Pesticide residue detection technology based on hyperspectral

Ziyue Wang

doi:10.1117/12.3011213

1 November 2023 Pesticide residue detection technology based on hyperspectral

Ziyue Wang

Proceedings Volume 12917, International Conference on Precision Instruments and Optical Engineering (PIOE 2023); 129170N (2023) https://doi.org/10.1117/12.3011213
Event: 3rd International Conference on Precision Instruments and Optical Engineering (PIOE 2023), 2023, Shanghai, China

Abstract

With the development of the times, due to the pursuit of healthy diet, people are paying more and more attention to the safety of vegetables and fruits. In recent years, there have been frequent safety issues in fruits and vegetables in China. The abuse of pesticides has made it difficult to clean residual pesticides on fruits and vegetables, and residual pesticides have a significant impact on physical health. In the past, relying on the experience of fruit farmers to judge has become outdated, and the cost of chemical colorimetric detection technology and other technologies is high and complex. Therefore, the use of hyperspectral technology for vegetable and fruit detection has become the mainstream detection method in recent years. This technology can analyze crops through spectroscopy, and use machine learning algorithms to process the data. This article will introduce the progress and methods achieved in the application of hyperspectral technology in fruit and vegetable detection in the past six years, for the convenience of scientific researchers for reference.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Ziyue Wang "Pesticide residue detection technology based on hyperspectral", Proc. SPIE 12917, International Conference on Precision Instruments and Optical Engineering (PIOE 2023), 129170N (1 November 2023); https://doi.org/10.1117/12.3011213

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KEYWORDS

Pesticides

Image processing

Hyperspectral imaging

Imaging systems

Principal component analysis

Machine learning

Near infrared

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