title: Detection of citrus canker and Huanglongbing using fluorescence imaging spectroscopy and support vector machine technique.
abstract: Citrus canker and Huanglongbing (HLB) are citrus diseases that represent a serious threat to the citrus production worldwide and may cause large economic losses. In this work, we combined fluorescence imaging spectroscopy (FIS) and a machine learning technique to discriminate between these diseases and other ordinary citrus conditions that may be present at citrus orchards, such as citrus scab and zinc deficiency. Our classification results are highly accurate when discriminating citrus canker from citrus scab (97.8%), and HLB from zinc deficiency (95%). These results show that it is possible to accurately identify citrus diseases that present similar symptoms.
authors.forename: Caio Bruno
authors.initials: CB
authors.lastname: Wetterich
authors.forename: Ruan
authors.initials: R
authors.lastname: Felipe de Oliveira Neves
authors.forename: José
authors.initials: J
authors.lastname: Belasque
authors.forename: Luis Gustavo
authors.initials: LG
authors.lastname: Marcassa
mesh_terms: D002957:Citrus / Q000382:microbiology*; D061848:Optical Imaging / Q000379:methods*; D010935:Plant Diseases / Q000382:microbiology*; D013050:Spectrometry, Fluorescence / Q000379:methods*; D060388:Support Vector Machine; D015032:Zinc / Q000172:deficiency
chemical_list: D015032:Zinc