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The yield of crops is influenced by various factors such as weather conditions, soil characteristics, irrigation facility, solar radiation, fertilizer application, tillage, etc. Accurate prediction of crop yield is an important issue in agriculture as un-presented changes in yield will significantly influence food supply and market prices. Data pre-processing and selection of relevant features is an essential step while perform prediction using machine learning algorithms. In this work, Monte Carlo simulation for random selection of data and binary cuckoo search for relevant feature selection are used with an objective of enhancing the accuracy of prediction using multiple linear regression technique. Experimental results are discussed.

Enhanced crop yield prediction using Monte Carlo method and binary cuckoo search