Uso de imagens de fluorescência para monitoramento da evolução do cancro cítrico

Wetterich, Caio Bruno

doi:10.11606/d.76.2012.tde-23032012-151751

Cited by 1 publication

(1 citation statement)

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“…The latter is associated chlorophyll a and b. Fluorescence imaging spectroscopy (FIS) is the imaging of these fluorescence signals and has been investigated as a tool in the study of plants [10,[20][21][22][23]. It is a nondestructive and non-invasive technique for plant physiology [12], besides it is technologically and economically viable to be applied in orchard conditions, when compared to other techniques [24]. More details about the fluorescence technique as well as its advantages and limitations can be found in this review article [18].…”

Section: Introductionmentioning

confidence: 99%

Multiclass classifier based on deep learning for detection of citrus disease using fluorescence imaging spectroscopy

Neves¹,

Wetterich²,

Sousa³

et al. 2023

Laser Phys.

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In this work, we have combined fluorescence imaging spectroscopy (FIS) and supervised learning methods to identify and discriminate between citrus canker, Huanglongbing, and other leaf symptoms. Our goal is to differentiate these diseases and nutrient conditions without prior eye assessment of symptoms. Five supervised learning methods were evaluated. Our results show that by combining FIS with a convolutional neural network (AlexNet), it is possible to identify the disease of a sample with up to 95% accuracy. An enormous gain of time and a substantial cost reduction were achieved by this approach compared to polymerase chain reaction-based methods.

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