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Agriculture is the most important sector and the backbone of a developing country’s economy. Accurate crop yield prediction models can provide decision-making tools for farmers to make better decisions. Crop yield prediction has challenged researchers due to dynamic, noisy, non-stationary, non-linear features and complex data. The factors that influence crop yield are changes in temperature and rainfall, plant disease, pests, fertilizer, and soil quality. The paper discusses the factors affecting crop yield, explores the features utilized, and analysis deep learning methodologies and performance metrics utilized in crop yield prediction.