Integration of IoT and Machine Learning for Real-Time Plant Health Monitoring and Disease Detection System

Integration of IoT and Machine Learning for Real-Time Plant Health Monitoring and Disease Detection System

Bhosale Aditya, Patil Manali, Lohar Sanika

Department of Electronics and Telecommunication Engineering Finolex Academy of Management and Technology, Ratnagiri, Maharashtra, India

{X210402, X210456, X210413}@famt.ac.in

Prof. Saurabh Athalye

Professor, Department of Electronics and Telecommunication Engineering Finolex Academy of Management and Technology, Ratnagiri, Maharashtra, India

saurabh.athalye@famt.ac.in

Abstract – Agricultural yield is highly dependent on timely disease management and optimal growing conditions. In mango cultivation, especially for the Alphonso variety, diseases such as anthracnose and rust cause significant damage. This paper introduces an IoT-based system that combines environmental monitoring with machine learning-driven leaf disease detection. Temperature, humidity, and soil moisture are tracked using DHT11 and soil moisture sensors interfaced with a NodeMCU ESP8266. This data is visualized on ThingSpeak. For disease diagnosis, a trained Convolutional Neural Network (CNN) model classifies mango leaves into healthy, rust-infected, or fungal-infected categories. The model is deployed via a Streamlit web application, offering users an intuitive interface for image upload and result display. The integrated system supports precision agriculture through timely alerts and remedies, reducing manual inspection and promoting sustainable farming.

      Keywords: Alphonso mango, Convolutional Neural Network (CNN), IoT-based monitoring, Leaf disease detection, Smart agriculture, Internet of Things (IoT).