Prediction of Crop Yield Using Efficient Data Analysis Techniques

Abstract

The weather's impact on crop output might be regarded as a crucial factor in agricultural yield forecast. Numerous studies have been undertaken to determine how weather impacts agriculture, however the majority of these studies need vast amounts of complicated data that are not readily accessible. As a result, the approach must be improved to account for the lack of data. Machine learning (ML) can extract patterns and associations from datasets and derive information from them. We gathered agricultural and meteorological data from a vast dataset to ensure reliable crop forecasting. We chose Random forest, SVM, and Decision tree as the best models for the proposed system since they are more efficient and perform better than current models at calculating extremely big datasets of weather and climate, assuring greater accuracy and faster processing. We provide input characteristics such as weather, rainfall, and temperature and obtain the most ideal crop as a result. The primary objective of this model is to forecast changes in the weather and to assist farmers in making agricultural choices in response to such changes. Additionally, data mining is beneficial for forecasting agricultural yield output. In general, data mining is the act of examining data from several perspectives and condensing it into useful information.

Random forest is the most widely used and powerful supervised machine learning algorithm. Random forest is the most widely used and powerful supervised machine learning algorithm.