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Predicting Agricultural Output by applying Machine Learning.


Siddhant Ghosh , Suman Ramesh Tulsiani Technical Campus; Prof. Sonali Patil, Suman Ramesh Tulsiani Technical Campus; Utkarsha Matere, Suman Ramesh Tulsiani Technical Campus


Machine Learning, KNN Filter, Gaussian Process Regression, Perceptron-Based, Single-Layered Perceptron-Based, Multi-Layered Perceptron-Based, Naïve Bayes Classification, Ordinary Least Squares Classification, Logistic Regression


Data sets of different related to the agriculture is vastly available. The main motto of our system will be to create a machine learning algorithm and use the available datasets for the unsupervised machine learning process and thereby predict the future crop production, crops which would mostly likely to give a greater profit margin, etc. The prediction can also help various State as well as Central Government for taking appropriate steps as described by the prediction. The methodology will be as follows: By taking various datasets from the government and by the help of clustering for the first step and in the next step linear regression for correctly predicting. The reason why we are using unsupervised learning for the first step is so that we won’t lose any data. The whole system will be available as a website with a GUI that can accept data for the learning process. The output will also depend upon the present state of the environment. The features for the learning process are: 1) Weather data (Rainfall, Winds, frequency of droughts, so on.) 2) Soil composition. 3) Types of fertilizers used. 4) The source for seeds/saplings 5) Seasonal data. 6) Whether mechanized farms or manual labor used. (If manual labor is not used what is the extent of mechanization) Any more help whatsoever received from the government.

Other Details

Paper ID: IJSRDV6I30199
Published in: Volume : 6, Issue : 3
Publication Date: 01/06/2018
Page(s): 777-780

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