Proceedings

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Buelvas, R
Dong , Y
Bae, I
Balint-Kurti, P
Van Oort, P
Pramanik, S
Gutiérrez, V
Barron, J
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Authors
Dong , Y
Wang , J
Li , C
Yang, G
Song, X
Huang , W
Gutiérrez, V
Ortega, R
Seo, Y
Lee, W
Kim, Y
Chung, S
Jang, S
Bae, I
Leksono, E
Adamchuk, V
Whalen, J
Buelvas, R
Alshihabi, O
Stenberg, B
Barron, J
van Evert, F
Van Oort, P
Maestrini, B
Pronk, A
Boersma, S
Kopanja, M
Mimić, G
Ottley, C
Kudenov, M
Balint-Kurti, P
Dean, R
Williams, C
Vincent, G
Kudenov, M
Balint-Kurti, P
Dean, R
Williams, C.M
Bari, M.A
Bakshi, A
Witt, T
Caragea, D
Jagadish, K
Felderhoff, T
Pramanik, S
Choton, J
Topics
Remote Sensing Applications in Precision Agriculture
Precision Nutrient Management
Precision Horticulture
Proximal and Remote Sensing of Soil and Crop (including Phenotyping)
In-Season Nitrogen Management
Big Data, Data Mining and Deep Learning
Artificial Intelligence (AI) in Agriculture
Type
Poster
Oral
Year
2012
2016
2018
2024
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1. 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