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

机译:荧光成像光谱和支持向量机技术检测柑橘溃疡病和黄龙病

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摘要

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. (C) 2016 Optical Society of America
机译:柑橘溃疡病和黄龙病(HLB)是柑橘类疾病,对全世界的柑橘生产构成严重威胁,并可能造成巨大的经济损失。在这项工作中,我们结合了荧光成像光谱(FIS)和机器学习技术来区分这些疾病与柑橘园中可能存在的其他普通柑橘病,例如柑橘结ab和锌缺乏症。当区分柑桔溃疡病和柑桔sc病(97.8%),HLB和锌缺乏症(95%)时,我们的分类结果非常准确。这些结果表明,可以准确地识别出表现出类似症状的柑橘类疾病。 (C)2016美国眼镜学会

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