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Ritenour, M.A
Rumpf, T
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Authors
R, C
Rumpf, T
B, K
Hunsche, M
Pl, L
Noga, G
Choi, D
Lee, W
Schueller, J.K
Ehsani, R
Roka, F.M
Ritenour, M.A
Topics
Modeling and Geo-statistics
Sensor Application in Managing In-season Crop Variability
Type
Oral
Year
2010
2016
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1. Early Identification Of Leaf Rust On Wheat Leaves With Robust Fitting Of Hyperspectral Signatures

Early recognition of pathogen infection is of great relevance in precision plant protection. Disease detection before the occurrence of visual symptoms is of particular interest. By use of a laserfluoroscope, UV-light induced fluorescence data were collected from healthy and with leaf rust infected wheat leaves of the susceptible cv. Ritmo 2-4 days after inoculation under controlled conditions. In order to evaluate disease impact on spectral characteristics 215 wavelengths in the range of 370-800... C. R, T. Rumpf, K. B, M. Hunsche, L. Pl, G. Noga

2. A Precise Fruit Inspection System for Huanglongbing and Other Common Citrus Defects Using GPU and Deep Learning Technologies

World climate change and extreme weather conditions can generate uncertainties in crop production by increasing plant diseases and having significant impacts on crop yield loss. To enable precision agriculture technology in Florida’s citrus industry, a machine vision system was developed to identify common citrus production problems such as Huanglongbing (HLB), rust mite and wind scar. Objectives of this article were 1) to develop a simultaneous image acquisition system using multiple cameras... D. Choi, W. Lee, J.K. Schueller, R. Ehsani, F.M. Roka, M.A. Ritenour