IIIT-Delhi researchers develop AI dataset to monitor crops and predict yields
Headline by Prism · from 1 report
Researchers from IIIT-Delhi and MS Swaminathan Research Foundation developed a multi-satellite dataset called SICKLE to improve AI-driven crop monitoring and yield prediction.
Telangana TodayThe brief
Written by software from the 1 report below.
- Researchers created a multi-satellite dataset named SICKLE to train AI models in identifying crop types, growth stages, and yields.
- The dataset combines satellite imagery from Landsat-8, Sentinel-1, and Sentinel-2 with field data collected from agricultural plots in the Cauvery Delta.
- It contains over 200,000 images covering 388 plots and 21 different crop types.
- This tool aims to assist farmers, researchers, and policymakers in conducting large-scale agricultural monitoring through automated analysis.
What to watch next
- Further development of AI models using the SICKLE dataset for real-world agricultural deployment.
- Efforts to address challenges like limited field survey availability and small-plot identification in satellite imagery.
The points restate the reports; where one says why it matters, that is Prism's reading, not a reported fact.
Coverage
1 outlet
All filed from India
NamedIndia · Cauvery Delta · Depanshu Sani · Gaurav Arora · Harsh Kumar Agarwal · IIIT-Delhi · Landsat-8 · M.S. Swaminathan Research Foundation · Saket Anand · Sentinel-1 · Sentinel-2 · SICKLE · Sourabh Saini
The 1 report is listed beside the record.
Corrections and versions
A correction says what was wrong and why. Every earlier headline and brief of this record is kept.
Something wrong?
Say what, and it arrives with this record's address filled in. A correction is welcome.
Ask this story
Answers cite the 1 report above, or say they can't.