Monitoring Pesticide Residue on Bok Choi using Convolution Neural Network with NIR spectral Data

Abstract

Deep learning (DL) has been applied in agriculture, especially quality control in agricultural processing. One key area of interest is the detection and monitoring of pesticide residues in crops. The most popular measurement tool for nondestructive monitoring of pesticide residues is near-infrared spectroscopy (NIRS). A combination of CNN model with NIR spectral data was developed for monitoring pesticide residue on bok choi. The NIR spectral of bok choi with and without pesticide residue (chlorpyrifos) was collected in wavelength range between 908 and 1676 nm. A simple structure of CNN was modified for a one-dimensional task and this deep learning architecture was trained for classification of the bok choi samples. The results showed prefect prediction with 100% accuracy, precision, recall and specificity. This study also found that deep learning for NIR spectroscopy data requires less processing than traditional machine learning while still achieving great results.