Exploring Vegetative Indices for Yield Prediction using Sentinel 2 Data – A Study in a Select Region of Karnataka

Authors

  • Geetha M. Bapuji Institute of Engineering and Technology, Davanagere – 577004,, INDIA https://orcid.org/0000-0002-8762-980X
  • Asha Gowda Karegowda SIT., Tumakuru – 572102, INDIA
  • Nandeesha R. Dayananda Sagar College of Engineering, Bangalore-560078, India
  • Nagaraj B. V. KIAMS, Harihara,-577601 India

Keywords:

Crop Yield, NDVI, NDWI, SAVI, Correlation and Regression Analysis

Abstract

India is an agrarian economy and largest share of population depend on agriculture. Though there are mechanisms to approximately estimate crop yield by means of controlled experiments or past data, the reliability is limited. As a matter of fact, the crop yield is based on estimates that may suffer from multiple bias. In recent years, remote sensing images augmented with machine learning and deep learning techniques help us get the efficient crop yield statistics based on the crop on the field which help policymakers in devising better policies and governance. Remote sensing images of crops under study when subjected to machine learning techniques, one can classify the images into homogenous crop classes and record crop health / growth indicators such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI) and Soil Adjusted Vegetative Index (SAVI) based on the training dataset and image quality, which further leads to crop yield estimates with desired level of accuracy. The present study investigates the relationship between yield influencing parameters such as physical variables, soil, weather characteristics and vegetation indices. Using correlation and multiple regression analysis, most efficient parameters that best estimate the crop yield is determined using the satellite data obtained for a study region in central part of the state of Karnataka. It was found that one of the vegetation indices, Soil Adjusted Vegetative Index values can predict near to accurate crop yield values by the end of 81 days after transplantation of paddy where as NDVI and NDVI can give yield estimated only after 116 days of transplantation. Thus crop yield estimates using SAVI works better in terms of predicting paddy yield at least one month before (after 81 days of transplantation) actual harvesting i.e., after 120 days as practiced by farmers in the study area.

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References

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Study Area (Davangere Taluk)

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Published

16.12.2022

How to Cite

Geetha M., Asha Gowda Karegowda, Nandeesha R., & Nagaraj B. V. (2022). Exploring Vegetative Indices for Yield Prediction using Sentinel 2 Data – A Study in a Select Region of Karnataka. International Journal of Intelligent Systems and Applications in Engineering, 10(4), 574–581. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2326

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Research Article