CNN-Based Tomato Leaf Disease Detection With Smart Spraying Mechanism

Abstract

Indian agriculture plays a vital role in crop production and economic development, providing employment to a significant portion of the population. The health of plants is crucial for better yield and profit, requiring continuous monitoring to prevent diseases that can severely impact crop productivity. Traditional disease detection relies on manual observation, which is time-consuming and inefficient. To address this, an automated plant disease identification and prevention system is introduced, integrating IoT-based agricultural monitoring with Machine Learning algorithms. Various IoT sensors, such as thermal, moisture, humidity, and color sensors, help detect plant diseases at an early stage. The system enables timely intervention through automated medicine spraying, improving efficiency and crop health.