Origin traceability of agricultural products: A lightweight collaborative neural network for spectral information processing

Abstract

The natural conditions of various regions, including climate, soil, and water quality, significantly influence the nutrient composition and quality of agricultural products. Identifying the origin of agricultural products can prevent adulteration, imitation, and other fraudulent practices, ensuring food quality and safety. This work proposes a Lightweight Collaborative Neural Network (LC-Net) integrated with a hyperspectral system to recognize the origin of peanuts and rice from seven different origins. The Collaborative Spectral Feature Extraction Module (CSFEM) enhances the expression of spectral features, improving detection performance through local and global deep spectral feature extraction. LC-Net achieves 99.33 % accuracy, 98.98 % precision, and 99.28 % recall for peanuts, and 99.76 % accuracy, 99.63 % precision, and 99.73 % recall for rice. This AI-based method, combined with spectral analysis, provides a reliable technique for ensuring the quality and safety of agricultural products.