Prediction of Crop Yardsticks to Evaluate Crop Proliferation using a Continual Data Mining Solution

Authors

  • Ananthi. N
  • Abishek. B
  • Akash Eswar
  • Dheeban. K

Abstract

Quantifying crop growth at various stages of its life cycle is a daunting task which has to be done by farmers in order to get an idea about how the end yield will manifest. This task has been haunting farmers for more than centuries and analysts all over the world are trying to find out new methods to quantify crop yield and demystify the evaluation of various crop yardsticks. Helping farmers to take effective decisions in order to help them increase crop yield is the main focus of many current methodologies. The quality of the crops however, has not been suitably quantified and this has resulted in low crop sales in certain places. In India, crop quality is of utmost importance and many buyers refrain from purchasing low-quality crops directly from farmers thus resulting in inadequate food supply at various places. The proposed system looks to analyze various parameters affecting crop yield and quality and evaluate the end quality and yield with the currently available data. Various factors are considered for accurate estimation which includes climatic factors such as temperature, light usage efficiency, effective rainfall and wind speed along with soil, groundwater and various other factors. It is unprecedented in the fact it is intended to be while continuously learning and improving its accuracy over time. It is also an integrated solution applied as a continual solution which when implemented as a real-time system can collect real-time data and make predictions which aims to try and evaluate how the resulting crop yield and quality might be with the help of data mining techniques that work upon datasets which can contain historical and real-time data as the situation demands.

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Published

2020-04-09

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Section

Articles