AI-Based Crop Disease Detection and Automated Fertilizer Spraying System

Abstract

Agriculture plays a crucial role in ensuring food security and economic stability, especially in developing countries like India. However, crop diseases significantly affect agricultural productivity by reducing both yield and quality. Early detection of plant diseases and timely application of fertilizers are essential to minimize losses and improve farming efficiency. Traditional methods of disease identification depend on manual inspection, which is time-consuming, labor-intensive, and often prone to human error. This project presents an AI-Based Crop Disease Detection and Automated Fertilizer Spraying System designed to support smart agriculture practices. The system uses artificial intelligence techniques to detect crop diseases at an early stage by analyzing plant leaf images using a pre-trained machine learning model. The detection results are processed externally and transmitted to the system through an IoT platform. The NodeMCU ESP8266 acts as the central control unit, receiving disease information and executing decision-making logic. Based on the detected disease condition, the system automatically activates a relay module to control a water pump for spraying the required fertilizer. This automation reduces manual intervention and ensures accurate and timely fertilizer application. Overall, the proposed system provides a cost-effective, reliable, and scalable solution for modern agriculture by integrating artificial intelligence with IoT and embedded systems, thereby improving crop productivity and supporting sustainable farming practices