Plant Disease Detection Using ML & IOT

Plant diseases can cause significant crop losses, leading to economic and food security challenges. Early detection and treatment of plant diseases are essential to minimize losses. IoT and machine learning (ML) can be used to develop a plant disease prediction system that can monitor crop health and send alerts to farmers when diseases are detected. This abstract proposes a plant disease prediction system using IoT and ML using ESP32, temperature sensor, humidity sensor, sun intensity sensor, moisture sensor, solar panel, battery, and Blynk app. The system consists of the following components: ESP32 microcontroller: The ESP32 is a low-cost, low-power microcontroller that is ideal for IoT applications. It has built-in Wi-Fi and Bluetooth connectivity, which makes it easy to connect to the cloud and send data to farmers. Temperature sensor: The temperature sensor measures the ambient temperature around the plant. Humidity sensor: The humidity sensor measures the relative humidity around the plant. Sun intensity sensor: The sun intensity sensor measures the amount of sunlight that the plant is receiving. Moisture sensor: The moisture sensor measures the moisture content in the soil around the plant. Solar panel: The solar panel provides power to the ESP32 and sensors. Battery: The battery provides backup power in case the solar panel is not able to generate enough power. Blynk app: The Blynk app is a mobile app that can be used to monitor the data from the sensors and receive alerts when diseases are detected.