Machine Learning Based Method for Forecasting Crop Yield

Applications of machine learning are revolutionizing data processing and decision-making, which is having a significant effect on the global economy. Given the worldwide food supply crisis, agriculture is one of the industries where the effects are most noticeable. This paper focuses on crop yield prediction based on pattern analysis with the help of the machine learning approach, which focuses on data acquisition, preprocessing, and assessment. Taking the Crop Yield Prediction Dataset as a solution, the most potential factors, including rainfall, temperature, and pesticide, have been identified to have the most influential factors in creating better prediction models. Among these, Decision Trees, Random Forest, Support Vector Machine (SVM), Artificial Neural Network (ANN), Naïve Bayes, and Long Short-Term Memory (LSTM)are the most common and are checked for their effectiveness. It brings out facts that are instrumental in analysis to improve the yields on farms and come up with possible recommendations on precision farming and sustainable agriculture. This paper aims to provide insights that can help improve farm yields.