A multi-verse optimizer-based CNN-BiLSTM pixel-level detection model for peanut aflatoxins.

Peanuts are highly susceptible to contamination by aflatoxins, posing a significant threat to human health. This study aims to enhance the accuracy of pixel-level aflatoxin detection in hyperspectral images using an optimized deep learning method. This study developed a CNN-BiLSTM fusion model optimized by the Multi-Verse Optimizer (MVO) algorithm, specifically designed to detect aflatoxins with high precision. The optimized CNN-BiLSTM model was fine-tuned using aflatoxin spectral data at varying concentrations. The results indicate that the fine-tuned MVO-CNN-BiLSTM model achieved the best performance, with a validation accuracy of 94.92 % and a recall rate of 95.59 %. The accuracy of this model is 6.93 % and 3.6 % higher than machine learning methods such as SVM and AdaBoost, respectively. Additionally, it is 4.18 % and 3.08 % higher than deep learning methods such as CNN and the CNN-LSTM fusion model, respectively. This method enhances pixel-level aflatoxin detection accuracy, supporting the development of online detection devices.