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A Review of Crop Yield Prediction Strategies based on Machine Learning and Deep Learning

Crop yield prediction is critical for solving increasing food security concerns, especially in the current climate change situation. Accurate forecasts will aid farmers in making financial and managerial decisions, thereby preventing food shortage. Machine learning (ML) techniques have proven an effective means for predicting the yield of crops, which greatly impact agricultural yield prediction where various Artificial Intelligence as well as deep learning techniques can be utilized. A detailed literature review has been performed in this work to analyze and explore various ML algorithms and to identify the features viz., weather, soil types and properties, fertilizers that highly influence the crop yield prediction. This study confirmed that the prediction accuracy depends mainly on the features considered for improving the crop yield prediction.