Prediction of crop and yield in agriculture using machine learning technique

Agriculture holds a predominant position in the growth of any country's prosperity. However, there exists a major threat in the crop yield due to unpredictable and uncontrolled climatic changes, traditional farming methods, and poor irrigation services. In the past, crop yield was predicted based on farmer's experience. At present, the challenge is to increase the crop yield to meet the needs of the growing population. Currently, Machine Learning (ML) techniques are being used in multiple fields to accomplish practical and productive solutions. There are various algorithms in ML based on classification, clustering, and neural networks which can be used to predict the crop yield. In this work, we propose a method based on K-Nearest Neighbors (KNN) algorithm which detects the soil quality and predicts the suitable crop for cultivation. We consider temperature and soil quality as inputs to our algorithm. In addition, our method suggests the fertilizer based on the crop predicted. The test results show that our method accurately predicts the crop selection and yield which helps the farmers to great extent.