Jianyu Wu
ABSTRACT
Agriculture is not only China's primary industry but also the foundation of the national economy. The amount and quality of agricultural products are inextricably linked to people's daily life. The outbreak of pests and diseases in the field has a great impact on agricultural production, so it can be seen that the prevention and control of pests and diseases are very important. In order to control crop diseases and pests, this paper combines emerging machine learning techniques based on a large number of crop pest and disease pictures, and introduces two kinds of convolutional neural networks------AlexNet and GoogleNet to detect crop pests and diseases. Much work has been done to improve the algorithm, including proposing an improved network based on migration learning and data expansion, which greatly improves the accuracy of detection. Compared with the manual detection method and the traditional algorithm, the improved detection algorithm based on the deep convolutional neural network has the highest detection accuracy rate of 98.48% for 38 pests and diseases, which has higher efficiency, practicability, and accuracy.
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Index Terms
- Detection of Crop Pests and Diseases Based on Deep Convolutional Neural Network and Improved Algorithm
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Reviews
Franz J Kurfess
Especially in large monoculture-based agricultural settings, an outbreak of pests or diseases can have a major impact on yield or quality of a crop. Advances in image processing based on convolutional neural network (CNN) architecture over the past decade have yielded major improvements in the accuracy of image classification, renewing interest in its application to pest and disease detection. The authors describe the use of CNNs for pest and disease detection, starting with two widely used models: GoogLeNet and AlexNet. Using straightforward approaches with these network types results in classification accuracy of about 80 percent for 38 categories of pests and diseases. Because this is not very high, the authors examine two techniques known for optimizing accuracy: transfer learning and data augmentation. In transfer learning, the network is trained on a source domain with a large, well-curated set of images. This trained network is then fine-tuned by training it further with images from the target domain. In data augmentation, new data is generated from the original dataset to increase the size of the training set. The authors select the addition of noise as an augmentation method and explore the performance of the network with different levels of added noise. This combination of techniques resulted in significantly higher performance, with a detection accuracy of more than 98 percent for a noise standard deviation of three. While the authors' claim that their method is more efficient and reliable than human classification sounds plausible, no data is provided for the accuracy of human-expert classification.
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