Crop survival analysis using machine learning and Internet of Things

Abstract

The application of IoT in agriculture has proved its advantage by predicting the survival of crops in selected types of soil. Most of the people rely on agriculture in India. Nowadays technology is used to get accurate results in the field of agriculture. In addition to technology, soil plays a most important role in the field of agriculture. Agriculture is one of the applications of IoT. Different types of soil are used to cultivate different types of crops. Each type of soil has its features and characteristics. To predict the survival of crops in the selected soil machine learning techniques are used. In this paper, we proposed a model that will predict the accuracy of the survival of sugarcane crop in a loamy type of soil by extracting the data from the soil with the help of sensors. Machine learning methods such as K-nearest neighbour, random forest and support vector machine (SVM) models are used to predict survival accuracy. After obtaining the results, it shows that k-NN performs better than the other two models.