Abstract
The application of IoT in agriculture has proven 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 area of agriculture. Moreover present technology, soil plays a most crucial impact in the area of agriculture. Agriculture is one of the applications of IoT. Varieties 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 selected soil machine learning techniques are used. In this study, a model is proposed 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 use of smart sensors. Machine learning methods such as K-Nearest Neighbor’s (k-NN), Random Forest and Support Vector Machine (SVM) models are used to predict the survival accuracy. After obtaining the results, it shows that k-NN performs better than the other two models.
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