Volume - 3 | Issue - 3 | september 2021
DOI
10.36548/jiip.2021.3.005
Published
09 September, 2021
In agriculture, crop yield estimation is critical. Remote sensing is being used in farming systems to increase yield efficiency and lower operating costs. Remote sensing-based strategies, on the other hand, necessitate extensive processing, necessitating the use of machine learning models for crop yield prediction. Descriptive analytics is a form of analytics that is used to accurately estimate crop yields. This paper discusses the most recent research on machine learning-based strategies for efficient crop yield prediction. In general, the training model's accuracy should be higher, and the error rate should be low. As a result, significant effort is being put forward to propose a machine learning technique that will provide high precision in crop yield prediction.
KeywordsCrop yield Machine learning Support Vector Machine Regression Model Naive Bayes Agriculture
