Abstract: Rice is one of the most widely consumed staple crops globally, and its quality assessment plays a crucial role in food safety, trade, and agricultural productivity. Traditional methods of rice grain classification are largely manual, time-consuming, and prone to human error. In this study, we propose the development of an automated Rice Grain Image Classification Model using Artificial Neural Network (ANN) architecture. A dataset comprising high-resolution images of various rice grain types was collected and pre-processed, including resizing, normalization, and feature extraction. The ANN model was designed to capture subtle morphological and textural differences among grain categories, enabling accurate classification. Experimental results demonstrate that the proposed model achieves high accuracy, robust generalization, and reliable performance across different grain classes. The study highlights the potential of ANN-based approaches for automating rice quality assessment, reducing human intervention, and improving efficiency in post-harvest processing and market evaluation. The proposed framework can be extended to other agricultural products, supporting intelligent and data-driven quality management in the agro-food sector.

Keywords: ANN, Accuracy, Classification, Cross-entropy, Machine learning, precision.

Downloads: | DOI: 10.17148/IJIREEICE.2025.131002