Hyperspectral Leaf Image-Based Cucumber Disease Recognition Using the Extended Collaborative Representation Model

Collaborative representation (CR)-based classification has been successfully applied to plant disease recognition in cases with sufficient training samples of each disease. However, collecting enough training samples is usually time consuming and labor-intensive. Moreover, influenced by the non-idea...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2020-07, Vol.20 (14), p.4045
Main Authors: Li, Yuhua, Luo, Zhihui, Wang, Fengjie, Wang, Yingxu
Format: Article
Language:English
Subjects:
Agriculture
Artificial intelligence
Classification
Collaboration
cucumber disease recognition
Cucumbers
Cucumis sativus
Diagnosis
Diagnostic systems
Discriminant analysis
Experiments
extended collaborative representation (ECR)
Feature selection
hyperspectral imaging
Infections
Machine learning
Medical diagnosis
Methods
Neural networks
Physiology
Plant Diseases
Plant Leaves
Representations
Spectra
spectral library
Support vector machines
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Summary:Collaborative representation (CR)-based classification has been successfully applied to plant disease recognition in cases with sufficient training samples of each disease. However, collecting enough training samples is usually time consuming and labor-intensive. Moreover, influenced by the non-ideal measurement environment, samples may be corrupted by variables introduced by bad illumination and occlusions of adjacent leaves. Consequently, an extended collaborative representation (ECR)-based classification model is presented in this paper. Then, it is applied to cucumber leaf disease recognition, which constructs a pure spectral library consisting of several representative samples for each disease and designs a universal variation spectral library that deals with linear variables superimposed on samples. Thus, each query sample is encoded as a linear combination of atoms from these two spectral libraries and disease identity is determined by the disease of minimal reconstruction residuals. Experiments are conducted on spectral curves extracted from normal leaves and the disease lesions of leaves infected with cucumber anthracnose and brown spot. The diagnostic accuracy is higher than 94.7% and the average online diagnosis time is short, about 1 to 1.3 ms. The results indicate that the ECR-based classification model is feasible in the fast and accurate diagnosis of cucumber leaf diseases.
ISSN:1424-8220
1424-8220
DOI:10.3390/s20144045