Crop Disease and Pest Identification Technology Based on Acpso-SVM Algorithm Optimization

Abstract

Research on the classification and identification of crop diseases and pests can help farmers quickly prevent crop diseases and pests. A crop disease and pest identification model based on adaptive chaotic particle swarm optimization algorithm is raised. The model introduces swarm intelligence algorithm to optimize the features of image extraction. Then the adaptive inertia weight is used to improve the optimization performance of PSO, and the support vector is used to accurately classify crop pests and diseases. Finally, the model is trained by simulation experiment to evaluate the performance of the model and analyze the performance. The model has a good performance in the experiment, the model has a clear recognition effect in the color feature extraction of pests and diseases, and the recognition accuracy is 95.08% after combining the texture feature. Moreover, in the visual transformation of 20°-40°, the recognition accuracy of the model is above 90%. In practical application, the average accuracy of the model is 91.78%, which is 3.71% higher than that of the comparison algorithm. In comparison experiments, the classification accuracy of the proposed models is above 90%. The experimental outcomes denote that the proposed algorithm has good effectiveness in identifying crop diseases and pests.