Sinsamutpadung, Natdanai

Preferred name

Alternative Name

Sinsamutpadung, N.

Main Affiliation

School of Engineering

natdanai.si@kmitl.ac.th

Now showing 1 - 4 of 4

Some of the metrics are blocked by your
consent settings
Evaluation of various machine learning-based bias correction approaches for NASA POWER air temperatures: a case study of Nigeria
(2025-01-01)
Faloye, Oluwaseun Temitope
;
Kamchoom, Viroon
;
Ogunrinde, Akinwale T.
;
Ajayi, Ayodele Ebenezer
;
Oguntunde, Philip Gbenro
Remotely sensed air temperature data from NASA POWER are widely used in regions with scarce climatic observations, particularly for agricultural applications such as calculating crop water requirements. This study employed a suite of machine learning (ML) algorithms to correct biases in NASA POWER air temperature outputs, including multiple support vector regression (SVR) variants—Linear SVR, Quadratic SVR, Cubic SVR, Fine Gaussian SVR, Medium Gaussian SVR, Coarse Gaussian SVR—and ensemble decision tree models: bagged trees (BGT) and boosted trees (BT). The objective of this study was to assess the ability of different ML algorithms to reduce biases in NASA POWER air temperature data, with the broader goal of identifying the most suitable ML method for air temperature bias correction in Nigeria. For this analysis, we used daily air temperature records from seven meteorological stations across diverse regions of Nigeria. The performance of NASA POWER minimum and maximum air temperature datasets was evaluated using standard error metrics. Subsequent application of ML algorithms significantly improved data accuracy: the normalized root mean square error (NRMSE) of the corrected outputs was mostly below 10%, indicating excellent predictive performance when ML was integrated. Among the SVR variants tested, Fine Gaussian SVR consistently yielded the best prediction results. This finding suggests that Fine Gaussian SVR is a robust tool for enhancing the reliability of air temperature data—critical for improving the accuracy of crop water requirement calculations in regions where in-situ air temperature observations are limited.
Some of the metrics are blocked by your
consent settings
Simulation of emitter discharge along drip laterals under drip fertigation system using artificial neural network
(2025-07-01)
Faloye, Oluwaseun Temitope
;
Samuel, Smart Idumoro
;
Okunola, Abiodun Afolabi
;
Kamchoom, Viroon
;
Sinsamutpadung, Natdanai
Simulation of emitter discharge under a drip fertigation system is important for capturing the variation in water and nutrient distribution to crops. This is important for an effective design and irrigation management for agricultural crops. Moreover, the field discharge measurements are laborious and time-consuming, hence the need for the development of a representative model. The application of artificial neural network to simulate drip emitter along drip laterals is new in the field of flow measurement under drip irrigation. The purpose of this study is to predict the emitter discharge along drip laterals using artificial neural network (ANN) and evaluate the performance of the model. The input parameters fed into the ANN include; pipe length away from the fertigation source, elevation heads and distance of emitter point along the laterals. The field measured discharge was considered as the output. Evaluation parameters considered for the designed drip fertigation system indicated high efficiency, in the range between 81 and 98%. Interaction effects were observed between the pipe length and elevation head on the uniformity coefficient (CU) and emitter discharge. When all data were simulated, the ANN model simulated the emitter discharge accurately and precisely along the drip laterals, with R2 value ranging between 0.81 and 0.89, while the normalized root mean square error (NRMSE) was mostly below 20%, thus indicating a good prediction. The mean absolute error ranged between 0.034 and 0.048. Therefore, the ANN model was efficient for capturing the variation in emitter discharge well under the drip fertigation system.
Some of the metrics are blocked by your
consent settings
Forecasting maize yield from growth parameters using machine learning in a biochar-inorganic fertilizer amended soil under drip irrigation
(2025-12-01)
Faloye, Oluwaseun Temitope
;
Ajayi, Ayodele Ebenezer
;
Kamchoom, Viroon
;
Sinsamutpadung, Natdanai
;
Adeyeri, Oluwafemi
The combined application of biochar and inorganic fertilizers has demonstrated significant potential to enhance crop productivity under both rainfed and irrigated conditions. However, predictive modeling approaches utilizing machine learning (ML) to simulate field outcomes under diverse agronomic scenarios remain understudied. This study addresses two critical objectives: (i) to evaluate the efficacy of ML models—Support Vector Machine (SVM), Artificial Neural Network (ANN), and Boosted Trees (BT)—in predicting maize grain yieldin biochar-inorganic fertizer amended soil under drip irrigation; and (ii) to identify the growth stage(s) and ML models that deliver the most accurate predictions. A three-year factorial field experiment was conducted during dry seasons, testing five biochar rates (0, 3, 6, 10 and 20 t/ha), two fertilizer levels (0 and 300 kg/ha), and deficit irrigation treatments (60%, 80%, and 100% of full irrigation). Growth parameters were measured at vegetative (35 days after planting – DAP), flowering stage (49 DAP), and maturity stage (77 DAP), with grain yield recorded at harvest (90 DAP). The measured growth parameters at the different DAP were used for the grain yield forecast. 70, 15 and 15% of the dataset were used for model training, validation and testing, respectively. Field results revealed progressive increases in growth parameters from vegetative to maturity stages, with treatment efficacy following the order: control < biochar-only < fertilizer-only < combined biochar-fertilizer. ML predictions mirrored this hierarchy, with ANN achieving superior accuracy (R² = 0.73–0.85, RMSE = 0.43–0.76, NRMSE = 0.095–0.17 at maturity) compared to SVM and BT. Predictive performance was weakest at the vegetative stage (35 DAP) but improved during flowering (49 DAP) and maturity (77 DAP), underscoring the importance of later growth data for reliable yield forecasting. This study demonstrates that ML models, particularly ANN, can effectively predict maize yield using accessible growth metrics, offering a cost- and labor-efficient complement to traditional field research. By enabling rapid scenario analysis, such models empower stakeholders to optimize resource allocation and inform crop management decisions under varying irrigation and soil amendment strategies.
Some of the metrics are blocked by your
consent settings
Hydro-physical and chemical suitability of rosewood sawdust as a hydroponic substrate under drip irrigation
(2025-11-01)
Samuel, Smart Idumoro
;
Faloye, Oluwaseun Temitope
;
Okunola, Abiodun Afolabi
;
Adediran, Adeolu
;
Kamchoom, Viroon
Comprehensive characterization of hydroponic substrate is important to determine its suitability as a growing media. Therefore, the suitability hypothesis was tested by determining the rosewood sawdust substrate hydrological response at different sizes:<0.425, 0.425–1.70 and 1.70–2.00mm. The physical and chemical properties of the substrates were determined in the laboratory using standard procedures. Water storage and air capacity of the substrates were determined while regression models were developed for the water storage prediction with respect to the substrate sizes and pipeline distance away from the fertigation source. The highest total porosity of 75.92% was obtained for the large particle, while the lowest value of 72.57% was recorded in the finest particle and thus translated to improved moisture content and storage efficiency. The values of field capacity obtained for the coarse and fine particle were 133 and 159%, respectively. The developed regression model for the water storage produces coefficient of determination (R2) greater than 0.6 (60%), indicating a good prediction. Results showed that major nutrients required for plants growth, in the rosewood enhanced the nutrients (N, P, K, Mg, Ca) in the applied solution, and were mostly considered normal, when compared to the standard. However, the electrical conductivity of 31.2 mS cm-1 obtained in the substrate was too high, thus necessitating the need to pre-treat it for reduced electrical conductivity (EC) before use. Therefore, considering the enhancement in the nutrients solution when applied to the rosewood, the substrate is recommended for growing crops in hydroponics.

Sinsamutpadung, Natdanai

Filters

Settings

Sort By

Results per page