Ability of near infrared spectroscopy to detect anthracnose disease early in mango after harvest-Reference-Cited by-同舟云学术

Ability of near infrared spectroscopy to detect anthracnose disease early in mango after harvest

Published:2024-01-18 Issue:4 Volume:65 Page:581-591
ISSN:2211-3452
Container-title:Horticulture, Environment, and Biotechnology
language:en
Short-container-title:Hortic. Environ. Biotechnol.

Author:

Seehanam Pimjai,Sonthiya Katthareeya,Maniwara Phonkrit^ORCID,Theanjumpol Parichat,Ruangwong Onuma,Nakano Kazuhiro,Ohashi Shintaroh,Kramchote Somsak^ORCID,Suwor Patcharaporn

Funder

School of Agricultural Technology, KMITL

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s13580-023-00590-3.pdf

Reference39 articles.

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2. Anagnostis A, Asiminari G, Papageorgiou E, Bochtis D (2020) A convolutional neural networks based method for anthracnose infected walnut tree leaves identification. Appl Sci 10:469. https://doi.org/10.3390/app10020469

3. Arauz LF (2000) Mango anthracnose: economic impact and current options for Integrated Management. APS Plublications, Minnesota. https://doi.org/10.1094/PDIS.2000.84.6.600

4. Ardila CEC, Ramirez LA, Ortiz FAP (2020) Spectral analysis for the early detection of anthracnose in fruits of Sugar Mango (Mangifera indica). Comput Electron Agric 173:105357. https://doi.org/10.1016/j.compag.2020.105357

5. Barbosa-Gámez I, Caballero-Montoya KP, Ledesma N, Sayago-Ayerdi SG, García-Magaña ML, von Wettberg B, E. J., Montalvo-González E (2017) Changes in the nutritional quality of five Mangifera species harvested in two-maturity stages. J Sci Food Agric 17:4987–4994. https://doi.org/10.1002/jsfa.8377