Disease Pest Detection and Animal Quarantine Based on CNN Algorithms

  • Hongbo Wang
Keywords: Deep convolution neural network; fisher criterion; recognition accuracy; animal quarantine

Abstract

As global trade grows, the invasion of exotic pests is becoming more serious. The invasion of exotic pests has brought about serious economic, ecological and social security. In this paper, the author analyzes the disease pest detection and animal quarantine based on cnn algorithms. From the perspective of biodiversity, animal and plant quarantine has academic significance and national strategic significance. In this paper, deep convolutional neural networks are applied to crop disease image recognition, and various studies have been carried out. In order to meet the needs of potato disease identification, the structure of the convolutional neural network was improved. The research results show that the improved convolutional neural network has improved the feature extraction speed and recognition accuracy, and the recognition accuracy of the collected crop disease samples reaches 87%. The test results show that the improved network can effectively improve the recognition accuracy of the network when the sample size is small.

Published
2020-03-01