Abstract
Abstract In order to screen high-quality peanut pod varieties on food processing production lines and promote the sustainable development of the peanut as well as the expansion of its consumer market, this study improved the recognition ability of peanut pod varieties and the efficiency of deep feature extraction by optimizing the ResNet50 deep learning network model. Experimental results showed that the accuracy of the optimized network model reached 91.6%, which was 2.1% higher than the original ResNet50 model. Additionally, this study extracted the deep features of the appearance morphology of peanut pods, used the agglomerative clustering method to explore the genetic relationship of hybrid offspring varieties under the same line, and constructed a pedigree diagram containing 18 varieties. These research findings provide a crucial scientific foundation for the cultivation of high-quality peanut varieties. The selected high-quality peanuts not only enhance the added value of the peanut industry but also further expand the market potential of peanut by-products.
Published Version
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