An IoT and Machine Learning-Based Crop Prediction System for Precision Agriculture

Abstract

With the advancement of intelligent devices we stand among a plethora of technologies, tools, state-of-the-art techniques, and proof of concepts for a number of applications that essentially use a huge volume of data. Our precision agriculture system aims towards low input, high accuracy with the help of machine learning and the Internet of things towards sustainable agriculture. This article presents results that show that the prediction of fertilizer with different classifiers can be calculated accurately with corresponding heatmaps. We show that Naive Bayes is more accurate as it depends on probabilistic features. Hence, this classifier can be used for better crop prediction.