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Santos, L.M
Mason, D
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Authors
Carrillo Montoya, K
De Guzman, C
Burgos, N
Ramos, L
Gyanwali, P
Uzoetoh, U
McCarty, D
Reddy Kalluri, R
Mason, D
Gimenez, L.M
Santos, L.M
Hass Bomfim Vieira, M
Topics
Predictive Modeling, Phenotyping, and Digital Tools for Decision Support
Artificial Intelligence, Big Data, and Advanced Analytics in Agriculture
Type
Oral
Poster
Year
2026
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1. Temporal NDRE Dynamics from UAS Imagery to Characterize Rice Drought Response

Characterizing drought resilience in rice remains challenging under increasing climate variability. Drought tolerance is a complex and dynamic trait that is difficult to quantify using traditional field phenotyping approaches, particularly when responses vary with time. High-throughput temporal phenotyping with unmanned aircraft systems (UAS) enables monitoring of canopy reflectance dynamics associated with water stress across the growing season. This study evaluated whether temporal...

2. Performance of Spatial Prediction Models Under Different Sample Densities in Corn Yield Maps

Yield maps are essential for the consistent management of crop variability. The accuracy of these maps, however, is directly affected by the density of data collected by grain harvesters and by the choice of interpolation method, whether deterministic or statistical. This study aimed to evaluate the performance of four spatial prediction methods, tested under different sample densities, for the development of corn yield maps. The study area corresponds to an 11-hectare commercial field, utilizing... L.M. Gimenez, L.M. Santos, M. Hass Bomfim Vieira