Artificial Neural Network-Based Forecasting of Rice Yield Using Environmental and Agricultural Data

This study presents a high-accuracy predictive model for rice production in Indonesia using Artificial Neural Networks (ANN), achieving an R² of 98.11%, Mean Absolute Error (MAE) of 0.0966, and Mean Squared Error (MSE) of 0.0189. Climate variability remains a significant challenge to rice cultivation in regions like Malang City, where unpredictable environmental factors such as rainfall, temperature, and humidity hinder effective crop planning and yield estimation. To address this, we developed a Multilayer Perceptron (MLP)-based ANN model incorporating agro-environmental variables: rainfall, temperature, humidity, harvested area, and production quantity. Historical data from 2009 to 2024 were sourced from the Meteorology, Climatology, and Geophysics Agency (BMKG) and the Central Statistics Agency (BPS). The dataset underwent preprocessing, including cleaning, feature extraction, Z-Score normalization, and partitioning into training and testing sets. The proposed ANN architecture consists of an input layer, three hidden layers, and an output layer for regression tasks. Comparative evaluation against Random Forest, K-Nearest Neighbors, and Support Vector Regression demonstrated the ANN’s superior ability to model complex nonlinear relationships in agricultural data. The results highlight the role of intelligent data-driven systems in enhancing the accuracy of yield forecasting, supporting sustainable agricultural practices, and informing national food security policy.