Variety Classification and Identification of Maize Seeds Based on Hyperspectral Imaging Method
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Abstract:
In this study, eight different varieties of maize seeds were used as the research objects. Conduct 81 types of combined preprocessing on the original spectra, through comparison, SG-MSC-MN was identified as the optimal preprocessing technique. CARS, SPA, and their combined methods were employed to extract feature wavelengths. Classification models based on BP, SVM, RF, and PLS were established using full-band data and feature wavelengths. Among all models, the (CARS-SPA)-BP model achieved the highest accuracy rate of 98.44%. This study offers novel insights and methodologies for the rapid and accurate identification of corn seeds as well as other crop seeds.
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Project Supported:
Jilin Provincial Department of Science and Technology Science and Technology Development Plan Project (Grant No. 20220203112SF) and Jilin Provincial Department of Education Science; Technology Research Project (Grant No. JJKH20210039KJ).
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