Predict the Crop-Yield Through UAV using Machine learning A Systematic Literature Review

Machine learning is used in smart farming to forecast crop yields by analyzing the effects of climate change on crops. In recent years, Machine learning and UAV based imagery is used in agriculture field for crop yield prediction. Today, remote sensing is utilized to monitor crop nutrients and predict crop yield. Various machine learning algorithms such as Random Forest, CNN and DNN are used for crop yield prediction. A systematic literature review is conducted to analyze the which machine learning algorithms used with remote sensing for crop yield prediction studies. This research aimed to conduct a comparative examination of the available procedures in order to identify the approaches that are most effective for resolving the most prevalent issues with agricultural yield prediction. In this study 487 papers are extracted from electronic databases and 50 papers were chosen based on inclusion and exclusion criteria. The total 50 articles have been chosen, over which the SLR is documented.