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- Title
CLASSIFYING EARLY BLIGHT AND LATE BLIGHT OF POTATO BASED ON CONVOLUTION NEURAL NETWORK.
- Authors
Haixia Qi; Zhenxin Lin; Yifeng Zhu; Jianjun Hao; Yubin Lan
- Abstract
Early and late blight are two of the most common potato diseases. Intelligent tools for automatically detecting these two diseases can benefit farmers and agricultural extension officers. However, it remains a challenge to use traditional image processing methods to recognize these diseases. Convolution neural network (CNN) is an advanced methodology in computer vision, which shows great promise in image classification. This article explores CNN models to classify potato early blight and late blight based on leaf images. This research task has three challenges: lack of adequate datasets, noise in existing data, and the construction of a model that handles variability in image backgrounds. This research designs a CNN model M_Net based on MobileNetV1 network and uses different dataset sources in the construction of a CNN model with a strong generalization ability to identify disease leaves and healthy leaves. Furthermore, this article adds a new dataset to the field by supplying the model with potato leaf images. The results show that the CNN model achieves the highest accuracy with low calculation cost compared to some classical models and the final model has a strong generalization capacity.
- Publication
Applied Engineering in Agriculture, 2022, Vol 38, Issue 5, p817
- ISSN
0883-8542
- Publication type
Article
- DOI
10.13031/aea.13732