Devi Sri, D and Saikumar, P and Pranati, J and Sameer Kumar, C V and Sharma, I (2026) Smart Farming- Trends, Innovation and Challenges. In: Artificial Intelligence and Data Sciences for Precision Agriculture. Springer, Cham, pp. 211-231. ISBN 978-3-032-12769-3
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The challenge to feed billions with limited resources has greatly shifted agriculture from being labour-intensive to technology and data driven mode. This forms the idea of smart farming which combines novel techniques such as Internet of Things (IoT), sensors, drones, robotics, block chain technology and big data analytics into agriculture. This smart farming involves recent trends including data driven farming using sensors and GPS based analytics, improving prediction efficiencies for productivity using artificial intelligence (AI) and machine learning, automating machinery for minimising labour-dependence and post-harvest losses, blockchains to improve traceability in support to conventional farming. The real-time big data availability is offering better monitoring and mapping of soil health, weather parameters, crop cover, abiotic and biotic stresses, nutrient parameters and post-harvest losses, thus improving decision-making ability of farmers, researchers and policy makers. Enabling these practices is set with various hurdles at every stakeholder’s end, but it drives towards resource-efficient farming to handle the current climate change and food security.
| Item Type: | Book Section |
|---|---|
| Divisions: | Global Research Program - Accelerated Crop Improvement |
| CRP: | UNSPECIFIED |
| Uncontrolled Keywords: | Smart farming, IoT, Sensors, AI, ML, Big data, Food security |
| Subjects: | Others > Agriculture Others > Plant Genetics Others > Food Security Others > Digital Agriculture |
| Depositing User: | Mr Nagaraju T |
| Date Deposited: | 07 May 2026 08:12 |
| Last Modified: | 07 May 2026 08:12 |
| URI: | http://oar.icrisat.org/id/eprint/13621 |
| Acknowledgement: | UNSPECIFIED |
| Links: |
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