Identification of maize (Zea mays L.) progeny genotypes based on two probabilistic approaches: Logistic regression and naïve Bayes

Seka, D.; Bonny, B.S.; Yoboue, Ahou Nicole; Sié, S.R.; Adopo-Gourène, B.A.

doi:10.1016/j.aiia.2019.03.001

Cited by 13 publications

(7 citation statements)

References 19 publications

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“…The seeds were planted in the Spring 2017, in a completely randomized factorial experiment at the experimental station of University Nangui Abrogoua, Abidjan, Côte d'Ivoire. Planting density and agricultural practices were done as described previously (Seka et al, 2019). Plots were tagged with identification number that identified each parent and its progeny.…”

Section: Methodsmentioning

confidence: 99%

Inheritance of maize (Zea mays L.) leaf traits

Seka¹,

Bonny²,

Adjoumani³

et al. 2019

Int. J. Genet. Mol. Biol.

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Maize leaf plays an important role in the synthesis of organic compounds. It hosts the interception of light energy and conversion to organic energy, and the capture and assimilation of carbon dioxide in order to produce food. In this study, we explore the heritability of the maize leaf length, leaf width, and the number of leaves of two maize genotypes, JNE and BLC. The estimates of heritability varied from 0.2 to 0.44. Only estimates for leaf width and leaf length are significant. The coefficients of genotypic correlation were between 0.45 and 0.66, and like the estimates of heritability, only the genotypic correlations between parents and offspring are significant for leaf length and leaf width of the two genotypes. The estimates of degree of genetic determination also known as heritability in the broad sense are all high. They range between 0.52 and 0.85. We determined a significance criterion for the degree of genetic determination, the critical value at the level of significance  = 0.05. We evaluated three methods for finding the critical value. With two methods based on the algebraic transformation of a function of the Snedecor's F and a simulation based on that function of F, it can be concluded that the estimates of the degree of genetic determination of leaf width of both the JNE and BLC genotypes are significant. With the third method that is based on fitting a Beta distribution, the critical value helped to conclude that none of the estimates of the degree of genetic determination is significant. The Pearson coefficients of correlation between plant height and all three leaf traits showed significant correlation between them, except leaf length and number of leaves.

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Section: Methodsmentioning

confidence: 99%

Inheritance of maize (Zea mays L.) leaf traits

Seka¹,

Bonny²,

Adjoumani³

et al. 2019

Int. J. Genet. Mol. Biol.

Self Cite

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“…Gonzalez et al (2019) developed R-CNN model for the quantification of blueberries via several CNN architectures with the best classification rate of 0.726. Another successful application of an AI application was for the identification of progeny genotypes in maize as reported by Seka et al (2019). The maize samples were tested using Gaussian Naïve Bayes and Logistic Regression with an overall prediction accuracy ranging from 78 to 87%.…”

Section: Applications Of Ai In Quality Determination Of Food and Agricultural Productsmentioning

confidence: 99%

Quality Inspection of Food and Agricultural Products using Artificial Intelligence

Ali

Hashim²,

Aziz³

et al. 2021

Adv Agric Food Res J

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A rising awareness for quality inspection of food and agricultural products has generated a growing effort to develop rapid and non-destructive techniques. Quality detection of food and agricultural products has prime importance in various stages of processing due to the laborious processes and the inability of the system to measure the whole of the food production. The detection of food quality has previously depended on various destructive techniques that require sample destruction and a large amount of postharvest losses. Artificial Intelligence (AI) has emerged with big data technologies and high-performance computation to create new opportunities in the multidisciplinary agri-food domain. This review presents the key concepts of AI comprising an expert system, artificial neural network (ANN), and fuzzy logic. A special focus is laid on the strength of AI applications in determining food quality for producing high and optimum yields. It was demonstrated that ANN provides the best result for modelling and effective in real-time monitoring techniques. The future use of AI for assessing quality inspection is promising which could lead to a real-time as well as rapid evaluation of various food and agricultural products.

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“…Variabels in this research consisted of educational background (ED), employment (EM) as independent variables and digital mobile scanner usage (DMS) as dependent variables [15]. About 340 respondents accross several big cities in Indonesia from various backgrounds participated in this research but reduced to 310 due to lack of completed informations [16]. The employment status that has been collected came from numerous background so we divided it into only 5 categories, i.e: unemployment, student, employee, self employeed, and entrepreneur.…”

Section: Samplingmentioning

confidence: 99%

Mobile Scanner Adoption Analysis Between Employment and Educational Background – An Analysis of Logistic Regression

2021

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As of today, the mobile apps may be downloaded everywhere. The development of mobile apps depends on the type of the work. An increasing use of mobile app is scanner apps due to an easy use. This paper presents the regression analysis on employment and educational background of the mobile scanner app because this research used category in the questionnaire. The use of logistic regression is to prove that any different comparisons are detected between employment and educational background so that the use of mobile scanner can be optimally used. The results show that educational background and employment have vital roles for mobile scanner adoption. This study also proves that previous researches on mobile scanner adoption were true for UTAUT model and comparison analysis.

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Identification of maize (Zea mays L.) progeny genotypes based on two probabilistic approaches: Logistic regression and naïve Bayes

Cited by 13 publications

References 19 publications

Inheritance of maize (Zea mays L.) leaf traits

Inheritance of maize (Zea mays L.) leaf traits

Quality Inspection of Food and Agricultural Products using Artificial Intelligence

Mobile Scanner Adoption Analysis Between Employment and Educational Background – An Analysis of Logistic Regression

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