A green and efficient method for detecting nicosulfuron residues in field maize using hyperspectral imaging and deep learning.

Accurate and rapid detection of nicosulfuron herbicide residues in field-grown maize is essential for implementing chemical remediation and optimizing spraying strategies. However, current detection methods are costly and time-consuming. This study analyzed residue levels in six maize varieties-both resistant and sensitive types-under two herbicide concentrations, categorizing residues into low, medium, and high levels.