Mosley, LawrencePham, HieuBansal, YogeshHare, EricMwanza, Charity2022-12-272022-12-272023-01-03978-0-9981331-6-4a2169b6a-15c0-4b81-bfda-4e48e162448dhttps://hdl.handle.net/10125/102723Modern trends in digital agriculture have seen a shift towards artificial intelligence for crop quality assessment and yield estimation. In this work, we document how a parameter tuned single-shot object detection algorithm can be used to identify and count sorghum heads from aerial drone images. Our approach involves a novel exploratory analysis that identified key structural elements of the sorghum images and motivated the selection of parameter-tuned anchor boxes that contributed significantly to performance. These insights led to the development of a deep learning model that outperformed the baseline model and achieved an out-of-sample mean average precision of 0.95.8engAttribution-NonCommercial-NoDerivatives 4.0 InternationalAnalytics and Decision Support for Green IS and Sustainability Applicationsagriculturecomputer visiondeep learningobject detectiontext10.24251/HICSS.2023.094