Built the first framework to map pre-emergence weed risk, published at IEEE
Arham developed WERM, a framework that estimates pre-emergence weed risk by fusing soil moisture, residue cover, structural disturbance and growing degree days. It combines ResNet-18 moisture and residue estimation from RGB field images, GLCM texture analysis and NASA POWER API thermal data through a log-space multiplicative model. Validated on 333 field images and 30 ground-truth soil samples with farmers of 20 to 30 years experience, it reached a ground-truth correlation of 0.879.
AI & Agricultural Science
Accepted at IEEE CIBCB 2026 in Greece
IEEE CIBCB 2026, 2026
Arham Sethi, YRI Fellow
A self-taught AI enthusiast researching robust CNNs who had presented at Young Scientist India but had no peer-reviewed publication.
Published a validated agricultural AI framework at IEEE CIBCB in Greece, checked against real farmers and ground-truth soil data.
Accepted for presentation and publication at IEEE CIBCB 2026 in Greece
Ground-truth correlation of 0.879 and robustness correlation at or above 0.990
Validated with farmers of 20 to 30 years experience and 30 ground-truth soil samples
First system to predict pre-emergence weed risk spatially, built at 15 years old
Aly Dhedhi, Accepted at IEEE CIBCB 2026