Rice performance prediction to deficit irrigation using microsatellite alleles and artificial intelligence

Ghasemi Bahareh and Sabouri Hossein and Moghaddam Hossein Hosseini and Biabani Abbas and Sheikhzadeh Mohamad Javad: Rice performance prediction to deficit irrigation using microsatellite alleles and artificial intelligence. In: Acta biologica Szegediensis, (66) 1. pp. 37-46. (2022)

[thumbnail of biologica_066_numb_001_037-046.pdf]
Cikk, tanulmány, mű

Download (1MB) | Preview


Rice germplasm investigated as completely randomized design under flooding and deficit irrigation conditions. The results of the association analysis indicated that RM29, RM63, and RM53 could be used for rice breeding programs to improve yields under deficit irrigation. The highest accuracy of rice performance prediction was 98.36 for the RFA (RFA) for panicle length, flag leaf length, and width, and the number of primary branches, after that, the MLP algorithm had better prediction power than other algorithms. When a genotypes code was considered as a criterion to classify the genotypes under the drought stress at the reproductive stage, the random forest algorithm (RFA) was the best algorithm based on the predictive accuracy (67.93), kappa value (0.514) and root mean square error (0.293). Based on the artificial intelligence methods, the RFA presented the best results to predict the response of genotypes to deficit irrigation using the microsatellite molecular data.

Item Type: Article
Journal or Publication Title: Acta biologica Szegediensis
Date: 2022
Volume: 66
Number: 1
ISSN: 1588-4082
Page Range: pp. 37-46
Language: English
Publisher: University of Szeged
Place of Publication: Szeged
Related URLs: https://acta.bibl.u-szeged.hu/77814/
DOI: 10.14232/abs.2022.1.37-46
Uncontrolled Keywords: Rizstermesztés
Additional Information: Bibliogr.: p. 45-46. ; összefoglalás angol nyelven
Subjects: 01. Natural sciences
01. Natural sciences > 01.06. Biological sciences
Date Deposited: 2022. Dec. 14. 08:58
Last Modified: 2022. Dec. 14. 08:58
URI: http://acta.bibl.u-szeged.hu/id/eprint/77836

Actions (login required)

View Item View Item