Description

In recent years, estimates of agricultural yields have had a significant impact on national economies. Early forecasting allows for the prediction of market prices, the provision of import and export plans, the reduction of the social and economic repercussions of waste goods, and the presentation of a program for humanitarian food relief. In addition, agricultural lands are expanding continuously to produce the goods needed. The efficient production of high-quality agricultural products can result from the application of machine learning (ML) techniques in this industry. The nonlinear correlations between the data could not be discovered by conventional predictive machine models. The development of machine learning (ML), which can be used to create highly accurate decision-making networks, has recently revolutionized prediction systems. This paper’s main goal is to recommend the crop by giving the proper data. The newly enlarged machine learning methods like SVM and DT were used to categorize the crops. In this project take N, P, K, temperature, humidity, PH, rainfall as a feature for finding the crop which is suitable for the climate and also calculate the accuracy and classification report and also calculate the confusion matrix for the prediction of the crop.