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A Fusion of Artificial Intelligence and Internet of Things for Emerging Cyber SystemsAnalysis of Agriculture Production and Impacts of Climate Change in South Asian Region: A Concern Related with Healthcare 4.0 Using ML and Sensors

A Fusion of Artificial Intelligence and Internet of Things for Emerging Cyber Systems: Analysis... [The Effect of Global Warming and rapid changing climate in an indefinite manner is a major concern and all domain of science are trying to address it in their ways. It is not only creating challenges to food production, yet to the human health. The presented research work is all about the prediction of the yield of agriculture of the land without involving any activity of humans and this makes our procedure superfast and quite easy and reliable for humans and hence the name of the project “Predicting Agricultural Productivity”. Main purpose of the research work includes the implementation and training of machine learning algorithms for the prediction of the yield of agriculture so that the error can get minimized and accuracy gets maximized. For training of the model, a collection of features from actual yield and pictures of satellite is extracted by us. After This phase, a suitable algorithm like Naive Bayes, NN and its variant are chosen and used as the mathematical way to learn the parameters that are based on the features of yield. Then during study, harvest of agriculture is prognoses for a separate set of data. Data that is prognosticated is compared in contrast to the actual land yield. The manuscript also focuses the different data sets which are obtained by satellite imaging and using remote sensing, the clear mapping of current condition is obtained which helps to predict the yield in better way.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

A Fusion of Artificial Intelligence and Internet of Things for Emerging Cyber SystemsAnalysis of Agriculture Production and Impacts of Climate Change in South Asian Region: A Concern Related with Healthcare 4.0 Using ML and Sensors

Part of the Intelligent Systems Reference Library Book Series (volume 210)
Editors: Kumar, Pardeep; Obaid, Ahmed Jabbar; Cengiz, Korhan; Khanna, Ashish; Balas, Valentina Emilia
Rastogi, Rohit; Maheshwari, Sarvam; Garg, Priyanshi; Rastogi, Mukund; Kumar, Pardeep
A Fusion of Artificial Intelligence and Internet of Things for Emerging Cyber Systems — Aug 24, 2021
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Publisher
Springer International Publishing
Copyright
© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
ISBN
978-3-030-76652-8
Pages
41 –65
DOI
10.1007/978-3-030-76653-5_3
Publisher site
See Chapter on Publisher Site

Abstract

[The Effect of Global Warming and rapid changing climate in an indefinite manner is a major concern and all domain of science are trying to address it in their ways. It is not only creating challenges to food production, yet to the human health. The presented research work is all about the prediction of the yield of agriculture of the land without involving any activity of humans and this makes our procedure superfast and quite easy and reliable for humans and hence the name of the project “Predicting Agricultural Productivity”. Main purpose of the research work includes the implementation and training of machine learning algorithms for the prediction of the yield of agriculture so that the error can get minimized and accuracy gets maximized. For training of the model, a collection of features from actual yield and pictures of satellite is extracted by us. After This phase, a suitable algorithm like Naive Bayes, NN and its variant are chosen and used as the mathematical way to learn the parameters that are based on the features of yield. Then during study, harvest of agriculture is prognoses for a separate set of data. Data that is prognosticated is compared in contrast to the actual land yield. The manuscript also focuses the different data sets which are obtained by satellite imaging and using remote sensing, the clear mapping of current condition is obtained which helps to predict the yield in better way.]

Published: Aug 24, 2021

Keywords: Agriculture; Squared error; Machine learning; Re-projection; Masking; Modis; Test cases

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