Hyperspectral imaging with shallow convolutional neural networks (SCNN) predicts the early herbicide stress in wheat cultivars

Abstract

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.

Keywords: Crops; Deep learning; Herbicide toxicity; Hyperspectral technology; Prediction model.