数字农科院2.0

Lightweight deep learning model for embedded systems efficiently predicts oil and protein content in rapeseed

文献类型： 外文期刊

作者： Mengshuai Guo;Huifang Ma;Xin Lv;Dan Wang;Li Fu;Ping He;Desheng Mei;Hong Chen;Fang Wei

作者机构：

关键词： Computer vision;Content prediction;Deep learning;Oil and protein;Rapeseed;Real-time prediction

期刊名称： Food Chemistry

ISSN： 0308-8146

年卷期： 2025 年 477 卷

页码：

收录情况： SCIE（2025版） ; ; EI（2025版）

摘要： Conventional methods for determining protein and oil content in rapeseed are often time-consuming, labor-intensive, and costly. In this study, a mobile application was developed using an optimized deep learning method for low-cost, non-destructive and real-time prediction of protein and oil content in rapeseed by inputting rapeseed images. Among the tested models, FasterNet-L showed the optimal performance, with predicted coefficients of determination (Rp2) of 0.9366 for oil content and 0.8828 for protein content. The mean square error of prediction (RMSEP) was 0.6982 and 0.6498, and the residual predictive deviation (RPD) was 3.88 and 2.92 for oil and protein content, respectively. Furthermore, three pruning methods were employed, and neural pruning via growth regularization proved to be the most effective, with a 13.18 % improvement in prediction speed and a 15.79 % reduction in model size. Finally, this method can be expanded and applied to other oilseed crops for rapid quality identification and detection.

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