Grid Search-Optimized Artificial Neural Network Model for Rice Yield Prediction Using Weather and Soil Data in Malang City
Received: 7 June 2025 | Revised: 21 June 2025, 28 June 2025, and 7 July 2025 | Accepted: 8 July 2025 | Online: 29 July 2025
Corresponding author: Muhammad Faisal
Abstract
This research optimizes an Artificial Neural Network (ANN) model using Grid Search (GS) for predicting the rice yields in Indonesia. The purpose of this research was to enhance the performance of the ANN model by systematically tuning its hyperparameters to improve its predictive accuracy. This research uses the Multilayer Perceptron (MLP) method, and a comprehensive GS method was employed to explore various hyperparameter combinations, including the number of hidden layers, activation functions, solvers, regularization parameters, and learning rates. The optimization process involved evaluating each hyperparameter configuration using cross-validation to select the best model based on performance metrics, including the coefficient of determination (R²), Mean Absolute Error (MAE), and Mean Squared Error (MSE). The study's results indicate that the optimized ANN model achieved an R² of 97.41%, MAE of 766.69, and MSE of 1859857.06, outperforming the model without hyperparameters. This study highlights the effectiveness of the GS optimization in enhancing the ANN model performance, demonstrating that Hyperparameter Tuning (HT) is crucial for achieving improved prediction accuracy. This study concludes that the ANN model can be optimized for practical use in predicting the rice yields, as it shows strong performance.
Keywords:
artificial neural network, rice yield prediction, grid searchDownloads
References
A. Ansari et al., "Evaluating the effect of climate change on rice production in Indonesia using multimodelling approach," Heliyon, vol. 9, no. 9, Sept. 2023, Art. no. e19639.
R. N. Rohim and F. Ramadhony, "Indonesia’s Economic Interest in Rice Trade Cooperation with Thailand 2019-2022," Moestopo International Review on Social, Humanities, and Sciences, vol. 5, no. 1, pp. 1–9, Apr. 2025.
A. Dhamira and I. Irham, "The Impact of Climatic Factors on Rice Production in Indonesia," Agro Ekonomi, vol. 31, no. 1, pp. 46–60, Sept. 2020.
N. Naja, "Climate Change and Food Security in Central Java," BALANGA: Jurnal Pendidikan Teknologi dan Kejuruan, vol. 12, no. 1, pp. 30–40, June 2024.
Y. Xia et al., "Effects of soil pH on the growth, soil nutrient composition, and rhizosphere microbiome of Ageratina adenophora," PeerJ, vol. 12, Apr. 2024, Art. no. e17231.
A. Saleem et al., "Securing a sustainable future: the climate change threat to agriculture, food security, and sustainable development goals," Journal of Umm Al-Qura University for Applied Sciences, July 2024.
S. Castillo-Girones, S. Munera, M. Martínez-Sober, J. Blasco, S. Cubero, and J. Gómez-Sanchis, "Artificial Neural Networks in Agriculture, the core of artificial intelligence: What, When, and Why," Computers and Electronics in Agriculture, vol. 230, Mar. 2025, Art. no. 109938.
T. van Klompenburg, A. Kassahun, and C. Catal, "Crop yield prediction using machine learning: A systematic literature review," Computers and Electronics in Agriculture, vol. 177, Oct. 2020, Art. no. 105709.
L. Liao, H. Li, W. Shang, and L. Ma, "An Empirical Study of the Impact of Hyperparameter Tuning and Model Optimization on the Performance Properties of Deep Neural Networks," ACM Transactions on Software Engineering and Methodology, vol. 31, no. 3, Dec. 2022, Art. no. 53.
M. Mwita, J. Mbelwa, J. Agbinya, and A. E. Sam, "The Effect of Hyperparameter Optimization on the Estimation of Performance Metrics in Network Traffic Prediction using the Gradient Boosting Machine Model," Engineering, Technology & Applied Science Research, vol. 13, no. 3, pp. 10714–10720, June 2023.
C.N. Deore, P.T. Deore, and A.L. Taskar, "Application of artificial neural network in agriculture," International Journal of Advanced Research in Science, Communication and Technology, vol. 5, no. 6, pp. 179–184, 2025.
M. S. Basir, M. Chowdhury, M. N. Islam, and M. Ashik-E-Rabbani, "Artificial neural network model in predicting yield of mechanically transplanted rice from transplanting parameters in Bangladesh," Journal of Agriculture and Food Research, vol. 5, Sept. 2021, Art. no. 100186.
A. Alridha, F. A. Alsharify, and Z. Al-Khafaji, "A Review of Optimization Techniques: Applications and Comparative Analysis," Iraqi Journal for Computer Science and Mathematics, vol. 5, no. 2, Jan. 2024, Art. no. 5.
N. Iqbal et al., "Analysis of Wheat-Yield Prediction Using Machine Learning Models under Climate Change Scenarios," Sustainability, vol. 16, no. 16, Jan. 2024, Art. no. 6976.
Erlin, A. Yunianta, L. A. Wulandhari, Y. Desnelita, N. Nasution, and Junadhi, "Enhancing Rice Production Prediction in Indonesia Using Advanced Machine Learning Models," IEEE Access, vol. 12, pp. 151161–151177, 2024.
K. N. Vhatkar, S. A. Koparde, S. Kothari, J. Sarwade, and K. Sakur, "Enhancing prediction of crop yield and soil health assessment for sustainable agriculture using machine learning approach," MethodsX, vol. 14, June 2025, Art. no. 103418.
E. A. U. Malahina, G. R. Iriane, Y. S. Belutowe, P. Katemba, and J. Asmara, "A Grid-search Method Approach for Hyperparameter Evaluation and Optimization on Teachable Machine Accuracy: A Case Study of Sample Size Variation," Journal of Applied Data Sciences, vol. 5, no. 3, pp. 1008–1025, July 2024.
H.M. Khasanah, A. Aminuddin, F.F. Abdulloh, M. Rahardi, H. Hairani, and B.P. Asaddulloh, "Optimizing mushroom classification through machine learning and hyperparameter tuning," Engineering and Applied Science Research, vol. 51, no. 5, pp. 651-660, Sept. 2024.
A. Maneesha, C. Suresh, and B. V. Kiranmayee, "Prediction of Rice Plant Diseases Based on Soil and Weather Conditions," in Proceedings of International Conference on Advances in Computer Engineering and Communication Systems, Singapore, 2021, pp. 155–165.
P. Patil, P. Athavale, M. Bothara, S. Tambolkar, and A. More, "Crop Selection and Yield Prediction using Machine Learning Approach," Current Agriculture Research Journal, vol. 11, no. 3, pp. 968–980, 2023.
"Yield Rice Dataset," Kaggle Datasets, https://www.kaggle.com/datasets/hairani10/yield-rice-dataset.
E.-S. M. El-Kenawy, A. A. Alhussan, N. Khodadadi, S. Mirjalili, and M. M. Eid, "Predicting Potato Crop Yield with Machine Learning and Deep Learning for Sustainable Agriculture," Potato Research, vol. 68, no. 1, pp. 759–792, Mar. 2025.
D. Chicco, M. J. Warrens, and G. Jurman, "The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation," PeerJ Computer Science, vol. 7, July 2021, Art. no. e623.
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Copyright (c) 2025 Priyanto, Muhammad Faisal, Mochamad Imamudin
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