Research on Wheat Field Irrigation Identification Method Based on Multi-Model Verification of 532nm Optical Characteristics

Abstract

This study presents a robust approach for identifying irrigation status in wheat fields using specific optical characteristics, validated through multiple deep learning models. Experimental results demonstrate the superior performance of the proposed method in detecting irrigated fields, with all models achieving consistently high accuracy. The optimized lightweight model maintains reliable performance while significantly reducing computational requirements, showing strong potential for practical agricultural monitoring applications. Comprehensive evaluation confirms the method's robustness, with stable detection capability and interpretable error patterns. The findings provide an effective solution for precision agriculture, with future research suggested to explore multi-spectral integration for enhanced performance.