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Mohammad Abdullah Bari on Muck Rack

Mohammad Abdullah Bari

As seen in: BioRxiv
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Articles

Deep Learning for Sorghum Yield Forecasting using Uncrewed Aerial Systems and Lab-Derived Imagery

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By Mohammad Abdullah Bari, Trevor Witt, Aliva Bakshi, Swaraj Pramanik
| BioRxiv
Abstract The AI revolution, advanced Graphics Processing Units (GPUs), and open-source platforms have enabled Machine Learning (ML) and Deep Learning (DL) algorithms to rapidly and accurately extract phenotypic features from Uncrewed Aerial System (UAS)-derived imagery. Such advancement leads to phenotypic digitization and sorghum yield forecasting. Yield analytics are critical for breeding programs to assess the genetics and breeding potential of genotypes to enhance cultivar development.
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