Pranuthi, G and Srikanth, R (2023) Globally Scalable and Locally Adaptable Solutions for Agriculture. In: Digital Ecosystem for Innovation in Agriculture. Studies in Big Data, 121 . Springer, Singapore, pp. 89-108. ISBN 978-981-99-0577-5
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Precision agriculture and smart farming will soon replace the conventional methods adopted in agriculture; these technologies are capable of increasing the productivity of crops and also optimizing the inputs, and at the same time sustaining the environmental resources. The ability of satellite data in precision farming has been made evident through several projects and research activities adopted across India. The past decade has seen a rapid increase in the use of satellite-based remote sensing data in crop mapping and crop monitoring. This growth can be attributed to at least three factors—the availability of open remote sensing data, advanced machine learning methods, and access to cloud computing platforms that can handle big data storage and processing. In this book chapter, we propose Globally Scalable and Locally Adaptable Solutions for Agriculture that can facilitate in crop monitoring 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 open-source satellite data and platforms with use cases. We also discuss in detail the methodology of developing a crop monitoring system using Google Earth Engine that can be globally scaled and locally adaptable, using a case study of the wheat crop for Samastipur district, Bihar, India.
| Item Type: | Book Section |
|---|---|
| Divisions: | Global Research Program - Resilient Farm and Food Systems |
| CRP: | UNSPECIFIED |
| Series Name: | Studies in Big Data |
| Uncontrolled Keywords: | Artificial Intelligence, Big data platforms, Google Earth Engine, Open-source satellite data, Sentinel |
| Subjects: | Others > Agriculture Others > Remote Sensing |
| Depositing User: | Mr Nagaraju T |
| Date Deposited: | 05 Feb 2024 08:48 |
| Last Modified: | 05 Feb 2024 08:48 |
| URI: | http://oar.icrisat.org/id/eprint/12432 |
| Acknowledgement: | UNSPECIFIED |
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