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Mahendra Bhandari
Texas A&M AgriLife, Corpus ChristiMeeting roles in:
Investigating the Potential of AI for Modeling Texas’s Carbon Budget Using Multi-Source Satellite Earth Observations And Limited In-Situ Ground Datasets
CODE-AG: A Multidisciplinary Framework for Digital Agriculture Education
Bayesian Autoencoder-Based Cotton Canopy Cover Estimation from Satellite Imagery Using UAV-Derived Latent Space Learning
In-Season Crop Growth Forecasting Using Satellite Imagery and Context-Aware Deep Learning
Monitoring spatio-temporal dynamics of forage species using high-resolution satellite and UAV imagery under rotational stocking management strategy
Generating High-Resolution Crop Canopy Height Information Using SkySat Stereopairs
A multi-modal learning approach for crop canopy volume estimation using satellite SAR and multi-spectral datasets, UAV images and representation learning
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