Presence or Absence of Fusarium oxysporum f. sp. cubense Tropical Race 4 (TR4) Classification Using Machine Learning Methods on Soil Properties

Soil health is an integral part in agriculture. In order to have a good production, plant pathogens and diseases should remain low in soil. However, Panama disease have been a threat in the production of Cavendish bananas in recent years. Fusarium oxysporum f. sp. cubense Tropical Race 4 (TR4) produ...

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Main Authors:	David, Apollo Ian C, Guico, Maria Leonora
Format:	text
Published:	Archīum Ateneo 2019
Subjects:	Soil Training Agriculture Computational modeling Machine learning Pathogens Electrical and Computer Engineering Other Engineering
Online Access:	https://archium.ateneo.edu/ecce-faculty-pubs/44 https://ieeexplore.ieee.org/document/8650116
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Institution:	Ateneo De Manila University

id	ph-ateneo-arc.ecce-faculty-pubs-1043
record_format	eprints
spelling	ph-ateneo-arc.ecce-faculty-pubs-10432022-01-27T03:16:57Z Presence or Absence of Fusarium oxysporum f. sp. cubense Tropical Race 4 (TR4) Classification Using Machine Learning Methods on Soil Properties David, Apollo Ian C Guico, Maria Leonora Soil health is an integral part in agriculture. In order to have a good production, plant pathogens and diseases should remain low in soil. However, Panama disease have been a threat in the production of Cavendish bananas in recent years. Fusarium oxysporum f. sp. cubense Tropical Race 4 (TR4) produces chlamydospores in soil which germinates and infect the banana plant and eventually kill it. This study aims to develop a model using pattern recognition in soil parameters that will identify the predisposition of soil to existence of Panama disease in an area. Soil parameters have been selected as input since these indirectly affect the potential for biological suppression of plant pathogens. 2019-02-25T08:00:00Z text https://archium.ateneo.edu/ecce-faculty-pubs/44 https://ieeexplore.ieee.org/document/8650116 Electronics, Computer, and Communications Engineering Faculty Publications Archīum Ateneo Soil Training Agriculture Computational modeling Machine learning Pathogens Electrical and Computer Engineering Other Engineering
institution	Ateneo De Manila University
building	Ateneo De Manila University Library
continent	Asia
country	Philippines Philippines
content_provider	Ateneo De Manila University Library
collection	archium.Ateneo Institutional Repository
topic	Soil Training Agriculture Computational modeling Machine learning Pathogens Electrical and Computer Engineering Other Engineering
spellingShingle	Soil Training Agriculture Computational modeling Machine learning Pathogens Electrical and Computer Engineering Other Engineering David, Apollo Ian C Guico, Maria Leonora Presence or Absence of Fusarium oxysporum f. sp. cubense Tropical Race 4 (TR4) Classification Using Machine Learning Methods on Soil Properties
description	Soil health is an integral part in agriculture. In order to have a good production, plant pathogens and diseases should remain low in soil. However, Panama disease have been a threat in the production of Cavendish bananas in recent years. Fusarium oxysporum f. sp. cubense Tropical Race 4 (TR4) produces chlamydospores in soil which germinates and infect the banana plant and eventually kill it. This study aims to develop a model using pattern recognition in soil parameters that will identify the predisposition of soil to existence of Panama disease in an area. Soil parameters have been selected as input since these indirectly affect the potential for biological suppression of plant pathogens.
format	text
author	David, Apollo Ian C Guico, Maria Leonora
author_facet	David, Apollo Ian C Guico, Maria Leonora
author_sort	David, Apollo Ian C
title	Presence or Absence of Fusarium oxysporum f. sp. cubense Tropical Race 4 (TR4) Classification Using Machine Learning Methods on Soil Properties
title_short	Presence or Absence of Fusarium oxysporum f. sp. cubense Tropical Race 4 (TR4) Classification Using Machine Learning Methods on Soil Properties
title_full	Presence or Absence of Fusarium oxysporum f. sp. cubense Tropical Race 4 (TR4) Classification Using Machine Learning Methods on Soil Properties
title_fullStr	Presence or Absence of Fusarium oxysporum f. sp. cubense Tropical Race 4 (TR4) Classification Using Machine Learning Methods on Soil Properties
title_full_unstemmed	Presence or Absence of Fusarium oxysporum f. sp. cubense Tropical Race 4 (TR4) Classification Using Machine Learning Methods on Soil Properties
title_sort	presence or absence of fusarium oxysporum f. sp. cubense tropical race 4 (tr4) classification using machine learning methods on soil properties
publisher	Archīum Ateneo
publishDate	2019
url	https://archium.ateneo.edu/ecce-faculty-pubs/44 https://ieeexplore.ieee.org/document/8650116
_version_	1724079127541055488

Presence or Absence of Fusarium oxysporum f. sp. cubense Tropical Race 4 (TR4) Classification Using Machine Learning Methods on Soil Properties

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