Nondestructive identification of pesticide residues on the Hami melon surface using deep feature fusion by Vis/NIR spectroscopy and 1D-CNN

Nondestructive identification of pesticide residues remains a challenge in terms of fruit safety assessment. In this study, a novel method based on visible/near-infrared (Vis/NIR) spectroscopy (348.45-1,141.34 nm) combined with deep feature fusion was proposed, achieving nondestructive identification of pesticide residues on the Hami melon surface. The spectra of Hami melons with clear water and three kinds of pesticide residues (chlorothalonil, imidacloprid, and pyraclostrobin) were collected in the diffuse reflectance mode. The one-dimensional convolutional neural network (1D-CNN), with increased width and depth through parallel convolution modules and concatenate layers, was presented to capture multiple deep features from Vis/NIR spectra and fuse them. This model had a better performance for four-class identification as the accuracy of 95.83%, and outperformed other CNN models and conventional approaches (partial least squares discriminant analysis and support vector machine). Moreover, the proposed 1D-CNN model could accurately differentiate whether there were pesticide residues with the identification accuracy as 99.17%. However, the prediction of imidacloprid and pyraclostrobin residues was not accurate due to the similar spectral features. The overall studies indicated that the 1D-CNN model with deep feature fusion looked promising for nondestructive identification of pesticide residues on the Hami melon surface based on Vis/NIR spectroscopy. Practical applications Visible and near-infrared (Vis/NIR) spectroscopy, as a nondestructive technique, looks promising for evaluation of fruit quality and safety. One-dimensional convolutional neural network, with deep feature fusion structure to capture multi-scale spectral information, has a better identification of pesticide residues on the Hami melon surface. Vis/NIR spectroscopy with deep feature fusion can be applied in research and development of a nondestructive detector for pesticide residues on the thick-skinned fruit surface in the future.

Automated summary

Nondestructive identification of pesticide residues on Hami melon surface remains a challenge. A novel method based on Vis/NIR spectroscopy and deep feature fusion was proposed in this study. The 1D-CNN model achieved a high accuracy of 95.83% for four-class identification and 99.17% for differentiation of pesticide residues, showing promising potential for nondestructive detection of pesticides using Vis/NIR spectroscopy.