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HYPERSPECTRAL IMAGERY FOR CROP YIELD ESTIMATION IN PRECISION AGRICULTURE USING MACHINE LEARNING APPROACHES: A REVIEW
Authors
Renuka Bhokarkar Vaidya,Dhananjay Nalawade,Dr KV Kale,Vaibhav A. Didore
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Keywords
Precision Agriculture, Remote sensing, Crop yield prediction, Machine learning, Hyperspectral Imagery
Abstract
Crop yield estimation is one of the most significant issues for agricultural management, and one of the areas that precision farming techniques can offer the greatest benefit. Crop yield prediction is an art of forecasting the yield of crop before harvesting. Prediction of crop yield will be very useful for the government to make food policies, market price, import and export policies and proper warehousing well in time. Remote sensing technologies, together with the use machine learning have been shown to be effective in monitoring crop yield, improving land management, and facilitating the implementation of precision farming techniques. The main goals of crop yield estimation of precision agriculture is achieving maximum crop yield at minimum cost with a healthy ecosystem using combination of technologies. In India climatic conditions effected more on crop yield estimation. Other environmental factors also need to concentrate while studying crop yield such as temperature, rainfall, vegetative index, soil type, texture and nutrients. Crop yield prediction and estimation is very important to our government on the aspect of making food policies, crop insurance, market price, import and export policies etc. Use of remote sensing technologies is currently recognized to be the next generation of technical innovations that have the potential to refine the quality of within-field yield mapping technologies. we can implement various machine learning algorithms like ANN and Decision Tree on this research for crop estimation. The performance of ANNs can compare with four conventional modelling methods, namely, Normalized Difference Vegetation Index (NDVI), Simple Ratio (SR), Photochemical Reflectance Index (PRI), and Stepwise Multiple Linear Regression (SMLR) models. Principal Component Analysis (PCA) can also use to reduce the dimensionality of the hyperspectral imagery. The prediction can be of two types they are classification or regression. Classification can be to identify in which classes the crop growth falls. For example: Classes such as, well grown, medium grown and under grown. Another type is regression where it will give numerical estimated value. So that value of percentage for the estimated crop yield can be obtained.
IJCRT's Publication Details
Unique Identification Number - IJCRT2109095
Paper ID - 210955
Author type - Indian Author
Page Number(s) - a777-a789
Pubished in - Volume 9 | Issue 9 | September 2021
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No Of Downloads - 824
Author Country - India, 431002, Aurangabad, Aurangabad, 431002, Science and Technology
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Renuka Bhokarkar Vaidya,Dhananjay Nalawade,Dr KV Kale,Vaibhav A. Didore, "HYPERSPECTRAL IMAGERY FOR CROP YIELD ESTIMATION IN PRECISION AGRICULTURE USING MACHINE LEARNING APPROACHES: A REVIEW", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.9, Issue 9, pp.a777-a789, September 2021, Available at :http://www.ijcrt.org/papers/IJCRT2109095.pdf
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