ASSESSING THE COCOA GENOTYPES FOR RESISTANCE TO BLACK POD USING THE AREA UNDER THE DISEASE-PROGRESS CURVE (AUDPC).

Autor: LING, ALBERT SH. CH.¹ albert@koko.gov.my, KAMIL, MOHD J. AHMAD², CHONG, KHIM PH.³, HO, CHONG M.³

Publikováno v: Bulgarian Journal of Agricultural Science. 2017, Vol. 23 Issue 6, p972-979. 8p.

Estimation of Genetic Parameters and Identification of Leaf Blast-Resistant Rice RILs Using Cluster Analysis and MGIDI.

Autor: Jalalifar, Reza¹ (AUTHOR), Sabouri, Atefeh¹ (AUTHOR) a.sabouri@guilan.ac.ir, Mousanejad, Sedigheh² (AUTHOR), Dadras, Ahmad Reza³ (AUTHOR)

Publikováno v: Agronomy. Nov2023, Vol. 13 Issue 11, p2730. 18p.

Effect of pathogen races on the area under the disease progress curve in barley infected with Puccinia hordei / Der Einfluß von Erregerrassen auf die Fläche unter der Befallskurve bei Gerste nach Infektion durch Puccinia hordei

Autor: Andres, M. W., Wilcoxson, R. D., Roelfs, A. P.

Publikováno v: Zeitschrift für Pflanzenkrankheiten und Pflanzenschutz / Journal of Plant Diseases and Protection, 1986 Dec 01. 93(6), 632-637.

Externí odkaz: https://www.jstor.org/stable/43383199

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Artificial neural network for prediction of the area under the disease progress curve of tomato late blight

Autor: Rafael Simões Tomaz, Derly José Henriques da Silva, Bruno Soares Laurindo, Renata Dias Freitas Laurindo, Fabyano Fonseca e Silva, Cosme Damião Cruz, Daniel Pedrosa Alves, Carlos Nick

Publikováno v: Scientia Agricola, Volume: 74, Issue: 1, Pages: 51-59, Published: FEB 2017
Scientia Agricola, Vol 74, Iss 1, Pp 51-59
Scientia Agricola v.74 n.1 2017
Scientia Agrícola
Universidade de São Paulo (USP)
instacron:USP

Artificial neural networks (ANN) are computational models inspired by the neural systems of living beings capable of learning from examples and using them to solve problems such as non-linear prediction, and pattern recognition, in addition to severa

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0c09f54359f47468c6b7516e5e5206f7
https://doi.org/10.1590/1678-992x-2015-0309