Emerging Trends in Machine Learning to Predict Crop Yield and Study Its Influential Factors: A Survey

Agriculture is one of the most crucial field contributing to the development of any nation. It not only affects the economy of nation but also has impact on the world food grain statistics. For agriculturist obtaining sustainable production of crop is always a challenge. Achieving optimum crop yield has always been a challenge for the farmer due to ever changing environmental conditions. The major reasons for unpredictability of crop yield are: land types, availability of resources, and changing nature of weather. Thus, the scientists all over the world are trying to discover techniques which can efficiently and accurately estimate the crop yield in much advance so that the farmers can take suitable actions to meet the future challenges. The main objectives of the study include: (a) Exploration of various machine learning techniques used in crop yield prediction; (b) Assessment of advanced techniques like deep learning in yield estimations; and (c) To explore the efficiency of hybridized models formed by the combination of more than one technique. The reviews done have shown good inclination towards hybrid models and deep learning techniques as means of crop yield prediction. The study also reviewed the works done by researchers in assessing the influence of various factors on crop yields and temperature and precipitation have been found to have maximum influence on the yields of different crops. Apart from climatic factors, agronomic practices adopted by farmers at various stages of growth of a plant also have considerable influence of the final yield of crop.