no code implementations • 20 Jun 2023 • Yogesh Bansal, Dr. David Lillis, Prof. Mohand Tahar Kechadi
The main objective of this study is to predict winter wheat crop yield using ML models on multiple heterogeneous datasets, i. e., soil and weather on a zone-based level.
no code implementations • 20 Jun 2023 • Yogesh Bansal, David Lillis, Mohand Tahar Kechadi
We showed that it outperforms the existing ML models.
no code implementations • 24 Sep 2020 • Lawrence Mosley, Hieu Pham, Yogesh Bansal, Eric Hare
Modern trends in digital agriculture have seen a shift towards artificial intelligence for crop quality assessment and yield estimation.