Agriculture 2025, 15(10), 1023; https://doi.org/10.3390/agriculture15101023 (registering DOI) - 9 May 2025
Abstract
Aflatoxin B1 (AFB1) is widely present in maize silage feed and poses strong toxicity, seriously threatening livestock production and food safety. To achieve efficient and accurate non-destructive detection of AFB1, this study proposes a quantitative prediction method based on hyperspectral imaging technology. Using
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Aflatoxin B1 (AFB1) is widely present in maize silage feed and poses strong toxicity, seriously threatening livestock production and food safety. To achieve efficient and accurate non-destructive detection of AFB1, this study proposes a quantitative prediction method based on hyperspectral imaging technology. Using the full-spectrum bands after SG, SNV, MSC, FD, SD, and SNV + FD, MSC + FD, SNV + SD, MSC + SD preprocessing, the characteristic wavelengths selected by CARS, BOSS, and RF feature selection methods, and the augmented bands generated by Mixup data augmentation as input features, three models were developed for AFB1 content prediction: a linear WPLSR_SD_Mixup_QPE model, a nonlinear SVR_SD_Mixup_PCA model, and a deep learning CNN_SD_Mixup_WMSE_SA model. The results demonstrated that SD preprocessing was the most suitable for AFB1 detection in maize silage, and the Mixup data augmentation method effectively improved model performance. Among the models, SVR_SD_Mixup_PCA achieved the best performance, with an
(This article belongs to the Section Digital Agriculture)
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