An Improved Machine Learning based Crop Recommendation System
B. Swathi Sri, G. Pavani, B.Y.S. Sindhuja, V. Swapna, P.L. Priyanka
Abstract
Agriculture is the most important and essential component for human survival. In order to prescribe fertilizers and predict which crops would provide the maximum yields, the paper focuses on using several machine learning methods. The soil properties are provided to the application by the farmer. Crop leaves are used as an additional input by this algorithm to forecast the disease. The yield of the land is entirely and partially dependent on the economy of a country, which is strongly dependent on agriculture and is influenced by organic, economic, and seasonal variables. Gathering, storing, and analysing data that can be used to predict agricultural productivity is the major goal. Farmers are able to select the greatest crop for their needs thanks to this. This study seeks to improve predictions of crop productivity, which will help the agricultural industry. The findings of this study will help farmers to decide which crops to plant based on many factors like the affordable price areas and crops with the lowest likelihood of losses.