 90.65%, and a commendable accuracy rate of 94.72% for the second stage classification model. In conclusion, this approach presents a novel research avenue for accurately segmenting and detecting potato leaf diseases in challenging background scenarios, thereby contributing to advancements in this field.
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Index Terms
- Potato Disease Identification Method based on Improved DeeplabV3+ Network
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