Improved Crop Yield prediction Using Neural Network

Computer Engineering

An Agricultural sector is in need for well-organized system to predict and improve the crop over the world. The complexity of predicting the best crops is high due to unavailability of proper knowledge discovery in crop knowledgebase which affects the quality of prediction. In data mining, classification of data is a crucial step in mining useful information. The classification techniques such as neural network, SVM, Naïve Bayes, KNN which make this task complicated due to problem like random selection of initial parameters. Here we propose crop prediction system using neural network and compare the results of the proposed system with various classifiers. Agricultural researchers over the world insist on the need for an efficient mechanism to predict and improve the crop growth. The need for an integrated crop growth control with accurate predictive yield management methodology is highly felt among farming community. The complexity of predicting the crop yield is highly due to multi-dimensional variable metrics and unavailability of predictive modeling approach, which leads to loss in crop yield. It works on an adaptive cluster approach over dynamically updated historical crop data set to predict the crop yield and improve the decision making in precision agriculture.

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