Abstract
With the long-term irrational use of pesticides, the resistance of diseases and insect pests to pesticides is increasing. The effect of relying solely on a single species of pesticide to control diseases and insect pests is no longer significant, which causes the phenomenon of mixed use of pesticides is becoming more and more common. To solve the problem that current nondestructive methods for detecting a single pesticide residue cannot meet simultaneous multiple pesticide residues, one method based on hyperspectral imaging technology for identifying multiple pesticide residues in lettuce leaves was investigated. In this paper, nondestructive and fast identification for multiple pesticide residues was performed from the angle of spectral analysis. Comprehensively considering the running time, the detection accuracy, the convergence iteration number and the particle number (N) of GSA algorithm, the support vector machine optimized by Gravitational search algorithm (GSA-SVM) model (N = 40) achieved the best performance, with the accuracies of 100% and 96.08% for training set and test set, respectively. The hyperspectral imaging technology combined with GSA-SVM model is feasible for identifying multiple pesticide residues in lettuce leaves, and so hopefully to provide a methodological basis for detecting multiple pesticide residues in other vegetables.
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Funding
The paper was supported by Wuxi City Soft Science Project (201913571004Z) and Industry-University Cooperation Collaborative Education Project of the Ministry of Education (201902167006).
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Cong, S., Liu, C., Zhu, Z., Hu, A. (2021). Study on Identification of Multiple Pesticide Residues in Lettuce Leaves Based on Hyperspectral Technology. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Advances in Artificial Intelligence and Security. ICAIS 2021. Communications in Computer and Information Science, vol 1424. Springer, Cham. https://doi.org/10.1007/978-3-030-78621-2_45
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