Crop Recommendation System Using Machine Learning

Abstract

Abstract: Agricultural Crop Recommendation System relies on a multitude of input factors. This proposes a hybrid approach for suggesting suitable crops in specific areas, taking into account various attributes like soil type, rainfall levels, temperature value, Nitrogen value, Phosphorus value, Potassium value, humidity value and PH value of the soil. Utilizing technology to recommend crops for agriculture empowers farmers to enhance crop yields by suggesting the most suitable crops based on geographic and climatic conditions with respect to soil. The fundamental concept of this paper revolves around implementing a crop selection method to address numerous agricultural and farmer-related challenges. Using a group of different machine learning models to make better predictions is like having a team of experts give you advice. Each expert has their own knowledge and experience, and by listening to all of them, you can make a better decision. Accuracy of different ML algorithms in crop recommendation system are such as that Support Vector Machine algorithm gives 99.54% accurate results, Neural networks algorithm gives 91% accurate results, Decision Tree algorithm gives us 87% of accuracy. While K- Nearest algorithm, Logistic Regression and Naive Bayes algorithm gives us 75%-85% accurate results. We’re gonna use machine learning algorithms to make the project more accurate and efficient enough to provide the real time results. In this crop recommendation system using machine learning Support Vector Classifier and Decision tree algorithms are being used as an expectation of most accurate outcomes of the model. Keywords: Support Vector Machine, Feature selection, ,,Crop suitability indices, Training dataset, Testing dataset, Model evaluation.