CROP YIELD PREDICTION IN PRECISION AGRICULTURE USING MACHINE LEARNING TECHNIQUES: A STUDY

Authors

  • Rakesh Kumar Ray , Sujata Chakravarty , SMIEEE

Abstract

In the field of agriculture, the accurate estimation of yield production is crucial for farmers and Govt. Remote
sensing plays an important role which collects data from the field level, that is been deployed in the
contemporary farming system for building decision-making tool, which can predict accurate yield production
and other field level parameters by minimizing operation cost. The remote sensing approach generates a lot of
data gathered from different platforms, so it becomes essential to introduce the Machine Learning technique to
manage the huge data. Machine Learning has the capability to analyze those huge numbers of inputs and handle
the non-linear task to produce knowledge, which can use in decision making. This paper discussed several
research works for irrigation control using machine learning techniques, plant disease monitoring, accurate yield
production. This paper concludes that the hybrid machine learning technique will give an effective and accurate
model for predicting yield production by using remote sensing for decision making and environment state
estimation in agriculture.

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Published

2020-12-01

How to Cite

Rakesh Kumar Ray , Sujata Chakravarty , SMIEEE. (2020). CROP YIELD PREDICTION IN PRECISION AGRICULTURE USING MACHINE LEARNING TECHNIQUES: A STUDY. International Journal of Modern Agriculture, 9(4), 197 - 209. Retrieved from http://modern-journals.com/index.php/ijma/article/view/203

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Articles