Crop Yield Prediction Using Machine Learning: A Comparative Analysis and Review of Classification Algorithms.

Abstract

Global civilization began with agricultural practises. Although human lifestyles have evolved over time, agriculture continues to play a significant role in providing for fundamental requirements and basic needs. Agriculture contributes significantly to nation-building beyond just feeding people. One such contribution is the growth of foreign reserves via the sale of agricultural goods and products. Predicting and estimating agricultural production is necessary because of the rapidly changing climate in our surroundings, which negatively affects most crops at harvest time.  The forecast assists farmers in selecting the best crop to plant in order to maximise yield by examining variables such as soil quality, temperature, humidity, rainfall, and field area. Crop production forecasts make it easier for farmers to plan all aspects of sale and storage. This paper reviews common, state of earth, effective and recently used prediction and classification algorithm for crop yield.