Image Classification of Disease Detection in Paddy Crops Using Transfer Learning Approach

Rice serves as the cornerstone of Sri Lanka's diet, contributing 45% of total calories and 40% of protein intake, essential for food security. It's not just sustenance; the rice industry is a major force, providing employment to 1.8 million individuals, directly and indirectly, from diligent farmers to traders and retailers. This sector significantly contributes to government revenue through taxes and exports, with 10% of rice production being exported, bolstering foreign exchange earnings. Moreover, rural households rely on rice for income, uplifting living standards and fighting poverty. However, the existing methods for disease detection, rice quality prediction, and soil quality assessment are often slow and subjective. This research advocates the use of cutting-edge deep learning techniques, specifically Convolutional Neural Networks (CNN), to tackle these challenges.