Classification of maize genotype using logistic regression. Issue 1 (November 2021)

• Record Type: Journal Article
• Authors: Arief Subchan, M.; Andayani, N.N.
• Is Part Of: IOP conference series. Volume 911:Issue 1(2021)
• Publication Date: 2021-11
• Subjects: Earth sciences -- Periodicals; Environmental sciences -- Congresses; Environmental sciences -- Periodicals; 550.5
• DOI: 10.1088/1755-1315/911/1/012017
• Languages: English
• ISSNs: 1755-1307

Abstract

Abstract: Characterization of maize plants is among the pre-requested document prior to release to the public in Indonesia. Characterization of maize genotype involves various parameters includes agronomic parameters, yield and yield components. Characterization is generally carried out by professionals because it requires special skills in identifying genotypes based on their specific characters. The objective of the study was to classify the genotypes of corn plants based on the characters of the ear and kernel using a logistic regression model. The research was conducted at IP2TP Bajeng in 2020 by planting 4 genotypes, namely DYM-15, N 79, Mal 03 and G102612. A total of 100 plants per genotype were planted for cob characterization. Data analysis was done by using open-source software, Orange Software. The results indicated that the logistic regression model had a very good performance in classifying maize genotypes with an accuracy of > 98%. The values of the five parameters used to access the accuracy of the model are AUC=1.0, CA=0.99, F1=0.99, precision=0.99, recall=0.99. This value indicates that the use of IT-based tools can correctly classifying genotypes with high accuracy and consistency of results. Thus, digital based model can be integrated with manual selection for fast and precise grading of maize genotypes for maintaining seed quality.