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International Journal of Computer Engineering in Research Trends. Scholarly, Peer-Reviewed, Platinum Open Access and Multidisciplinary

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Dynamic Crop-yield and Price Forecasting using Machine Learning

Chaitra Kotnikall, Jayateerth Vadavi, , ,
1*Student, Dept. Of CSE, Shri Dharmasthala Manjunatheshwara College of Engineering, Dharwad, India. 2: Associate Professor, Dept. Of CSE, Shri Dharmasthala Manjunatheshwara College of Engineering, Dharwad, India.

Background: Guaranteeing food profitability is a significant issue for creating nations like India, where more than 33% of the individuals is live in neediness. To estimate cost, there is no system in place to advise farmers what crops to grow. Hence, this paper explains the attempt to predict crop price that a farmer can obtain from his land by analyzing patterns in past data. Methods/Statistical analysis: This method makes use of several data such as rainfall, temperature, market prices, and past yield of a crop. The supervised machine learning algorithm, namely, the Decision tree algorithm and analyse the data and predict for the new set of data, is implemented. It also predicts the price and the gain for the next twelve months over the past twelve months and gives the time series analysis of the same. Findings: The proposed model is developed to help farmers make better decisions concerning which crop is most suitable during his desired time of sowing and the location. This System predicts the yield and price of the crop of choice, giving the farmer useful information well before starting the process of cultivation. Improvements: The System can introduce and make available climate-aware cognitive farming techniques and identifying systems of crop monitoring, early warning on pest/disease outbreak based on advanced AI innovation.

Chaitra Kotnikall, Jayateerth Vadavi."Dynamic Crop-yield and Price Forecasting using Machine Learning ". International Journal of Computer Engineering In Research Trends (IJCERT) , ISSN:2349-7084, Vol.7, Issue 06,pp.46-51, June - 2020, URL:,

Keywords : Crop-yield, Supervised Machine learning in Agriculture, Decision tree, Forecasting, Analysis.

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