Innovative Agricultural Solutions: Utilizing Machine Learning to Combat Rice Leaf Disease in Indonesia

Advances in machine learning and image processing technology offer promising solutions for identifying diseases in rice plants. This study, which utilizes 80 data samples and trains on 20 data points from available datasets, proposes a novel approach using the Random Forest algorithm to efficiently classify leaf diseases. This method assists farmers, especially newcomers, in quickly identifying diseases via smartphone devices. By integrating various feature extraction techniques such as color, texture, and shape, the approach enhances accuracy in disease recognition, which is crucial for timely preventive measures. The process includes stages of data acquisition, preprocessing, feature extraction, and model evaluation to ensure robustness and reliability. By harnessing the power of machine learning, this research contributes to addressing the challenges faced in rural agriculture, aligning with Indonesia’s development goals outlined in the third Nawacita program. Through systematic analysis and evaluation, this approach highlights the potential of technology to empower farming communities, promote sustainable agricultural practices, and enhance food security in the region.