Crop Yield Prediction Based on Ensemble Model Using Historical Data

Computer Engineering

India is a country where agriculture and agriculture related industries are the dominant source of living for the people. Agriculture is in poor condition since before comparing previous years. The vital reason for this is without a well organized pattern about farming and proper instructions to the farmers. Agriculture is an extensive source of economy of the country. Apart from this India also ail from natural disasters like flood and drought which forfeit the crops. One’s strategy should be spot on while harvesting the crops, factors like season, soil moisture and weather condition should be well planned and also when to harvest the crop to get the maximum yield. In this project study, we provide an effective recommendation system for the farmers using machine learning techniques. In the proposed system we use ensemble based hybrid model to make the classifier strong. The input dataset is been obtained from the public repository. The input historical dataset in provided to machine learning platforms for data processing. The clustering of data is performed using Guassian Mixture Model clustering and for classification of data we use KNN and XGBboost techniques to define the conditions and solutions. This would provide effective recommendation for the farmers in defining the best crop which can be farmed to obtain huge profitable yield analyzing the environmental factors

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