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Application of UAV Image Detection Based on CBPSO Algorithm in Crop Pest Identification

Application of UAV Image Detection Based on CBPSO Algorithm in Crop Pest Identification

Li Tian1*, Chun Wang2, Hailiang Li2, Haitian Sun2 and Chang Wang3

1College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing, 163319, China
2South Subtropical Crops Research Institute, Chinese Academy of Tropical Agriculture Science, Zhanjiang, 524000, China
3College of Science, Heilongjiang Bayi Agricultural University, Daqing, 163319, China
 
* Corresponding author: tllbj19781015@126.com

ABSTRACT

In order to quickly control crop diseases and insect pests, chaos theory is used to optimize PSO, and CPSO algorithm is proposed. In the practical application of crop diseases and insect pests identification, CBPSO algorithm is obtained by binary operation of CPSO, and its performance and application are analyzed. The experimental results show that the classification accuracy of CBPSO-SVM algorithm is 98.22% and 97.78% respectively in gray spot disease and algal spot disease, higher than that of PCA-SVM (82.5% and 83.67%). In addition, the average classification accuracy of CBPSO-SVM is 91.26%, better than that of PCA-SVM (81.83%). At the same time, through the comparison of CBPSO algorithm, BPSO algorithm and PSO algorithm, it is found that CBPSO algorithm has great adaptability, the maximum fitness is 0.99, and the average fitness is 0.97. Therefore, CBPSO algorithm has good effect on global optimization, and its convergence speed is faster, and its execution task and efficiency are higher. In addition, among the five particle swarm optimization algorithms, the CBPSO algorithm performs best in the 20 dimension, with the minimum value of 1.0008 and the minimum value of variance of 8.4206. Therefore, it is determined that the search efficiency of the CBPSO algorithm in the 20 dimension is the best. Compared with other algorithms, the stability and accuracy of the CBPSO algorithm have been greatly improved, and it has high robustness in the actual UAV image detection and crop pest identification.

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Pakistan Journal of Zoology

June

Pakistan J. Zool., Vol. 56, Iss. 3, pp. 1001-1500

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