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

Abstract

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 (R p 2 ) 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.

Plant phenotyping relevance

ラペシード画像から油分・タンパク質含量を推定する深層学習モデルとモバイルアプリを開発し、性能評価とモデル圧縮も行っており、植物種子形質の取得・推定手法が中心である。

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