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Karn, R
Melnitchouck, A
Marin, A
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Authors
Cointault, F
Marin, A
Journaux, L
Miteran, J
Martin, R
Hoffmann Silva Karp, F
Adamchuk, V
Melnitchouck, A
Dutilleul, P
Karn, R
Gu, H
Adedeji, O
Guo, W
Adedeji, O.I
Ghimire, B.P
Gu, H
Karn, R
Lin, Z
Guo, W
Topics
Modeling and Geo-statistics
Geospatial Data
Applications of Unmanned Aerial Systems
Type
Oral
Poster
Year
2010
2022
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Authors

Filter results4 paper(s) found.

1. Wheat Growth Stages Discrimination Using Generalized Fourier Descriptors In Pattern Recognition Context

... F. Cointault, A. Marin, L. Journaux, J. Miteran, R. Martin

2. Optimization of Batch Processing of High-density Anisotropic Distributed Proximal Soil Sensing Data for Precision Agriculture Purposes

The amount of spatial data collected in agricultural fields has been increasing over the last decade. Advances in computer processing capacity have resulted in data analytics and artificial intelligence becoming hot topics in agriculture. Nevertheless, the proper processing of spatial data is often neglected, and the evaluation of methods that efficiently process agricultural spatial data remains limited. Yield monitor data is a good example of a well-established methodology for data processing... F. Hoffmann silva karp, V. Adamchuk, A. Melnitchouck, P. Dutilleul

3. Evaluation of Unmanned Aerial Vehicle Images in Estimating Cotton Nitrogen Content

Estimating crop nitrogen content is a critical step for optimizing nitrogen fertilizer application. The objective of this study was to evaluate the application of UAV images in estimating cotton (Gossypium hirsutum L.) N content. This study was conducted in a dryland cotton field in Garza County, Texas, in 2020. The experiment was implemented as a randomized complete block design with three N rates of 0, 34, and 67 kg N ha-1. A RedEdge multispectral sensor was used to acquire... R. Karn, H. Gu, O. Adedeji, W. Guo

4. Estimation of Cotton Biomass Using Unmanned Aerial Systems and Satellite-based Remote Sensing

Satellite and unmanned aerial system (UAS) images are effective in monitoring crop growth at various spatial, temporal, and spectral scales. The objective of the study was to estimate cotton biomass at different growth stages using vegetation indices (VIs) derived from UAS and satellite images. This research was conducted in a cotton field in Hale County, Texas, in 2021. Data collected include 54 plant samples at different locations for three dates of the growing season. Multispectral images from... O.I. Adedeji, B.P. Ghimire, H. Gu, R. Karn, Z. Lin, W. Guo