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Title |
Crop Yield and Crop Production Predictions using Machine Learning |
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Author |
Divya Goel and Payal Gulati |
| Citation |
Vol. 23 No. 9 pp. 17-28 |
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Abstract |
Today Agriculture segment is a significant supporter of Indian economy as it represents 18% of India's Gross Domestic Product (GDP) and it gives work to half of the nation's work power. Farming segment are required to satisfy the expanding need of food because of increasing populace. Therefore, to cater the ever-increasing needs of people of nation yield prediction is done at prior. The farmers are also benefited from yield prediction as it will assist the farmers to predict the yield of crop prior to cultivating. There are various parameters that affect the yield of crop like rainfall, temperature, fertilizers, ph level and other atmospheric conditions. Thus, considering these factors the yield of crop is thus hard to predict and becomes a challenging task. Thus, motivated this work as in this work dataset of different states producing different crops in different seasons is prepared; which was further pre-processed and there after machine learning techniques Gradient Boosting Regressor, Random Forest Regressor, Decision Tree Regressor, Ridge Regression, Polynomial Regression, Linear Regression are applied and their results are compared using python programming. |
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Keywords |
Agriculture, Machine Learning, Random Forest, Decision Tree, Linear Regression, Gradient Boosting Regression, Polynomial Regression, Ridge Regression. |
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