Detection of Huanglongbing in Florida using fluorescence imaging spectroscopy and machine-learning methods

Author(s)

Wetterich, Caio Bruno ; de Oliveira Neves, Ruan Felipe ; Belasque, Jose ; Ehsani, Reza ; Marcassa, Luis Gustavo

• Document type: Journal article
• Source: APPLIED OPTICS; v. 56, n. 1, p. 9-pg., 2017-01-01.

Abstract

In this study, we combine a fluorescence imaging technique and two machine-learning methods to discriminate Huanglongbing (HLB) disease from zinc-deficiency stress on samples from Florida, USA. Two classification methods, support vector machine (SVM) and artificial neural network (ANN), are used. Our classification results present high accuracy for both classification methods: 92.8% for SVM and 92.2% for ANN. The results from Florida are also compared to results from Sao Paulo State, Brazil. This comparison indicates that the present technique can be applied to discriminate HLB from zinc deficiency in both states. (C) 2016 Optical Society of America (AU)

• FAPESP's process: 11/22275-6 - Fluorescence Imaging using variable liquid crystal tunable filter for citrus diseases
• Grantee: Caio Bruno Wetterich
• Support Opportunities: Scholarships in Brazil - Doctorate
• FAPESP's process: 13/11023-1 - Use o fluorescence images for a comparative study of diseases in citrus samples from Florida and São Paulo
• Grantee: Caio Bruno Wetterich
• Support Opportunities: Scholarships abroad - Research Internship - Doctorate
• FAPESP's process: 10/16536-9 - Fluorescence Imaging Applied to Citrus Diseases
• Grantee: Luis Gustavo Marcassa
• Support Opportunities: Regular Research Grants