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

Journal: Journal of hazardous materials
PMID: 39637793

Abstract

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. We developed the HerbiResNet model to predict and classify herbicide residues in maize leaves using spectral data. The model achieved a coefficient of determination (R²) of 0.88 for residue prediction and an accuracy of 0.87 for residue level classification on the test set, significantly outperforming traditional regression models (SVR, PLSR) and classical neural networks (MLP, AlexNet). Additionally, we explored combining spectral technology with deep learning, revealing strong correlations between specific spectral bands (around 550 nm, 680 nm, 750 nm, and 1000 nm) and herbicide residues as well as physiological changes in maize. This provides a solid theoretical foundation for the broader application of spectral technology in agriculture. Overall, the HerbiResNet model demonstrates substantial potential for precision agriculture and sustainable agricultural practices.

Authors

  • Tianpu Xiao
    College of Engineering, China Agricultural University, Beijing 100083, China; The Soil-Machine-Plant key laboratory of the Ministry of Agriculture of China, Beijing 100083, China.
  • Li Yang
    Department of Pharmacy, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
  • Xiantao He
    College of Engineering, China Agricultural University, Beijing 100083, China; The Soil-Machine-Plant key laboratory of the Ministry of Agriculture of China, Beijing 100083, China.
  • Liangju Wang
    Department of Agricultural and Biological Engineering, Purdue University, 225 S. University St., West Lafayette, IN 47907, USA.
  • Dongxing Zhang
    College of Engineering, China Agricultural University, Beijing 100083, China; The Soil-Machine-Plant key laboratory of the Ministry of Agriculture of China, Beijing 100083, China.
  • Tao Cui
    Department of Gynecology and Obstetrics, West China Second University Hospital, Sichuan University, Chengdu, Sichuan 610041, China.
  • Kailiang Zhang
    Tianjin Key Laboratory of Film Electronic & Communication Devices, School of Integrated Circuit Science and Engineering, Tianjin University of Technology, Tianjin 300384, China.
  • Lei Bao
    School of Public Health, Hubei University of Medicine, Shiyan, China.
  • Shaoyi An
    College of Engineering, China Agricultural University, Beijing 100083, China; The Soil-Machine-Plant key laboratory of the Ministry of Agriculture of China, Beijing 100083, China.
  • Xiaoshuang Zhang
    College of Engineering, China Agricultural University, Beijing 100083, China; The Soil-Machine-Plant key laboratory of the Ministry of Agriculture of China, Beijing 100083, China.

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