Lapcharoensuk, Ravipat

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Lapcharoensuk, Ravipat
Alternative Name
Lapcharoensuk, R.
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Email
ravipat.la@kmitl.ac.th

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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.
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    Low-cost Multispectral Acquisition Device Coupled with Machine Learning for Detecting Adulteration of Honey
    (2026-07-15)
    Boodnon, Wutthiphong
    ;
    Lunvongsa, Thayanont
    ;
    Suntisakoonwong, Phanchay
    ;
    Sitorus, Agustami
    ;
    Lapcharoensuk, Ravipat
    Honey is a natural sweetener created by honeybees from the nectar of flowers. Honey's extensive health benefits have led to its widespread use across multiple industries. Honey adulteration with inferior substances undermines its quality, reducing natural nutrients and antioxidants, and diminishing its health benefits. This study aimed to study the possibility of detection of honey adulteration with a low-cost multispectral device coupled with machine learning. The adulterated honey came from deliberate adulteration with cane syrup in the 1 to 90% range. Spectral data was collected for pure honey and the adulterated honey samples at the wavelengths of 610, 680, 730, 760, 810, and 860 nm. The detection models for distinguishing pure and adulterated honey were developed by Linear Discriminant Analysis (LDA), Partial Least Squares Discriminant Analysis (PLS-DA), C-Support Vector Machine (C-SVM), and K-Nearest Neighbors (KNN). All models achieved high accuracy between 0.91 and 0.98 and maintained balanced precision and recall metrics. This study serves as a guideline for developing a low-cost portable honey authentication device that is practical for real-world applications.
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    Estimation of the Weight and Volume of Lime (Citrus aurantifolia (Christm.) Swingle) Fruit Using Computer Vision Based on Traditional Machine Learning and Deep Learning
    (2024-10-01)
    Onmankhong, Jiraporn
    ;
    Poonpakdee, Pasu
    ;
    Lapcharoensuk, Ravipat
    The post-harvest process is important to increasing the market value of limes and requires focus. During this process, limes are graded and categorized based on size, weight, and volume. Therefore, identifying efficient means of estimating these properties is very important and remains an open research area. This study applies the concept of computer vision based on traditional machine learning algorithms (partial least square regression (PLS), epsilon-support vector regression (ε-SVR), decision tree (DT), random forest (RF), adaptive boosting (AB), gradient boosting (GB), Bagging meta-estimator (BME), and extremely randomized trees (ERTs)) and pre-trained deep learning (InceptionV3, MoblieNetV2, ResNet50, and VGG-16) for estimating the weight and volume of limes. Our findings showed that the BME and ResNet50 could yield the highest performance for estimating the weight and volume of limes. The BME produced (Formula presented.) values of 0.954 and 0.882 for weight and volume, respectively, while the (Formula presented.) values of ResNet50 models were between 0.951 and 0.957 for weight and volume, respectively. This study concluded that computer vision based on both traditional machine learning and deep learning could be used to estimate the weight and volume of limes. The approach proposed in this study can be adopted for applications related to computer vision in the post-harvest process.