Intelligent Disease Prediction in Hydroponic Systems Using Machine Learning

Abstract

Hydroponics is the soil-less agriculture farming, which consumes less water and other resources as compared to the traditional soil-based agriculture systems. However, mon- itoring hydroponics farming is a challenging task due to the simultaneous supervising of numerous parameters and plant diagnosis system. Therefore, this article focuses on the imple- mentation of web application integrated machine learning-based smart hydroponics expert system. The proposed project with IoT consists of three phases, where the first phase implements hardware environment equipped with real-time sensors such as pH, temperature, water level, and camera module which are con- trolled by Raspberry Pi processor. The second phase implements the CNN Model for plant disease detection and classification and the system includes a chat bot for user interaction, addressing plant-related questions and providing details about any detected diseases. In the third phase, farmers can monitor the real-time sensor data using AWS TwinMaker and plant leaf disease status using an web-based application. In this manner, the farmer can continuously track the status of his field using the mobile app. Through this innovative approach, hydroponic farming can become more efficient, sustainable, and ultimately contribute to addressing global food security challenges.