Rice Kernel

Rice kernel research encompasses improving yield prediction, ensuring seed purity, and automating quality assessment. Current efforts leverage machine learning, particularly deep learning architectures like CNNs and hybrid models, to analyze image data for disease detection, seed purity classification, and kernel quality estimation. These advancements aim to enhance rice production efficiency, improve food security, and reduce crop losses due to disease and poor seed quality, impacting both agricultural practices and food science. The integration of remote sensing data and advanced algorithms is also improving yield forecasting and resource management.