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Hock, M.W
Junior, C.S
Bari, M.A
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Authors
Junior, C.S
Hirakawa, A.R
Samiappan, S
Henry, B
Moorhead, R.J
Hock, M.W
Bari, M.A
Bakshi, A
Witt, T
Caragea, D
Jagadish, K
Felderhoff, T
Pramanik, S
Choton, J
Topics
Information Management and Traceability
Unmanned Aerial Systems
Big Data, Data Mining and Deep Learning
Type
Poster
Oral
Year
2012
2016
2024
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1. Ontology for Data Representation in the Production of Cotton Fiber in Brazil

... C.S. Junior, A.R. Hirakawa

2. Plant Stand Count and Corn Crop Density Assessment Using Texture Analysis on Visible Imagery Collected Using Unmanned Aerial Vehicles

Ensuring successful corn farming requires an effective monitoring program to collect information about stand counts at an early stage of growth and plant damages due to natural calamities, farming equipment, hogs, deer and other animals. These monitoring programs not only provide a yield estimate but also help farmers and insurance companies in assessing the causes of damages. Current field-based assessment methods are labor intensive, costly, and provide very limited information. Manual assessment... S. Samiappan, B. Henry, R.J. Moorhead, M.W. Hock

3. Deep Learning to Estimate Sorghum Yield with Uncrewed Aerial System Imagery

In the face of growing demand for food, feed, and fuel, plant breeders are challenged to accelerate yield potential through quick and efficient cultivar development. Plant breeders often conduct large-scale trials in multiple locations and years to address these goals. Sorghum breeding, integral to these efforts, requires early, accurate, and scalable harvestable yield predictions, traditionally possible only after harvest, which is time-consuming and laborious. This research harnesses high-throughput... M.A. Bari, A. Bakshi, T. Witt, D. Caragea, K. Jagadish, T. Felderhoff