DEEP LEARNING-BASED DETECTION OF RICE LEAF DISEASES FOR SMART AGRICULTURE

Agriculture serves as the backbone of India's economy, with Andhra Pradesh standing out as a major contributor in rice production and distribution.Maximizing rice yield requires effective management of resources like water and land, alongside efficient disease control strategies.Crop disease management plays a vital role in improving the economic conditions of farmers by reducing infection rates and ensuring healthier, more productive crops.Traditionally, disease identification has relied on manual inspections by farmers, guided by personal experience, or consultations with agricultural experts who conduct laboratorybased, rule-driven diagnostics.While useful, these conventional methods are time-consuming, error-prone, and require substantial human effort especially in the face of rising disease variants.To address these challenges, this study proposes a deep learning-based approach for automatic rice leaf disease detection.By using rice leaf images as input, the model identifies specific diseases and recommends appropriate pesticides accordingly.This method enhances both the accuracy and speed of disease diagnosis, reducing the dependence on manual inspections and enabling timely interventions.Ultimately, the adoption of this AI-driven system aims to improve rice crop yield, support farmer decision-making, and boost overall agricultural productivity.