Affiliation:
1. Huzhou Vocational and Technical College, College of Intelligent Manufacturing and Elevator, Huzhou / China; Key Laboratory of Robot System Integration and Intelligent Equipment of Huzhou City, Huzhou / China
2. Huzhou Vocational and Technical College, College of Intelligent Manufacturing and Elevator, Huzhou / China
3. Huzhou Vocational and Technical College, College of Intelligent Manufacturing and Elevator, Huzhou / China; Key Laboratory of Robot System Integration and Intelligent Equipment of Huzhou City, Huzhou / China
Abstract
The detection of pesticide residues in white tea fresh leaves is an important step to ensure the quality safety of white tea finished products. Traditional detection methods are costly and inefficient to realize the demand for fast, low-cost, and accurate detection of pesticide residues in white tea fresh leaves. In this study, five types of white tea fresh leaf pesticide residue sample data were obtained using hyperspectral imaging technology for the high-frequency detected pesticides Glyphosate and Bifenthrin, and the SVM and 1D-CNN models were established to detect the samples after noise reduction processing and feature band screening methods. The study shows that the 1D-CNN model has better feature extraction ability, in which the SG-CARS-1D-CNN model has the highest detection accuracy, which is 94.62%, 95.12%, 94.35%, 94.95%, and 95.27% for the five type of species samples, respectively. This study provides pesticide residue detection for white tea fresh leaves based on the combination of hyperspectral data and an artificial intelligence model, which provides an intelligent, nondestructive, efficient, and high-precision pesticide residue detection model for white tea fresh leaves.
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