Stress Detection of Philippine Rice Variety in Lubang Occidental Mindoro Using Deep Learning

The present study deals with rice varieties from the Philippines that were grown in Lubang, Occidental Mindoro using deep learning. The aim of the research is to tackle the problem of rice plant stress, which greatly affects yield and quality. This research uses sophisticated deep learning techniques to measure different types of stress, such as discoloration, leaf wilting, and growth anomalies, as shown in high resolution images taken in rice fields. A complete data set was created through field surveys and remote sensing technology. The developed model showed a better performance than traditional methods in terms of accuracy when identifying and classifying stresses. Such an approach promises to be an effective tool for farmers as well as other agricultural stakeholders who wish to have better monitoring and management of crop health, resulting into increased productivity and sustainability within paddy farming. The results highlight how artificial intelligence can be used in agriculture to achieve food safety amidst environmental challenges.