Titlebook: Digital Ecosystem for Innovation in Agriculture; Sanjay Chaudhary,Chandrashekhar M. Biradar,Mehul S Book 2023 The Editor(s) (if applicable

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Hyperspectral Remote Sensing for Agriculture Land Use and Land Cover Classificationnd spectra with higher correlation for agriculture and built-up classes. Classification is performed using seven per pixel classifiers and one ensemble classifier. Support vector (SVM) and ensemble classifiers for both Hyperion and AVIRIS-NG HyS images have shown higher accuracy with accuracy percen

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Ahmad M. Salih,Mazni Omar,Azman Yasinitoring at a larger scale. This chapter focuses on the use of open-source high-resolution (in terms of spectral, spatial, and temporal resolution) satellite data; open source cloud-based platforms, and big data algorithms that are reforming agriculture. This book chapter will detail the available op

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https://doi.org/10.1007/978-3-662-48634-4 CWR methods in India are mainly based on sparsely located in situ measurements and high-resolution remote sensing data, which limit the overall precision. To overcome the mentioned challenge, the deep learning architecture and soil moisture techniques have been used in this study to generate high-r