Application of Forecasting Models using Artificial Neural Network Techniques: A Case Study of Jasmine Rice Yield Forecasting in Ban Samran, Nong Khaen Subdistrict, Pathum Rat District, Roi Et Province
| International Journal of Electronics and Communication Engineering |
| © 2026 by SSRG - IJECE Journal |
| Volume 13 Issue 3 |
| Year of Publication : 2026 |
| Authors : Khemawit Jittayasothon, Sitthisak Audomsi, Worawat Sa-Ngiamvibool |
10.14445/23488549/IJECE-V13I3P104
How to Cite?
Khemawit Jittayasothon, Sitthisak Audomsi, Worawat Sa-Ngiamvibool, "Application of Forecasting Models using Artificial Neural Network Techniques: A Case Study of Jasmine Rice Yield Forecasting in Ban Samran, Nong Khaen Subdistrict, Pathum Rat District, Roi Et Province," SSRG International Journal of Electronics and Communication Engineering, vol. 13, no. 3, pp. 41-50, 2026. Crossref, https://doi.org/10.14445/23488549/IJECE-V13I3P104
Abstract:
The work seeks to construct an Artificial Neural Network (ANN) model to predict the yield of jasmine rice 105, utilizing the field, soil, and agro-climatic information of Ban Samran, Nong Khaen Subdistrict, Pathum Rat District, Roi Et Province, Thailand. The study was conducted from 2014 to 2023, and the data were collected from 130 paddy fields, covering primary and secondary daily datasets, and processing variables such as the Growing Degree Days (GDD), Sunshine Duration Days (SDD), and cumulative rainfall. Preprocessing the datasets normalizes all variables by the min-max method before loading them into the network. After network design, the performance of the developed network has evaluated the model by common measures of accuracy and efficiency of the model, and its results imply that the new model has highly accurate predictions of the yield, which was validated by having the root mean square error of 1.05 kg/rai, the mean absolute error of 0.90 kg/rai, the relative mean absolute error of 0.25%, and the coefficient of determination (R²) of 0.99, which confirm the success of the model in identifying the differences of the yield. Also, the new model performs precision enhancement by demonstrating prediction errors of about five to seven times smaller when compared to the traditional models such as Multiple Linear Regression, Polynomial Regression, Random Forest, and the Support Ma Regression models. The results also implicate the factors affecting rice yield, including the cumulative rainfall from the flowering stage, the maximum temperatures in the milky stage, the Soil Electrical Conductivity (EC), the Soil Organic Matter (OM), and the planting date. Lastly, the results indicate that the new model has significant effectiveness and contribution to the applicability of planting and water resource techniques and yield prediction for the Thung Kula Rong Hai Region.
Keywords:
Artificial Neural Network, Rice Yield Forecasting, Jasmine Rice 105, Thung Kula Rong Hai, Machine Learning Model.
References:
[1] Somaieh Samimi et al., “Rice, Wheat or Dust? Water Resource Carrying Capacity Estimation by Degrowth Transition Scenario for Fars Province, Iran,” Water Resources and Industry, vol. 34, pp. 1-16, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Zijie Shen et al., “Genetic Structure, KASP Markers, and Core Germplasm Resources of Rice Landraces from the Taihu Lake Basin,” Genomics, vol. 117, no. 4, pp. 1-9, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Federica Coelli, “Trade Policy Uncertainty and Innovation: Evidence from China,” Journal of International Economics, vol. 158, pp. 1 20, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Xiaosong Wang et al., “Climate Policy Uncertainty, Environmental Risk Aversion, and Import Structure Adjustment,” Journal of Asian Economics, vol. 102, 2026.
[CrossRef] [Google Scholar] [Publisher Link]
[5] S. Gupta et al., “Main Outcomes of OECD/NEA THAI-2 Project on Hydrogen Risk and Source Term Investigations: Data Application for Code Validation and Containment Safety Assessment,” Nuclear Engineering and Design, vol. 436, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Mohd Ashraff Mohd Sanusi, Muhammad Kashif Shad, and Wai Ching Poon, “Electricity Supply Industry Transformation and Decarbonization in Peninsular Malaysia,” Energy Reports, vol. 14, pp. 4370-4386, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Wann-Yih Wu et al., “Contextualizing Competitive Advantage in Emerging Markets: A Qualitative Study of Company Competitiveness in Laos,” Asia Pacific Management Review, vol. 31, no. 1, pp. 1-20, 2026.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Tawarat Treeamnuk, Tinakorn Pengprakhon, and Krawee Treeamnuk, “Effect of Temperature on Moisture Sorption Isotherm Characteristics of Thai Jasmine Paddy (Khao Dawk Mali 105),” LWT, vol. 210, pp. 1-7, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Chunyapuk Kukusamude, and Supalak Kongsri, “Elemental and Isotopic Profiling of Thai Jasmine Rice (Khao Dawk Mali 105) for Discrimination of Geographical Origins in Thung Kula Rong Hai Area, Thailand,” Food Control, vol. 91, pp. 357-364, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Ravipat Lapcharoensuk, and Chen Moul, “Geographical Origin Identification of Khao Dawk Mali 105 Rice Using Combination of FT NIR Spectroscopy and Machine Learning Algorithms,” Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, vol. 318, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Xuan Wei et al., “Evaluation of 2-Acetyl-1-Pyrroline in Foods, with an Emphasis on Rice Flavour,” Food Chemistry, vol. 232, pp. 531 544, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[12] L.G. Dias et al., “Identification of Volatiles and Odor-Active Compounds of Aromatic Rice by OSME Analysis and SPME/GC-MS,” Food Research International, vol. 142, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Godwin Olasehinde-Williams, and Seyi Saint Akadiri, “European-Transition Climate Risk Pricing in International Clean Energy Markets: Evidence from Quantile Dependence and Directional Predictability,” Journal of Environmental Management, vol. 392, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Fu Jia, Shoufeng Hu, and Lujie Chen “Impact of Carbon Border Adjustment Mechanism on the Stock Market Value of High-Polluting Firms in China: The Role of Supply Chain Risk Management and Internationalization Capabilities,” Transportation Research Part E: Logistics and Transportation Review, vol. 197, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[15] M. Arjun et al., “Medical Cannabis: Regulatory Review in United States, European Union and United Kingdom,” Journal of Forensic and Legal Medicine, vol. 115, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Elisa Tamudo et al., “Climatic Drivers of Growth and Intra-Annual Wood Density Fluctuations in Juniper Species from Seasonally Dry Environments,” Dendrochronologia, vol. 94, pp. 1-11, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Zeyu Zhou et al., “Estimating the Temperature Sensitivity of Rice (Oryza sativa L.) Yield and its Components in China using the CERES Rice Model,” European Journal of Agronomy, vol. 162, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Sisay Tefera et al., “Evaluating the Effects of the CERES-Rice Model to Simulate Upland Rice (Oryza sativa L.) Yield Under Different Plant Density and Nitrogen Management Strategies in Fogera Plain, Northwest Ethiopia,” Heliyon, vol. 10, no. 13, pp. 1-12, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Evandro H. Figueiredo Moura da Silva et al., “Implications of Water Management on Methane Emissions and Grain Yield in Paddy Rice: A Case Study under Subtropical Conditions in Brazil using the CSM-CERES-Rice Model,” Agricultural Water Management, vol. 307, pp. 1-12, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Saikat Biswas et al., “Validation and Future Yield Prediction of Winter Maize Under Irrigation and Primary Nutriens’ Levels in Gangetic Plains of India using DSSAT CERES-Maize Model,” Next Research, vol. 2, no. 2, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Dorsa Darikandeh et al., “Optimizing Rice Management to Reduce Methane Emissions and Maintain Yield with the CSM-CERES-Rice Model,” Agricultural Systems, vol. 224, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Hanane Ait Lahoussine Ouali et al., “Forecasting Hydrogen Production through Electrolysis Powered by Concentrated Solar Power Plant Using Artificial Neural Network,” Cleaner Engineering and Technology, vol. 28, pp. 1-17, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Salvador Castillo-Girones et al., “Artificial Neural Networks in Agriculture, the Core of Artificial Intelligence: What, When, and Why,” Computers and Electronics in Agriculture, vol. 230, pp. 1-19, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Mohd Azam Mohd Adnan et al., “Deep Insights Into the Integration of Artificial Neural Networks (ANNs) for Predicting the Photocatalytic Activities of Metal-Based Catalysts in Water Pollutant Reduction,” Journal of Environmental Chemical Engineering, vol. 13, no. 3, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Lizbeth Alejandra Ibarra-Muñoz et al., “Artificial Intelligence in the Food and Bioprocess Industries: Addressing Food Security Challenges,” Food and Humanity, vol. 5, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Sirwan Khalid Ahmed, “How to Choose a Sampling Technique and Determine Sample Size for Research: A Simplified Guide for Researchers,” Oral Oncology Reports, vol. 12, pp. 1-7, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Tahiru Lukman, Abdul-Kadri Yahaya, and Gordon N-yelkabo Tuu, “External Stakeholders in the Collaborative Governance of Natural Resources in Ghana: Experiences from the Wa West District,” Environmental Challenges, vol. 13, pp. 1-13, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Eli W. Estey et al., “Spaceborne Estimates of Canopy Temperature and Soil Moisture Predict Daily and Annual Subalpine Tree Growth,” Agricultural and Forest Meteorology, vol. 376, 2026.
[CrossRef] [Google Scholar] [Publisher Link]
[29] Paula Paredes et al., “Estimating the Lengths of Crop Growth Stages to Define the Crop Coefficient Curves using Growing Degree Days (GDD): Application of the Revised FAO56 Guidelines,” Agricultural Water Management, vol. 319, pp. 1-10, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[30] Caixia Song et al., “Unified Method for Assessing Soil Disturbance Caused by Underground Construction,” Tunnelling and Underground Space Technology, vol. 167, pp. 1-11, 2026.
[CrossRef] [Google Scholar] [Publisher Link]
[31] Xiaoyi Zhao et al., “Vegetation Regulation of Soil Erosion on Collapsed Accumulation Slopes Under the action of Rainfall and Upstream Inflow,” Engineering Geology, vol. 360, pp. 1-17, 2026.
[CrossRef] [Google Scholar] [Publisher Link]
[32] Dan Liu et al., “Deep Attention SMOTE: Data Augmentation with a Learnable Interpolation Factor for Imbalanced Anomaly Detection of Gas Turbines,” Computers in Industry, vol. 151, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[33] Bingqi Jia et al., “Enhancing the Absolute Positioning Accuracy of Welding Robots based on Joint Error Compensation,” Engineering Applications of Artificial Intelligence, vol. 158, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[34] Chengyu Shi et al., “Determination of the Coefficient of Thermal Expansion in Metallic Materials using Ultrasonic Testing Method,” Thermal Science and Engineering Progress, vol. 68, 2025.
[CrossRef] [Google Scholar] [Publisher Link]