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Lee, W
Karampoiki, M
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Authors
Lee, W
Kumar, A
Ehsani, R
Yang, C
Albrigo, L.G
Karampoiki, M
Todman, L
Mahmood, S
Murdoch, A
Paraforos, D
Hammond, J
Ranieri, E
Topics
Precision Horticulture
Big Data, Data Mining and Deep Learning
Type
Oral
Year
2010
2022
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Filter results2 paper(s) found.

1. Citrus Greening Disease Detection Using Airborne Multispectral And Hyperspectral Imaging

Citrus greening disease (Huanglongbing or HLB) has become a major catastrophic disease in Florida’s $9 billion citrus industry since 2005, and continued to be spread to other parts of the U.S. There is no known cure for this disease. As of October 2009, citrus trees in 2,702 different sections (square mile) in 34 counties were infected in Florida. A set of hyperspectral imageries were used to develop disease detection algorithms using image-derived spectral library, the mixture tuned... W. Lee, A. Kumar, R. Ehsani, C. Yang, L.G. Albrigo,

2. A Bayesian Network Approach to Wheat Yield Prediction Using Topographic, Soil and Historical Data

Bayesian Network (BN) is the most popular approach for modeling in the agricultural domain. Many successful applications have been reported for crop yield prediction, weed infestation, and crop diseases. BN uses probabilistic relationships between variables of interest and in combination with statistical techniques the data modeling has many advantages. The main advantages are that the relationships between variables can be learned using the model as well as the potential to deal with missing... M. Karampoiki, L. Todman, S. Mahmood, A. Murdoch, D. Paraforos, J. Hammond, E. Ranieri