Smart Agriculture Using ESP32

Abstract

Agriculture is an important sector that requires continuous monitoring of crop health and field conditions to improve productivity and reduce crop loss. Traditional plant disease identification mainly depends on manual observation by farmers or agricultural experts, which may be time-consuming, less accurate, and difficult to perform continuously in large fields. In this paper, we propose a smart agriculture system using ESP32, ESP32-CAM, and Convolutional Neural Network (CNN) based leaf disease detection to support real-time crop monitoring. The proposed system monitors environmental parameters such as soil moisture, water level, temperature, pump status, and solenoid valve status through an interactive web dashboard. Leaf images are captured using an ESP32-CAM or uploaded manually to the system, and the trained CNN model classifies the disease category from the input image. The system further provides disease description, preventive steps, and treatment recommendations to assist farmers in taking timely action. The integration of IoT-based environmental monitoring and AI-based disease detection improves the efficiency of crop health analysis and supports smart decision-making in agriculture.