Soil Sensors-Based Prediction System for Plant Diseases Using Exploratory Data Analysis and Machine Learning

Abstract

Plant diseases cause losses to agricultural production and hence, the economy. This necessitates a need to develop prediction models for the plant disease detection and assessment. Fungal infection, the most dominant disease, can be controlled by taking appropriate measures if detected at an early stage. The article aims to develop an expert system for the prediction of various fungal diseases (powdery mildew, anthracnose, rust, and root rot/leaf blight). A multi-layered perceptron model is used for the classification of the diseases which not only detects the plant diseases effectively but can also increase the production drastically. The proposed technique incorporates three significant steps of dataset pre-processing, exploratory data analysis, and detection module. Firstly, the real-time data is captured by the soil sensors system installed at agriculture field at Sardarkrushinagar Dantiwada Agricultural University, Gujarat, India, along with the satellite data for other micro-meteorological factors. Next, an extensive exploratory data analysis has been performed to get insights into the collected data. Finally, the proposed machine learning model has been employed to predict plant diseases. The experimental results indicate that the model outperforms several existing methods in terms of accuracy. Average accuracy in predicting each disease has been found more than 98%. This work also proves the feasibility of using this technique for faster plant disease detection at an affordable cost.