1 code implementation • 29 Nov 2023 • Depanshu Sani, Sandeep Mahato, Sourabh Saini, Harsh Kumar Agarwal, Charu Chandra Devshali, Saket Anand, Gaurav Arora, Thiagarajan Jayaraman
Out of the 2, 370 samples, 351 paddy samples from 145 plots are annotated with multiple crop parameters; such as the variety of paddy, its growing season and productivity in terms of per-acre yields.
Ranked #1 on Crop Yield Prediction on SICKLE
no code implementations • 25 Sep 2022 • Depanshu Sani, Sandeep Mahato, Parichya Sirohi, Saket Anand, Gaurav Arora, Charu Chandra Devshali, Thiagarajan Jayaraman, Harsh Kumar Agarwal
We also propose a yield prediction strategy that uses time-series data generated based on the observed growing season and the standard seasonal information obtained from Tamil Nadu Agricultural University for the region.
1 code implementation • 26 Jul 2022 • Ashima Garg, Depanshu Sani, Saket Anand
In this paper, we propose a novel approach for learning Hierarchy Aware Features (HAF) that leverages classifiers at each level of the hierarchy that are constrained to generate predictions consistent with the label hierarchy.
no code implementations • 6 May 2022 • Depanshu Sani, Sandeep Mahato, Parichya Sirohi, Saket Anand, Gaurav Arora, Charu Chandra Devshali, T. Jayaraman
The F1-score is increased by 7% when using multispectral data of MSTR images as compared to the best results obtained from HSTR images.