Detection of Crop Pests and Diseases Based on Deep Convolutional Neural Network and Improved Algorithm

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.