Crop Recommender System

Agriculture is the important aspect in terms of existence and livelihood of humans, as it has become a necessary and crucial factor in our daily life; evolution of farming techniques is developed that leads to the improvement in supply of food to the country’s GDP and also made easy for farmers to grow the accurate and suitable crop without any risk and loss in the productivity. 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 concept and ideology of this project named crop recommendation system defines and states that the respective crop has to be grown on basis of various relative parameters like soil characteristics or features such as nitrogen, phosphorus, potassium that are extracted from the soil through filtration process, and weather conditions which are embedded in a dataset in 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. We used machine learning techniques like decision tree, random forest, KNN along with few dimensionality reduction techniques like PCA, LDA and cross-validation.