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In Journal of hazardous materials

The toxicity impacts of herbicides on crop, animals, and human are big problems global wide. The rapid and non-invasive ways for assessing herbicide-responsible effects on crop growth regarding types and levels still remain unexplored. In this study, visible/near infrared hyperspectral imaging (Vis/NIR HSI) coupled with SCNN was used to reveal the different characteristics in the spectral reflectance of 2 varieties of wheat seedling leaves that were subjected to 4 stress levels of 3 herbicide types during 4 stress durations and make early herbicide stress prediction. The first-order derivative results showed the spectral reflectance exhibited obvious differences at 518-531 nm, 637-675 nm and the red-edge. A SCNN model with attention mechanism (SCNN-ATT) was proposed for herbicide type and level classification of different stress durations. Further, a SCNN-based feature selection model (SCNN-FS) was proposed to screen out the characteristic wavelengths. The proposed methods achieved 96% accuracy of herbicide type classification and around 80% accuracy of stress level classification for both wheat varieties after 48 h. Overall, this study illustrated the potential of using Vis/NIR HSI to rapidly distinguish different herbicide types and serial levels in wheat at an early stage, which held great value for developing on-line herbicide stress recognizing methods in the field.

Chu Hangjian, Zhang Chu, Wang Mengcen, Gouda Mostafa, Wei Xinhua, He Yong, Liu Yufei

2021-Jul-22

Crops, Deep learning, Herbicide toxicity, Hyperspectral technology, Prediction model