Machine Learning Approach: Recommendation of Suitable Crop for Land Using Meteorological Factors

Abstract

Increasing population increases the need for food. As most population migrates towards cities for employment, the cultivable lands are turning into factories and apartments. The landlords are selling the plots due to the loss they face after cultivation. This loss occurs due to improper selection of crop for the particular field. The loss could be rectified if they are suggested with a suitable crop, based on the meteorological factors over the land area like testing soil quality, humidity, pH, etc. The farmers in interior places face difficulty in consulting with the experts for selection or rotation of crop. To overcome this problem, ANN came to play a role and also gave an effective solution. After knowing the suitable crop for the field, it is getting easier to decide the fertilizers and intercrop alongside. The profit rate will be considerably high using this method. It is also cost-efficient. This paper discusses the model for crop prediction using Machine learning algorithms. The model is compared with different approaches such as random forest, decision tree and SVM aiming to get a complete solution for the crop prediction and recommendation problem.

Keywords: Artificial neural networks (ANN), Decision Tree (DT), Support Vector Machine (SVM)