An Intelligent Plant Dissease Detection System for Smart Hydroponic Using Convolutional Neural Network

Recently, researchers proposed automation of hydroponic systems to improve efficiency and minimize manpower requirements. Thus increasing profit and farm produce. However, a fully automated hydroponic system should be able to identify cases such as plant diseases, lack of nutrients, and inadequate water supply. Failure to detect these issues can lead to damage of crops and loss of capital. This paper presents an Internet of Things-based machine learning system for plant disease detection using Deep Convolutional Neural Network (DCNN). The model was trained on a data set of 54,309 instances containing 38 different classes of plant disease. The images were retrieved from a plant village database. The system achieved an Accuracy of 98.0% and AUC precision score of 88.0%.