A Comprehensive Review of Crop Yield Prediction Using Machine Learning Approaches With Special Emphasis on Palm Oil Yield Prediction

An early and reliable estimation of crop yield is essential in quantitative financial evaluation at the field level for determining strategic plans agricultural commodities import-export policies doubling farmer’s incomes. Crop predictions are carried out to estimate higher through use machine learning algorithms which one challenging issues sector. Due this developing significance prediction, article provides an exhaustive review on predict with special emphasis palm oil prediction. Initially, current status around world presented, along a brief discussion overview widely used features prediction algorithms. Then, critical state-of-the-art learning-based application industry comparative analysis related studies presented. Consequently, detailed study advantages difficulties proper identification future challenges The potential solutions additionally prescribed order alleviate existing problems Since major objectives explore perspectives areas including remote sensing, plant’s growth disease recognition, mapping tree counting, optimum have been broadly discussed. Finally, prospective architecture has proposed based studies. This technology will fulfill its promise by performing new research development extremely effective model yields most minimal computational difficulty.