A SYSTEMATIC STUDY ABOUT THE CROP YIELD PREDICTION WITH MACHINE LEARNING TECHNIQUES REVIEW Article

Abstract

Nowadays Food security and  Agriculture were already becoming incredibly prominent issues on a worldwide scale. In addition, the significance of food production increases along with the size of the population. New ways of monitoring and managing agriculture must be introduced to satisfy the Future accuracy of the intended training data from the precious experience of previous generations of common types of problems, including technology. However, Machine Learning in Agriculture helps to enhance Crop Productivity and Quality within the Agricultural Sector. Increasing crop yields has become one of the most frequently discussed issues among farmers in modern agriculture. Due to the growing importance of crop yield prediction, this can be done accurately using a mathematical procedure to reduce repetition or organizing the data based on similarities in yield prediction across countries. An in-depth summary of broadly used components and prediction algorithms is also provided. We evaluate contemporary Machine Learning approaches and compare relevant studies. The strength and weaknesses of Machine Learning algorithms supported the prediction of current and forthcoming agricultural concerns are explored. This study examines yield using Machine Learning and associated techniques. A machine learning-supported agricultural yield prediction architecture was presented based on current studies. This challenges researchers to create an accurate crop yield prediction model with minimum computation.