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A multi-verse optimizer-based CNN-BiLSTM pixel-level detection model for peanut aflatoxins
Cong Wang1, Hongfei Zhu2, Yifan Zhao3
1College of Science and Information, Qingdao Agricultural University, Qingdao 266109, China.
Food Chemistry
|September 29, 2024
Summary
A new deep learning model accurately detects aflatoxins in peanuts using hyperspectral imaging. This optimized CNN-BiLSTM method improves precision for safer food products.
Area of Science:
- Agricultural Science
- Food Science
- Computational Science
Background:
- Peanuts are prone to aflatoxin contamination, a serious health risk.
- Accurate, real-time detection of aflatoxins is crucial for food safety.
Purpose of the Study:
- To improve pixel-level aflatoxin detection accuracy in hyperspectral images.
- To develop an optimized deep learning model for precise aflatoxin identification.
Main Methods:
- A Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) fusion model was developed.
- The model was optimized using the Multi-Verse Optimizer (MVO) algorithm.
- Fine-tuning was performed using hyperspectral data of aflatoxins at various concentrations.
Main Results:
- The MVO-optimized CNN-BiLSTM model achieved 94.92% validation accuracy and 95.59% recall.
- This model outperformed traditional machine learning (SVM, AdaBoost) and other deep learning methods (CNN, CNN-LSTM).
- Accuracy improvements ranged from 3.08% to 6.93% compared to existing methods.
Conclusions:
- The MVO-CNN-BiLSTM model significantly enhances pixel-level aflatoxin detection accuracy.
- This advancement supports the development of effective online monitoring systems for aflatoxins.
- The findings contribute to improved food safety and public health by enabling better detection of peanut contamination.
