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Hanyabui, E
Huang, W
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Authors
Song, X
Zhao, C
Chen, L
Huang, W
Cui, B
Huang, W
Zhao, C
Freire de Oliveira, M.F
Ortiz, B.V
Souza, J.B
Bao, Y
Hanyabui, E
Oliveira, M.F
Ortiz, B.V
Hanyabui, E
Costa Souza, J.B
Sanz-Saez, A
Luns Hatum de Almeida , S
Pilcon, C
Vellidis, G
Topics
Spatial Variability in Crop, Soil and Natural Resources
Remote Sensing Applications in Precision Agriculture
Artificial Intelligence (AI) in Agriculture
Proximal and Remote Sensing of Soils and Crops (including Phenotyping)
Type
Poster
Oral
Year
2012
2010
2024
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Filter results4 paper(s) found.

1. Winter Wheat Growth Uniformity Monitoring Through Remote Sensed Images

  ... X. Song, C. Zhao, L. Chen, W. Huang, B. Cui

2. Inversion Of Vertical Distribution Of Chlorophyll Concentration By Canopy Reflectance Spectrum In Winter Wheat

          The objective of this study was to investigate the inversion of foliage chlorophyll concentration(Chl) vertical-layer distribution by bidirectional reflectance difference function (BRDF) data, so as to provide guidance on the application of fertilizer. The ratio of transformed chlorophyll absorption reflectance index (TCARI) to optimized soil adjusted vegetation index (OSAVI) was named as canopy chlorophyll inversion index (CCII) in... W. Huang, C. Zhao

3. Towards a Digital Peanut Profile Board: a Deep Learning Approach

Artificial intelligence techniques, particularly deep learning, offer promising avenues for revolutionizing object detection and counting algorithms in the context of digital agriculture. The challenges faced by peanut farmers, particularly the precise determination of optimal maturity for digging, have prompted innovative solutions. Traditionally, peanut maturity assessment has relied on the Peanut Maturity Index (PMI), employing a manual classification process with the aid of a peanut profile... M.F. Freire de oliveira, B.V. Ortiz, J.B. Souza, Y. Bao, E. Hanyabui

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 decisions... M.F. Oliveira, B.V. Ortiz, E. Hanyabui, J.B. Costa souza, A. Sanz-saez, S. Luns hatum de almeida , C. Pilcon, G. Vellidis