Implementation of Crop Yield Forecasting System based on Climatic and Agricultural Parameters

India is an agricultural country, with over half of the population dependent on agriculture. This creates a huge source of income and is an important sector in the Indian economy as it supplies about 17% of the total Gross Domestic Product (GDP). So agriculture in India is said to be the backbone of...

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Bibliographic Details
Published in:	2021 IEEE International Conference on Intelligent Systems, Smart and Green Technologies (ICISSGT) pp. 207 - 211
Main Authors:	Kandan, M., Niharika, Garapati Sravani, Lakshmi, Mallula Jhansi, Manikanta, Kallakuri, Bhavith, Korlepara
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01-11-2021
Subjects:	Agriculture crop yield prediction Crops Economic indicators machine learning Machine learning algorithms random forest Reliability Sociology Urban areas
Online Access:	Get full text
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Summary:	India is an agricultural country, with over half of the population dependent on agriculture. This creates a huge source of income and is an important sector in the Indian economy as it supplies about 17% of the total Gross Domestic Product (GDP). So agriculture in India is said to be the backbone of our Indian economy. Agriculture is not only an occupation for the people but also a way of life. Agriculture is a risky business and the reliance on reliable crop harvests is important in decisions related to agricultural risk management. We often see farmers suffer losses because of improper selection of the crops they grow without taking into account climatic and environmental boundaries. Crop prediction is a tough task because it depends on a variety of factors such as region, season, rainfall, etc. So a system is needed to recommend reliable crops to a farmer by considering climatic conditions, region of cultivation, etc. The proposed work uses the random forest algorithm to solve agricultural problems by recommending the good crop and its average yield to a farmer by analyzing climatic and agricultural parameters like state, season and rainfall.
DOI:	10.1109/ICISSGT52025.2021.00051