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Fusarium Wilt of Banana Latency and Onset Detection Based on Visible/Near Infrared Spectral Technology

文献类型： 外文期刊

作者： Li, Cuiling 1 ; Xiang, Dandan 3 ; Yang, Shuo 1 ; Wang, Xiu 2 ; Li, Chunyu 3 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Beijing 100097, Peoples R China

2.Beijing Acad Agr & Forestry Sci, Intelligent Equipment Res Ctr, Beijing 100097, Peoples R China

3.Guangdong Acad Agr Sci, Inst Fruit Tree Res, Guangzhou 510640, Peoples R China

4.Natl Engn Res Ctr Intelligent Equipment Agr, Beijing 100097, Peoples R China

关键词： banana Fusarium wilt; visible/near-infrared spectroscopy; disease grading; 1D-CNN

期刊名称：AGRONOMY-BASEL （ 影响因子：3.4； 五年影响因子：3.8 ）

ISSN：

年卷期： 2024 年 14 卷 12 期

页码：

收录情况： SCI

摘要： Fusarium wilt of banana is a soil-borne vascular disease caused by Fusarium oxysporum f. sp. cubense. The rapid and accurate detection of this disease is of great significance to controlling its spread. The research objective was to explore rapid banana Fusarium wilt latency and onset detection methods and establish a disease severity grading model. Visible/near-infrared spectroscopy analysis combined with machine learning methods were used for the rapid in vivo detection of banana Fusarium wilt. A portable visible/near-infrared spectrum acquisition system was constructed to collect the spectra data of banana Fusarium wilt leaves representing five different disease grades, totaling 106 leaf samples which were randomly divided into a training set with 80 samples and a test set with 26 samples. Different data preprocessing methods were utilized, and Fisher discriminant analysis (FDA), an extreme learning machine (ELM), and a one-dimensional convolutional neural network (1D-CNN) were used to establish the classification models of the disease grades. The classification accuracies of the FDA, ELM, and 1D-CNN models reached 0.891, 0.989, and 0.904, respectively. The results showed that the proposed visible/near infrared spectroscopy detection method could realize the detection of the incubation period of banana Fusarium wilt and the classification of the disease severity and could be a favorable tool for the field diagnosis of banana Fusarium wilt.

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