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Machine Vision / Multispectral & Hyperspectral Imaging Applications to Precision Agriculture
Proximal and Remote Sensing of Soils and Crops (including Phenotyping)
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Authors
Abdelaty, E.F
Abderaouf, E.A
Abdinoor, J.A
Adamchuk, V
Agampodi, G.S
Alchanatis, V
Alchanatis, V
Allam, D.G
Amaral, L.R
Armstrong, S
Arnall, B
Arnall, B
Bai, G
Bantchina, B
Barai, K
Bathke, K.J
Bazzi, C.L
Bede, L
Bhandari, M
Bishop, T
Biswas, A
Biswas, A
Borbás, Z
Bourlai, T
Bullock, D
Chamara, N
Chang, Y
Choudhury, S.D
Chung, S
Cohen, S
Cohen, Y
Costa Souza, J.B
Czarnecki, J
Daggupati, P
Dhiman, V
Dua, A
Dua, A
Dutilleul, P
E. Flores, A
Ehsani, R
Eldeeb, E
Ewanik, C
Fathololoumi, S
Fernandez, C.J
Filippi, P
Firozjaei, M.K
Flores, A
Flores, P
Fritz, B.K
Ge, Y
Ge, Y
Gebler, L
Ghimire, B
González Piqueras, J
Gulandaz, M
Gummi, S
Guo, W
Gómez-Candón, D
Hanyabui, E
Hashim, Z.K
Hegedűs, G
Herrmann, I
Hessel, R
Hessel, R
Hijazi, B
Hodeghatta, U.R
Hoffmann Silva Karp, F
Horváth, B
Huang, Y
Jørgensen, R.N
Jiménez Castaño, V
KABIR, M
Kechchour, A
Kechchour, A
Kemeshi, J.O
Krüger, N
Kukorelli, G
Kulmany, I.M
Kósa, A
Lacerda, L
Lacerda, L
Lacerda, L
Lacerda, L
Landivar, J.A
Laursen, M.S
Lee, W
Levi, O
Li, H
Li, M
Lu, J
Lu, J
Luck, J.D
Lund, E
Lund, T
Lund, T
Luns Hatum de Almeida, S
López-Urrea, R
Maatougui, M
Maktabi, S
Maxton, C.R
Melnitchouck, A
Melo, D.D
Miao, Y
Miao, Y
Midtiby, H.S
Miguez, F
Mizuta, K
Mokhtari, A
Montoya Sevilla, F
Moulay, H
Muller, I
Nichols, R.L
Odvody, G.N
Oldoni, H
Oliveira, M.F
Oliveira, W.K
Ortiz, B.V
PHILLIPS, S
Palla, S
Paz Kagan, T
Pecze, R
Phillips, S
Pilcon, C
Pilcon, C
Pinke, G
Puntel, L
Pérez García, Y
Quinn, D.J
Rabello, L.M
Rabia, A.H
Rabia, A.H
Rai, S
Rauber, L.A
Reinholz, A
Rubaino Sosa, S.A
Sahoo, M
Sanz-Saez, A
Schapaugh, W
Schapaugh, W
Schenatto, K
Schumacher, L
Shafik, K
Sharda, A
Sharda, A
Sharma, V
Sharma, V
Shrestha, S
Smith, B.K
Sobjak, R
Stencinger, D
Suh, C
Sun, R
Sysskind, M
Sánchez Tomás, J
Sánchez Virosta, Ã
Tarshish, R
Tilse, M.J
Varga, Z
Vellidis, G
Vellidis, G
Wang, K
Xu, X
Yang, C
Yang, C
Yang, C
Yu, K
Yu, K
Zhang, J
Zhang, J
Zhang, J
Zhang, Y
Zhao, H
Zhoa, L
Zhou, J
Zsebő, S
cointault, F
da Cunha, I.A
dos Santos, C.L
paindavoine, M
pieters, J
vangeyte, J
Topics
Proximal and Remote Sensing of Soils and Crops (including Phenotyping)
Machine Vision / Multispectral & Hyperspectral Imaging Applications to Precision Agriculture
Type
Oral
Poster
Year
2024
2012
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Filter results4 paper(s) found.

1. Integrating Nonlinear Models and Remotely Sensed Data to Estimate Crop Cardinal Dates

Crop planting and harvest dates are a major component affecting agricultural productivity, risk, and nutrient cycling. The ability to track these cardinal dates allows researchers to investigate strategies to manage risk and adapt to climate change. This study was conducted to determine whether nonlinear statistical models combined with remotely sensed data from satellites can be used to estimate planting and harvest dates. Time of planting and harvest were reported by farmers for 16 commerci... C.L. Dos santos, F. Miguez, L. Puntel, D. Bullock

2. Enhancing Nutrient-related Stress Detection: High Throughput Phenotyping and Image Analysis for Improved Precision

In the 21-century agriculture has the unique responsibility to provide food, fuel, fiber and feed for the growing population under the stress of climate change and diminishing natural resources. A feat that will take considerable change to the sustainability of such practices. One of which is the idea of assessing phenotypic expression of complex traits in response to environmental factors. This idea elevates the use of phenotyping to quantitatively monitor stress manifestation.  ... K.J. Bathke, Y. Ge, S.D. Choudhury, J.D. Luck

3. Field Mapping for Aflatoxin Assessment in Peanut Crops Using Thermal Imagery

Aflatoxin is a toxic carcinogenic compound produced by certain species of Aspergillus fungi, which has a significant impact on peanut production. Aflatoxin levels above a certain threshold (20 ppb in the USA and 4 ppb in Europe) make peanuts unsuitable for export, resulting in significant financial losses for farmers and traders. Unmanned Aerial Vehicles (UAVs) are becoming increasingly popular for remote sensing applications in agriculture. Leveraging this advancement, UAV-based thermal imag... S. Shrestha, L. Lacerda, G. Vellidis, C. Pilcon, S. Maktabi, M. Sysskind

4. Use of Crop and Drought Spectral Indices to Support Harvest Decisions of Peanut Fields in Alabama

Harvest efficiency expressed in quantity and quality of peanut fields could increase if farmers are provided with tools to support harvest decisions. Peanut farmers still rely on a visual and empiric method to assess the right time of peanut maturity but this method does not account for within-field variability of crop growth and maturity. The integration of spectral vegetation indices to assess drought, soil moisture, and crop growth to predict peanut maturity can help farmers strengthen dec... M.F. Oliveira, B.V. Ortiz, E. Hanyabui, J.B. Costa souza, A. Sanz-saez, S. Luns hatum de almeida , C. Pilcon, G. Vellidis