CROP RECOMMENDER SYSTEM

Abstract

The development of farming techniques improved food supply to the nation's GDP and made it simpler for farmers to grow the precise and suitable crop without any risk or loss in productivity. Agriculture is important to human existence and livelihood because it has become a necessary and crucial part of our daily lives. To maintain the sustainability in rate and quality of production, we need to involve and introduce the best portable and noncomplex strategies such as machine learning tools to carry on the needed operations and procedures to get desired and expected crop by coordinating and initiating data exchange between the scientific and practical platforms which is the trending existing system. The idea behind this project, called the crop recommendation system, is to define and state that the appropriate crop should be grown based on a number of relative parameters, including soil features like nitrogen, phosphorus, and potassium that are extracted from the soil through filtration, and weather conditions that are embedded in a dataset in the form of structured data. This dataset is taken over by machine learning algorithms that will perform some operations like classification and will be finding the accuracy where in detail to be explained we will be splitting the given dataset into training and testing data and compare the results of those algorithms based on accuracy that each model gives and that will be our preferred algorithm. Along with a few dimensionality reduction techniques including PCA, LDA, andcross validation, we applied machine learning techniques like Decision Tree, Random Forest, and KNN.