Udompetaikul, Vasu
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Preferred name
Udompetaikul, Vasu
Alternative Name
Udompetaikul, V.
Main Affiliation
Email
vasu.ud@kmitl.ac.th
13 results
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Item type:Publication, Managing the almond and stone fruit replant disease complex with less soil fumigant(2013-07-01) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Interpretable Machine Learning for Sugarcane Harvester Performance: A Comparison of Additive and Tree-Based Models on Telematics Data(2026-07-01); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Tractor-mounted, GPS-based spot fumigation system manages Prunus replant disease(2013-10-01); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Development of the partial least-squares model to determine the soluble solids content of sugarcane billets on an elevator conveyor(2021-01-01); ; This study aimed to determine the optimum multivariate model for monitoring the soluble solids content (SSC) of sugarcane billets being transferred on a conveyor. The study covered two main issues: the exploration of an appropriate spectral range (450–900 nm versus 700–900 nm) and the assessment of the influence of different levels of cane billets on an elevator via modelling to predict the SSC values. Partial least squares regression (PLSR) was used for model development. Modelling using the range of 450–900 nm employed 4 latent variables (LVs) and showed the coefficient of determination (R<sup>2</sup>) and root mean squares error of prediction (RMSEP) of 0.83 and 0.29 °Brix, respectively. This caused the model established using the range of 700–900 nm, employed 3 LVs and provided the R<sup>2</sup> and RMSEP values of 0.81 and 0.31 °Brix, respectively, seems more appropriate. In case of assessing the different cane levels on the conveyor, the outcomes presented model performance of the full and half cane levels in predicting half and full cane datasets with R<sup>2</sup> and RMSEP of 0.52 and 0.55 °Brix and 0.53 and 0.48 °Brix, respectively. This showed that the different levels affected the SSC predictive accuracy of the model. The combined model was developed to cover variations of this difference and was used to predict two external sets. The predictions of ninety and thirty samples that were collected from the same and different growing seasons as the samples for the modelling presented the R<sup>2</sup>, RMSEP and RPD of 0.70, 0.42 °Brix and 1.83 and 0.56, 0.42 °Brix and 2.00, respectively. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Rapid Classification of Sugarcane Nodes and Internodes Using Near-Infrared Spectroscopy and Machine Learning Techniques(2024-11-01); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Design of a laboratory-scale sugarcane weighing system(2019-09-09); ; Recently, sugarcane harvesters have been increasingly used in sugarcane harvesting. Loading trucks were traveling along the harvesters to collect the harvested cane billets. Since cane harvesters are expensive machines, there is an idea of collaborative farming by combining multiple fields from different owners to reduce operating costs and time. However, it is difficult to fairly classify yields from different fields. Site-specific yield monitoring system is not common in typical harvesters. Farmers only know the weight on each truck without its collecting location when selling the sugarcane to the factory. This research was the feasibility study to develop a hydraulic weighing system in laboratory scale for further applying to the side-tipping loading trucks. A low-cost hydraulic weighing system was fabricated. A microcontroller was used to read signals from pressure and gyroscopic sensors and then to calculate the applied load. Accuracy and precision of the system were examined. The coefficient of determination (R<sup>2</sup>) of the relationship between the actual and determined loads was 0.978. The standard error of prediction (SEP) of the system was 2.348 kg. The results show that there was feasibility to apply the system on farm scale; however, further study with a larger scale should be conducted. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Determination of field capacity for the sugarcane harvester using GNSS data(2019-09-09) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, In-line near infrared spectroscopy for the prediction of moisture content in the tapioca starch drying process(2019-03-01); ; Moisture content is an important parameter measured in tapioca starch production as this parameter has been shown to correlate strongly with the quality of the finished product. However, there is currently no in-line sensor which can be used to directly measure the moisture content of the product in real time. The objective of the present work was to study the use of an in-line measurement which can be introduced at the end of the drying process for tapioca starch moisture content evaluation. Either in-line NIR data or at-line NIR data was used to develop the necessary calibration models for evaluating the moisture content. Furthermore, calibration models were also developed by pooling the in-line and at-line data. Its performance was then verified using additional in-line data. The NIR model developed using 100% of the at-line data and 50% of the in-line data was validated using the unused 50% of the inline data. This model was shown to provide better performance in moisture content prediction with an SEP of 0.61% and a bias of 0.001%. In addition, the results showed that the at-line spectrum can also be used for the calibration model development to predict the moisture content of the samples scanned by an in-line spectrometer. However, the in-line spectrometer installation on a pneumatic conveying circular tube where tapioca starch and air mixed was found to be complicated due to significant vibration. This caused additional variation in the data with time. Therefore, it is concluded that the most suitable place for installing a spectrometer would be at a position involving a low pressure, or where the stream flow of a product is steadier in order to avoid the dynamic mixing of the product within the drying tube affecting the uncertainty of NIR scattering during the measurement. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Machine learning-based prediction of nutritional status in oil palm leaves using proximal multispectral images(2022-07-01); ; ; This study evaluated the application of proximal multispectral images accompanied by 4 machine learning approaches for estimating the nutritional status of oil palm leaves. The image responded for five bands: blue, green, red, red edge, and near-infrared regions with a center wavelength of 475, 560, 668, 717, and 840 nm. Average and standard deviation (SD) values from the leaf pixels of each band were extracted, obtaining 5 average and 5 SD values from 5 bands. Thirty-four vegetation variables were generated based on those average and SD values. In total, forty-four variables consisted of 10 average-and SD-based features, and 34 vegetation variables were used as the input candidates for analyses against 10 target variables: nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), iron (Fe), manganese (Mn), zinc (Zn), boron (B), and chlorophyll (SPAD). No significant input came out for modeling with P and Zn based on the stepwise selection. Therefore, 8 nutritional models were proposed in this study. A training set with 50 samples was used to be modeled for each target, and a test set with 15 samples was employed to evaluate the models' performances. Based on random forest (RF), support vector regression (SVR), partial least square regression (PLSR), and artificial neuron network (ANN) applied to be modeled, the models for chlorophyll, N, and Ca predictions were acceptable for screening, and those for K and Mg predictions were acceptable for rough screening. The chlorophyll model developed based on the RF had the predictive statistics in terms of coefficient of determination for prediction (r<sup>2</sup>), root mean square error of prediction (RMSEP), and standard error of prediction (SEP) of 0.752, 5.46 SPAD, and 5.65 SPAD, respectively. The other 2 screening models developed based on SVR and RF for N and Ca, respectively, gave the performances with the r<sup>2</sup>, RMSEP, and SEP ranging from 0.655 to 0.718, 0.12 to 0.17%, and 0.12 to 0.18%, respectively. In the case of the 2 rough screening models established using the RF algorithm, the predictive statistics ranged from 0.496 to 0.530 for the r<sup>2</sup> and 0.07–0.16% for both RMSEP and SEP. In this study, the Fe, Mn, and B models had poor results presenting the range of r<sup>2</sup>, RMSEP, and SEP of 0.308–0.491, 2.39–72.9 ppm, and 2.45–62.8 ppm, respectively. Based on the results, this study confirmed that the proximal multispectral information of oil palm leaves had enough significance to account for the status of chlorophyll and macro-nutrients: N, K, Ca, and Mg in the leaves. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, An online visible and near-infrared spectroscopic technique for the real-time evaluation of the soluble solids content of sugarcane billets on an elevator conveyor(2018-11-01); ; The aim of this research study is to propose a prototype online detection system based on the visible and near-infrared spectroscopic (vis/SW-NIR) technique for the real-time evaluation of the soluble solids content (SSC) of sugarcane billets on an elevator conveyor. The system consisted of two main parts, a cane billet elevator and a spectral acquisition device. An elevator speed of 2 m/s was used for the transfer of sugarcane billets. Spectra acquisition was performed using four 50 W tungsten halogen lamps as a light source in conjunction with vis/SW-NIR spectrometer in reflectance mode. Partial least squares regression (PLSR) was subsequently used to correlate the spectra with the experimentally determined SSC values. The model performance was then assessed using an independent prediction set. The model was found to display a coefficient of determination of prediction (R<sup>2</sup>) of 0.785, a root mean square error of prediction (RMSEP) of 0.30 and a residual predictive deviation (RPD) of 2.16. The result on the prediction set confirm that the proposed system is suitable for the online SSC measurement of the sugarcane billets on an elevator conveyor.