For Publishers
DiscoveryMetadataPeer reviewHostingPublishing
For Researchers
JoinPublishReviewCollect
Register
Blog
About
Search
Advanced search
My ScienceOpen
Search
For Publishers
For Researchers
Blog
About
2
views
83
references
 Top references
cited by
0
    
0 reviews
 Review
0
comments
 Comment
0
recommends
+1 Recommend
0 collections
    0
    shares
      511
      similar
       All similar
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      A Vis/NIR spectra-based approach for identifying bananas infected with Colletotrichum musae

      research-article

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Introduction

          Anthracnose of banana caused by Colletotrichum species is one of the most serious post-harvest diseases, which can cause significant yield losses. Clarifying the infection mechanism of the fungi using non-destructive methods is crucial for timely discriminating infected bananas and taking preventive and control measures.

          Methods

          This study presented an approach for tracking growth and identifying different infection stages of the C. musae in bananas using Vis/NIR spectroscopy. A total of 330 banana reflectance spectra were collected over ten consecutive days after inoculation, with a sampling rate of 24 h. The four-class and five-class discriminant patterns were designed to examine the capability of NIR spectra in discriminating bananas infected at different levels (control, acceptable, moldy, and highly moldy), and different time at early stage (control and days 1-4). Three traditional feature extraction methods, i.e. PC loading coefficient (PCA), competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA), combining with two machine learning methods, i.e. partial least squares discriminant analysis (PLSDA) and support vector machine (SVM), were employed to build discriminant models. One-dimensional convolutional neural network (1D-CNN) without manually extracted feature parameters was also introduced for comparison.

          Results

          The PCA-SVM and·SPA-SVM models had good performance with identification accuracies of 93.98% and 91.57%, 94.47% and 89.47% in validation sets for the four- and five-class patterns, respectively. While the 1D-CNN models performed the best, achieving an accuracy of 95.18% and 97.37% for identifying infected bananas at different levels and time, respectively.

          Discussion

          These results indicate the feasibility of identifying banana fruit infected with C. musae using Vis/NIR spectra, and the resolution can be accurate to one day.

          Related collections

          Most cited references83

          • Record: found
          • Abstract: not found
          • Article: not found

          The successive projections algorithm for variable selection in spectroscopic multicomponent analysis

          Mário César Ugulino de Araújo, Teresa Saldanha, Roberto Kawakami Harrop Galvão … (2001)
          Article 0 comments Cited 198 times – based on 0 reviews     Review now
            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Improving quality inspection of food products by computer vision––a review

            Tadhg Brosnan, Da-Wen Sun (2004)
            Article 0 comments Cited 100 times – based on 0 reviews     Review now
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Plant infection and the establishment of fungal biotrophy.

              Kurt Mendgen, Matthias Hahn (2002)
              To exploit plants as living substrates, biotrophic fungi have evolved remarkable variations of their tubular cells, the hyphae. They form infection structures such as appressoria, penetration hyphae and infection hyphae to invade the plant with minimal damage to host cells. To establish compatibility with the host, controlled secretory activity and distinct interface layers appear to be essential. Colletotrichum species switch from initial biotrophic to necrotrophic growth and are amenable to mutant analysis and molecular studies. Obligate biotrophic rust fungi can form the most specialized hypha: the haustorium. Gene expression and immunocytological studies with rust fungi support the idea that the haustorium is a transfer apparatus for the long-term absorption of host nutrients.
              Article 0 comments Cited 85 times – based on 0 reviews     Review now
                Bookmark
                All references

                Author and article information

                Contributors
                Journal
                Journal ID (nlm-ta): Front Plant Sci
                Journal ID (iso-abbrev): Front Plant Sci
                Journal ID (publisher-id): Front. Plant Sci.
                Title: Frontiers in Plant Science
                Publisher: Frontiers Media S.A.
                ISSN (Electronic): 1664-462X
                Publication date (Electronic): 02 June 2023
                Publication date Collection: 2023
                Volume: 14
                Electronic Location Identifier: 1180203
                Affiliations
                [1] 1 College of Mechanical and Electrical Engineering, Zhongkai University of Agriculture and Engineering , Guangzhou, China
                [2] 2 College of Engineering, South China Agricultural University , Guangzhou, China
                [3] 3 College of Mechanical and Electronic Engineering, Nanjing Forestry University , Nanjing, China
                Author notes

                Edited by: Edvaldo Da Silva, São Paulo State University, Brazil

                Reviewed by: Zhiming Guo, Jiangsu University, China; Tiago Moraes, University of São Paulo, Brazil

                *Correspondence: Hongli Liu, liuhongli@ 123456zhku.edu.cn
                Article
                DOI: 10.3389/fpls.2023.1180203
                PMC ID: 10272841
                SO-VID: 937b7553-97fa-4d84-a532-22413cbdf481
                Copyright © Copyright © 2023 Chu, Zhang, Wei, Ma, Fu, Miao, Jiang and Liu
                License:

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                Date received : 05 March 2023
                Date accepted : 09 May 2023
                Page count
                Figures: 7, Tables: 5, Equations: 4, References: 83, Pages: 14, Words: 7158
                Funding
                Funded by: National Natural Science Foundation of China , doi 10.13039/501100001809;
                Award ID: 32102087
                Funded by: Natural Science Foundation of Guangdong Province , doi 10.13039/501100003453;
                Award ID: 2020A151501795
                This research was funded by the National Natural Science Foundation of China (grant No. 32102087); Natural Science Foundation of Guangdong Province (grant No. 2020A151501795); Guangzhou basic and applied basic research project, (grant No. SL2023A04J0125) and Guangdong Provincial Agricultural Science and Technology Innovation and Extension Project (grant No. 2023A04J1667).
                Categories
                Subject: Plant Science
                Subject: Original Research
                Custom metadata
                section-in-acceptance Technical Advances in Plant Science

                ScienceOpen disciplines: Plant science & Botany
                Keywords: vis/nir spectra,banana fruit, colletotrichum musae infection,fungi contamination detection,traditional classification methods,deep learning algorithms
                Data availability:
                ScienceOpen disciplines: Plant science & Botany
                Keywords: vis/nir spectra, banana fruit, colletotrichum musae infection, fungi contamination detection, traditional classification methods, deep learning algorithms

                Comments

                Comment on this article