Lapcharoensuk, Ravipat

Preferred name

Alternative Name

Lapcharoensuk, R.

Main Affiliation

School of Engineering

ravipat.la@kmitl.ac.th

Now showing 1 - 10 of 13

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Interpretable ANN-Based Computer Vision System for Mangosteen Ripeness Detection for Export Markets
(2026-01-21)
Lapcharoensuk, Ravipat
;
Tosribunjerd, Naphon
;
Poonpakdee, Pasu
Mangosteen is a high-value tropical fruit widely consumed and exported from Thailand. Mangosteen ripeness classification is crucial for export quality control, but manual grading leads to inconsistency and inefficiency. This study presents a computer vision system using an Artificial neural network to classify mangosteen into ripe, semi-ripe, and unripe stages based on peel color. A dataset of 378 images was collected and processed to extract 40 color-based features across multiple color spaces. Principal Component Analysis demonstrated non-linear separability among the ripeness classes. SMOTE and Gaussian noise augmentation were used to tackle data imbalance and enhance generalizability. The model reached a 95% accuracy rate and displayed flawless precision and recall for the ripe class. Integrated Gradients analysis highlighted the importance of the red-green color component (CIELAB a*) in the classification process. The proposed method demonstrates a low-cost, interpretable, and efficient solution suitable for real-world application in the mangosteen export industry.
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Combination of NIR spectroscopy and machine learning for monitoring chili sauce adulterated with ripened papaya
(2020-09-08)
Lapcharoensuk, Ravipat
;
Danupattanin, Kitticheat
;
Kanjanapornprapa, Chaowarin
;
Inkawee, Tawin
This research aimed to study the combination of NIR spectroscopy and machine learning for monitoring chilli sauce adulterated with papaya smoothie. The chilli sauce was produced by the famous community enterprise of chilli sauce processing in Thailand. The ingredients of the chilli sauce consisted of 45% chilli, 25% sugar, 20% garlic, 5% vinegar, and 5% salt. The chilli sauce sample was mixed with ripened papaya (Khaek Dam variety) smoothie with 9 levels from 10 to 90 %w/w. The NIR spectra of pure chilli sauce, papaya smoothie and 9 adulterated chilli sauce samples were recorded using FT-NIR spectrometer in the wavenumber range of 12500 and 4000 cm-1. Three machine learning algorithms were applied to develop a model for monitoring adulterated chilli sauce, including partial least squares regression (PLS), support vector machine (SVM), and backpropagation neural network (BPNN). All model presented performance of prediction in the validation set with R2al = 0.99 while RMSEP of PLS, SVM and BPNN were 1.71, 2.18 and 3.27% w/w respectively. This finding indicated that NIR spectroscopy coupled with machine learning approaches were shown to be an alternative technique to monitor papaya smoothie adulterated in chilli sauce in the global food industry.
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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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Classification of saline water for irrigated agriculture using near infrared spectroscopy coupled with pattern recognition techniques
(2019-09-24)
Lapcharoensuk, Ravipat
;
Phuphanutada, Jirawat
;
Wongpromrat, Patthranit
This research aimed to create near infrared (NIR) spectroscopy models for the classification of saline water with a pattern recognition technique. A total of 112 water samples were collected from the Tha Chin river basin in Thailand. Water samples with salinity less than 0.2 g/l were identified as suitable for agriculture, while water samples with salinity higher than 0.2 g/l were found to be unsuitable. The NIR spectra of water samples were recorded using a Fourier transform (FT) NIR spectrometer in the wavenumber of 12,500-4,000 cm-1. The salinity of each water sample was analysed by electrical conductivity meter. Identification models were established with 5 supervised pattern recognition techniques including k-nearest neighbour (k-NN), support vector machine (SVM), artificial neural network (ANN), soft independent modelling of class analogies (SIMCA), and partial least squares-discriminant analysis (PLS-DA). The performance of the NIR model was carried out with a split-test method. About 80% of spectra (90 spectra) were randomly selected to develop the classification models. After model development, the NIR spectroscopy models were used to classify the categories of the remaining samples (22 samples). The ANN model showed the highest performance for classifying saline water with precision, recall, F-measure and accuracy of 84.6%, 100.0%, 91.7% and 90.9%, respectively. Other techniques presented satisfactory classification results with accuracy greater than 68.2%. This point indicated that NIR spectroscopy coupled with the pattern recognition technique could be applied to classify saline water for agricultural use according to salinity level in natural resources.
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Discrimination of vegetable oil types using Fourier transforms near infrared spectroscopy coupled with pattern recognition techniques
(2019-09-09)
Lapcharoensuk, R.
;
Malithong, A.
;
Thappho, D.
;
Phonpho, P.
The aim of this research was to investigate the potential of near infrared spectroscopy (NIRs) coupled with pattern recognition techniques for discriminating of vegetable oil types (i.e. coconut oil, olive oil, rice bran oil, sesame oil, soybean oil and sun flower oil). Principle component analysis (PCA) was performed for clustering vegetable oil types. Five of supervised pattern recognition techniques such as soft independent modelling of class analogies (SIMCA), Partial least squares-discriminant analysis (PLS-DA), k-nearest neighbor (k-NN), support vector machine (SVM) and artificial neural network (ANN) were used to identify vegetable oil types. The PCA model could separate coconut oil from other vegetable oils. Two PLS-DA and SVM models showed 100% of precision, recall F-measure and accuracy for all vegetable oil whilst remainder techniques achieved a satisfactory classified performance. All supervised models could discriminate coconut oil from other oils with precision, recall F-Measure and accuracy of 100%. It seems that NIRs technique coupled with pattern recognition techniques is possible for discriminating vegetable oil types.
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Disinfestation of Sitophilus zeamais Motschulsky in stored maize using microwave
(2020-09-08)
Lapcharoensuk, Ravipat
;
Wisetsri, Sattawat
;
Meesuk, Supattra
This study aimed primarily to investigate on disinfestation of maize weevil (Sitophilus zeamais Motschulsky) in maize storage by using microwave method. Specimens of Maize weevils were cultured for 30 days in a maize sample with 17% moisture content (wet basis). Several different life stages of the maize weevil (egg, young larva, old larva, pupa and adult) were then infested in the sample. Next, the maize sample was exposed to 200, 300 and 450 W of microwave power for 60, 120 and 180 s, and the number of weevils were counted after each treatment. Moreover, the sample was kept for another 2 months to see whether the eggs have survived the treatment and grown to be adults. In addition, the effects of the level of microwave power and exposure time on the property of the maize sample were observed. The quality parameters investigated were moisture content, colour and protein and fat contents. It was found that, at 300 W of power for 180 s, the microwave was able to disinfect weevils at all life stages successfully, while the quality of the maize sample did not change significantly at the tested microwave settings and exposure times, except for the protein content. These findings indicate that microwave can be a good alternative to harmful chemical methods for disinfestation of maize weevils.
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Performance Comparison of Machine Learning Algorithms for Identification of Physiological Maturity of Pineapple using Optical Property
(2023-01-01)
Lapcharoensuk, Ravipat
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Phannote, Noppadon
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Kasetyangyunsapa, Dimae
Pineapple is important fruit of Thailand which is consumed in its fresh state or in processed products. Typically, harvested dates affected to quality of pineapple fresh. Identification of pineapple harvested dates at raw material receiving state in factory is very difficult. This research aims to determination of appropriate machine learning algorithm for Identifying maturity of pineapple using optical property. Color of pineapple fruits and fresh was measured by portable colorimeter on CIE system (L*, a∗ and b∗ values). The ten algorithms were fit to the training set including naive Bayes (NB), linear discriminant analysis (LDA), K-nearest neighbor (KNN), support vector machine (SVM), artificial neural network (ANN), logistic regression (LR), decision tree (DT), random forest (RF), gradient boosting (GB) and adaptive boosting (AB). The best model for pineapple fruit were established from ANN while DT showed highest performance for pineapple fresh. The accuracy of ANN and DT for fruit and fresh models were 83 and 92% respectively. This finding point is novel technique for identification of pineapple according to harvested dates which it can apply to quality control and assurance in pineapple industries.
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Quantitative detection of pepper powder adulterated with rice powder using Fourier-transform near infrared spectroscopy
(2019-09-09)
Lapcharoensuk, R.
;
Chalachai, S.
;
Sinjaru, S.
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Singsriand, P.
;
Hongwiangjan, J.
Near infrared (NIR) spectroscopy model was developed for detecting pepper powder adulterated with rice powder. The adulterated pepper powder samples were prepared by mixing rice powders with pure pepper powder to 19 levels of concentrations (w/w) from 5-95%w/w. Two hundred ten NIR spectra of pure and adulterant pepper powders were recorded using Fourier-transform near infrared spectrometer. The NIRs quantitative model for detecting adulterant pepper were established using partial least squares regression (PLS). The optimum model was established from NIR spectra treated by constant offset elimination with the of 0.99. These results show that the NIR spectroscopy could be a modern method for monitoring adulteration of pepper powder with rice powder.
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Development of the near infrared spectroscopy model for detecting herbicide concentrations contaminated in water
(2019-09-09)
Lapcharoensuk, Ravipat
;
Wongpromrat, Patthranit
This research aims to develop the near infrared spectroscopy (NIR) models for detecting herbicide concentrations contaminated in water. Atrazine, the organochlorine herbicide, solution in the concentration range of 0-15 ppm were prepared in distilled water. Near infrared spectra were scanned by using Fourier transform spectrometer at wavenumbers of 12,500-4,000 cm-1 (800-2, 500 nm). Partial least square regression technique was used to establish the NIR models for detecting herbicide concentrations. The developed models showed high prediction potential of herbicide concentrations contaminated in water with the R2 of 0.97, RMSEE of 0.899 ppm, bias of -0.0003 ppm and RPD of 5.43. This indicated that these models can apply to analyse contamination of atrazine in the natural water sources.
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Quantitative detection of buffalo milk adulteration with cow milk using Fourier transform near infrared spectroscopy
(2019-01-01)
Lapcharoensuk, Ravipat
;
Chaiyanate, Jirapad
;
Winichai, Supakit
;
Phetnak, Achiraya
A near infrared (NIR) spectroscopy model was used to quantitatively detect buffalo milk adulteration with cow milk. Pasteurized buffalo milk samples were purchased from a dairy farm and from a local supermarket. Adulterated milk samples were prepared with ratio of cow milk to buffalo milk at 9 levels of 10:90, 20:80, 30:70, 40:60, 50:50, 60:40, 70:30, 80:20 and 90:10 wt%. Spectra of pure buffalo milk, pure cow milk and adulterated milk samples were recorded by a Fourier transform NIR spectrometer in the wavenumber range of 12500-4000 cm-1 with resolution of 8 cm-1. A NIR spectroscopy quantitative model was developed with partial least square (PLS) regression. The NIR spectroscopy model showed ability to detect adulterated milk as follows: R<inf>val</inf>2 = 0.998, RMSEP = 2.121 wt%, Bias =-0.396 wt% and RPD = 18.1. NIR spectroscopy coupled with PLS algorithm was shown to be an alternative technique to detect buffalo milk adulteration with cow milk in the global dairy industry.

Lapcharoensuk, Ravipat

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