Early detection of nicosulfuron toxicity and physiological prediction in maize using multi-branch deep learning models and hyperspectral imaging.
J Hazard Mater
; 474: 134723, 2024 Aug 05.
Article
en En
| MEDLINE
| ID: mdl-38815392
ABSTRACT
The misuse of herbicides in fields can cause severe toxicity in maize, resulting in significant reductions in both yield and quality. Therefore, it is crucial to develop early and efficient methods for assessing herbicide toxicity, protecting maize production, and maintaining the field environment. In this study, we utilized maize crops treated with the widely used nicosulfuron herbicide and their hyperspectral images to develop the HerbiNet model. After 4 d of nicosulfuron treatment, the model achieved an accuracy of 91.37 % in predicting toxicity levels, with correlation coefficient R² values of 0.82 and 0.73 for soil plant analysis development (SPAD) and water content, respectively. Additionally, the model exhibited higher generalizability across datasets from different years and seasons, which significantly surpassed support vector machines, AlexNet, and partial least squares regression models. A lightweight model, HerbiNet-Lite, exhibited significantly low complexity using 18 spectral wavelengths. After 4 d of nicosulfuron treatment, the HerbiNet-Lite model achieved an accuracy of 87.93 % for toxicity prediction and R² values of 0.80 and 0.71 for SPAD and water content, respectively, while significantly reducing overfitting. Overall, this study provides an innovative approach for the early and accurate detection of nicosulfuron toxicity within maize fields.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Piridinas
/
Compuestos de Sulfonilurea
/
Zea mays
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Aprendizaje Profundo
/
Herbicidas
Idioma:
En
Revista:
J Hazard Mater
/
J. hazard. mater
/
Journal of hazardous materials
Asunto de la revista:
SAUDE AMBIENTAL
Año:
2024
Tipo del documento:
Article
País de afiliación:
China
Pais de publicación:
Países Bajos