Detection of Pesticide Residue Level in Grape Using Hyperspectral Imaging with Machine Learning.

ABSTRACT: Rapid and accurate detection of pesticide residue levels can help to prevent the harm of pesticide residue. This study used visible/near-infrared (Vis-NIR) (376-1044 nm) and near-infrared (NIR) (915-1699 nm) hyperspectral imaging systems (HISs) to detect the level of pesticide residues. Three different varieties of grapes were sprayed with four levels of pesticides. Logistic regression (LR), support vector machine (SVM), random forest (RF), convolutional neural network (CNN), and residual neural network (ResNet) models were used to build classification models for pesticide residue levels. The saliency maps of CNN and ResNet were conducted to visualize the contribution of wavelengths. Overall, the results of NIR spectra performed better than those of Vis-NIR spectra. For Vis-NIR spectra, th

SUBMITTER: Ye W 

PROVIDER: S-EPMC9180647 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature