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    Monitoring Pesticide Residue on Bok Choi using Convolution Neural Network with NIR spectral Data
    (2024-12-29)
    Lapcharoensuk, Ravipat
    ;
    Fhaykamta, Chawisa
    ;
    Anurak, Watcharaporn
    ;
    Chadwut, Wasita
    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.
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    Nondestructive Detection of Pesticide Residue (Chlorpyrifos) on Bok Choi (Brassica rapa subsp. Chinensis) Using a Portable NIR Spectrometer Coupled with a Machine Learning Approach
    (2023-03-01)
    Lapcharoensuk, Ravipat
    ;
    Fhaykamta, Chawisa
    ;
    Anurak, Watcharaporn
    ;
    Chadwut, Wasita
    ;
    Sitorus, Agustami
    The contamination of agricultural products, such as vegetables, by pesticide residues has received considerable attention worldwide. Pesticide residue on vegetables constitutes a potential risk to human health. In this study, we combined near infrared (NIR) spectroscopy with machine learning algorithms, including partial least-squares discrimination analysis (PLS-DA), support vector machine (SVM), artificial neural network (ANN), and principal component artificial neural network (PC-ANN), to identify pesticide residue (chlorpyrifos) on bok choi. The experimental set comprised 120 bok choi samples obtained from two small greenhouses that were cultivated separately. We performed pesticide and pesticide-free treatments with 60 samples in each group. The vegetables for pesticide treatment were fortified with 2 mL/L of chlorpyrifos 40% EC residue. We connected a commercial portable NIR spectrometer with a wavelength range of 908–1676 nm to a small single-board computer. We analyzed the pesticide residue on bok choi using UV spectrophotometry. The most accurate model correctly classified 100% of the samples used in the calibration set in terms of the content of chlorpyrifos residue on samples using SVM and PC-ANN with raw data spectra. Thus, we tested the model using an unknown dataset of 40 samples to verify the robustness of the model, which produced a satisfactory F1-score (100%). We concluded that the proposed portable NIR spectrometer coupled with machine learning approaches (PLS-DA, SVM, and PC-ANN) is appropriate for the detection of chlorpyrifos residue on bok choi.