Fabrication of a Tool for Prediction of Crops Using Machine Learning

Climate change is a major problem all over the world in the current scenario that affects many things but its great impact on agricultural crops in India past 20–25 years has been consistent. The crop yield prediction can be more beneficial and helpful with policymakers and all communities of farmers for making informed decisions regarding the marketing purpose and storage area. This paper aims to develop a K-nearest neighbors (KNN) model for an interactive crop prediction system specifically for the purpose of prediction of crops for providing a better environment by using a machine learning algorithm. This constructive model system will give accurate crop yield predictions to the farmers before the cultivation of crops and also enables them to provide the appropriate decisions for different types of crops for cultivation purposes. This implementation of this KNN model includes a user-friendly web-based interface for easy access the weather conditions. After analyzing all such weather conditions as temperature, humidity, draught, rainfall, and moisture, the KNN model algorithm predicts the effectiveness of crop yield. This data mining approach summarizes important information by analyzing data from various perspectives and conditions. KNN model is a popular machine learning tool algorithm that is adequate for performing reversion and their grouping by making decision trees with training programmed and creating the outcome-based data on the majority of order or mean value for the purpose used.