Literature DB >> 26835778

Detection of citrus canker and Huanglongbing using fluorescence imaging spectroscopy and support vector machine technique.

Caio Bruno Wetterich, Ruan Felipe de Oliveira Neves, José Belasque, Luis Gustavo Marcassa.   

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.

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Year:  2016        PMID: 26835778     DOI: 10.1364/AO.55.000400

Source DB:  PubMed          Journal:  Appl Opt        ISSN: 1559-128X            Impact factor:   1.980


  3 in total

1.  DS-MENet for the classification of citrus disease.

Authors:  Xuyao Liu; Yaowen Hu; Guoxiong Zhou; Weiwei Cai; Mingfang He; Jialei Zhan; Yahui Hu; Liujun Li
Journal:  Front Plant Sci       Date:  2022-07-22       Impact factor: 6.627

2.  Chlorophyll Fluorescence Imaging Uncovers Photosynthetic Fingerprint of Citrus Huanglongbing.

Authors:  Haiyan Cen; Haiyong Weng; Jieni Yao; Mubin He; Jingwen Lv; Shijia Hua; Hongye Li; Yong He
Journal:  Front Plant Sci       Date:  2017-08-29       Impact factor: 5.753

3.  A Method of High Throughput Monitoring Crop Physiology Using Chlorophyll Fluorescence and Multispectral Imaging.

Authors:  Heng Wang; Xiangjie Qian; Lan Zhang; Sailong Xu; Haifeng Li; Xiaojian Xia; Liankui Dai; Liang Xu; Jingquan Yu; Xu Liu
Journal:  Front Plant Sci       Date:  2018-03-28       Impact factor: 5.753

  3 in total

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