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利用高光谱图像早期检测香蕉叶上的黑叶斑病。

Early detection of black Sigatoka in banana leaves using hyperspectral images.

作者信息

Ugarte Fajardo Jorge, Bayona Andrade Oswaldo, Criollo Bonilla Ronald, Cevallos-Cevallos Juan, Mariduena-Zavala María, Ochoa Donoso Daniel, Vicente Villardón José Luis

机构信息

Facultad de Ciencias Naturales y Matemáticas (FCNM) Escuela Superior Politécnica del Litoral (ESPOL) Guayaquil Ecuador.

Facultad de Ingeniería Eléctrica y Computación (FIEC) Escuela Superior Politécnica del Litoral (ESPOL) Guayaquil Ecuador.

出版信息

Appl Plant Sci. 2020 Aug 28;8(8):e11383. doi: 10.1002/aps3.11383. eCollection 2020 Aug.

DOI:10.1002/aps3.11383
PMID:32995103
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7507400/
Abstract

PREMISE

Black Sigatoka is one of the most severe banana ( spp.) diseases worldwide, but no methods for the rapid early detection of this disease have been reported. This paper assesses the use of hyperspectral images for the development of a partial-least-squares penalized-logistic-regression (PLS-PLR) model and a hyperspectral biplot (HS biplot) as a visual tool for detecting the early stages of black Sigatoka disease.

METHODS

Young (three-month-old) banana plants were inoculated with a conidia suspension of the black Sigatoka fungus (). Selected infected and control plants were evaluated using a hyperspectral imaging system at wavelengths in the range of 386-1019 nm. PLS-PLR models were run on the hyperspectral data set. The prediction power was assessed using leave-one-out cross-validation as well as external validation.

RESULTS

The PLS-PLR model was able to predict the presence of the disease with a 98% accuracy. The wavelengths with the highest contribution to the classification ranged from 577 to 651 nm and from 700 to 1019 nm.

DISCUSSION

PLS-PLR and HS biplot effectively estimated the presence of black Sigatoka disease at the early stages and can be used to graphically represent the relationship between groups of leaves and both visible and near-infrared wavelengths.

摘要

前提

黑叶斑病是全球最严重的香蕉病害之一,但尚未有关于该病害快速早期检测方法的报道。本文评估了利用高光谱图像建立偏最小二乘惩罚逻辑回归(PLS-PLR)模型以及高光谱双标图(HS双标图)作为检测黑叶斑病早期阶段的可视化工具的应用。

方法

用黑叶斑病菌的分生孢子悬浮液接种三个月大的幼嫩香蕉植株。使用高光谱成像系统在386 - 1019nm波长范围内对选定的受感染植株和对照植株进行评估。在高光谱数据集上运行PLS-PLR模型。使用留一法交叉验证以及外部验证来评估预测能力。

结果

PLS-PLR模型能够以98%的准确率预测病害的存在。对分类贡献最大的波长范围为577至651nm以及700至1019nm。

讨论

PLS-PLR和HS双标图有效地估计了黑叶斑病早期阶段的存在,并且可用于以图形方式表示叶片组与可见光和近红外波长之间的关系。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/380c/7507400/e1938d0b63a6/APS3-8-e11383-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/380c/7507400/dd901a0a3ac6/APS3-8-e11383-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/380c/7507400/b85dfb81ecef/APS3-8-e11383-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/380c/7507400/89ba17b4d695/APS3-8-e11383-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/380c/7507400/31a18b366f6f/APS3-8-e11383-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/380c/7507400/e1938d0b63a6/APS3-8-e11383-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/380c/7507400/dd901a0a3ac6/APS3-8-e11383-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/380c/7507400/b85dfb81ecef/APS3-8-e11383-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/380c/7507400/89ba17b4d695/APS3-8-e11383-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/380c/7507400/31a18b366f6f/APS3-8-e11383-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/380c/7507400/e1938d0b63a6/APS3-8-e11383-g005.jpg

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