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利用基于光谱的传感器在不同阶段检测多种番茄叶病(晚疫病、靶斑病和细菌性斑点病)。

Detection of multi-tomato leaf diseases (late blight, target and bacterial spots) in different stages by using a spectral-based sensor.

机构信息

School of Mechanical Engineering, Xihua University, 999 Jinzhou Road, Chengdu, Sichuan, 610000, China.

Mechanical Engineering Department, University of California-Merced, 5200 N. Lake Road, Merced, CA, 95343, U.S.A.

出版信息

Sci Rep. 2018 Feb 12;8(1):2793. doi: 10.1038/s41598-018-21191-6.

Abstract

Several diseases have threatened tomato production in Florida, resulting in large losses, especially in fresh markets. In this study, a high-resolution portable spectral sensor was used to investigate the feasibility of detecting multi-diseased tomato leaves in different stages, including early or asymptomatic stages. One healthy leaf and three diseased tomato leaves (late blight, target and bacterial spots) were defined into four stages (healthy, asymptomatic, early stage and late stage) and collected from a field. Fifty-seven spectral vegetation indices (SVIs) were calculated in accordance with methods published in previous studies and established in this study. Principal component analysis was conducted to evaluate SVIs. Results revealed six principal components (PCs) whose eigenvalues were greater than 1. SVIs with weight coefficients ranking from 1 to 30 in each selected PC were applied to a K-nearest neighbour for classification. Amongst the examined leaves, the healthy ones had the highest accuracy (100%) and the lowest error rate (0) because of their uniform tissues. Late stage leaves could be distinguished more easily than the two other disease categories caused by similar symptoms on the multi-diseased leaves. Further work may incorporate the proposed technique into an image system that can be operated to monitor multi-diseased tomato plants in fields.

摘要

几种疾病已经威胁到佛罗里达州的番茄生产,造成了巨大的损失,尤其是在新鲜市场。在这项研究中,使用高分辨率便携式光谱传感器来研究在不同阶段(包括早期或无症状阶段)检测多病害番茄叶片的可行性。从田间采集了一片健康叶片和三片患病番茄叶片(晚疫病、靶斑病和细菌性斑点病),将它们定义为四个阶段(健康、无症状、早期和晚期)。根据先前研究中发表的方法和本研究中建立的方法,计算了 57 个光谱植被指数(SVIs)。进行主成分分析以评估 SVIs。结果显示出六个特征值大于 1 的主成分。在每个选定的 PC 中,具有权重系数排名在 1 到 30 之间的 SVIs 被应用于 K-最近邻分类。在所检查的叶片中,健康叶片的准确率最高(100%),错误率最低(0),因为它们的组织均匀。晚期叶片比其他两种由相似症状引起的疾病类别更容易区分,因为它们在多病害叶片上的症状相似。进一步的工作可能会将所提出的技术纳入图像系统中,以便在田间监测多病害的番茄植株。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0975/5809472/c006922c48a4/41598_2018_21191_Fig1_HTML.jpg

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