Multifactor Analysis to Predict Best Crop using Xg-Boost Algorithm

Abstract

As already known, Machine learning is a rapidly evolving technology. Several machine learning algorithms are used to predict the crop based on our analysis and study. Artificial neural network (ANN), Random forest, Linear regression, and Gradient boosting tree are just a few examples. They also used data sets such as soil, temperature, humidity, rainfall, pH value, and so on. This project includes modules like Crop, Fertilizer, etc. In Crop Module, the data sets like Nutrition, PH value, Rainfall, State and District Data are collected. In Nutrition, the values like nitrogen, phosphorus, potassium are collected. In fertilizer, the data sets like nutrition values and crop type data are collected. In Disease module, plant disease images data set are collected and futher this research work employs Deep learning concept like Convolutional Neural Network (CNN) for performing plant disease detection. Coming to machine learning part, Six major machine learning algorithms such as Decision Tree, SVM, Random forest, Logistic Regression XG Boost, and Naive Bayes are utilized in this paper. By collecting all data sets, the data will be trained by using the aforementioned machine learning algorithms.