Wang Y, Zhang X, Taha MF, Chen T, Yang N, Zhang J, Mao H. Detection Method of Fungal Spores Based on Fingerprint Characteristics of Diffraction-Polarization Images.
J Fungi (Basel) 2023;
9:1131. [PMID:
38132732 PMCID:
PMC10744520 DOI:
10.3390/jof9121131]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 11/17/2023] [Accepted: 11/21/2023] [Indexed: 12/23/2023] Open
Abstract
The most significant aspect of promoting greenhouse productivity is the timely monitoring of disease spores and applying proactive control measures. This paper introduces a method to classify spores of airborne disease in greenhouse crops by using fingerprint characteristics of diffraction-polarized images and machine learning. Initially, a diffraction-polarization imaging system was established, and the diffraction fingerprint images of disease spores were taken in polarization directions of 0°, 45°, 90° and 135°. Subsequently, the diffraction-polarization images were processed, wherein the fingerprint features of the spore diffraction-polarization images were extracted. Finally, a support vector machine (SVM) classification algorithm was used to classify the disease spores. The study's results indicate that the diffraction-polarization imaging system can capture images of disease spores. Different spores all have their own unique diffraction-polarization fingerprint characteristics. The identification rates of tomato gray mold spores, cucumber downy mold spores and cucumber powdery mildew spores were 96.02%, 94.94% and 96.57%, respectively. The average identification rate of spores was 95.85%. This study can provide a research basis for the identification and classification of disease spores.
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