Artificial Neural Network-Based Method to Identify Five Varieties of Egyptian Faba Bean According to Seed Morphological Features

Aboukarima, Abdulwahed M.; El-Marazky, Mohamed S.; Elsoury, Hussien A.; Zayed, Moamen; Minyawi, Mamdouh

doi:10.1590/1809-4430-eng.agric.v40n6p791-799/2020

Cited by 5 publications

(4 citation statements)

References 15 publications

(12 reference statements)

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“…Giza3, Giza461, Misr1, Nobarya1, and Sakha1 are the fve varieties of Egyptian faba-bean seeds studied by Abdulwahed et al [15]. Tis method uses morphological features and an ANN to grade and classify the quality of Egyptian faba-bean seeds.…”

Section: Related Workmentioning

confidence: 99%

Identification of Dry Bean Varieties Based on Multiple Attributes Using CatBoost Machine Learning Algorithm

Krishnan¹,

Aruna

Karthick³

et al. 2023

Scientific Programming

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Dry beans are the most widely grown edible legume crop worldwide, with high genetic diversity. Crop production is strongly influenced by seed quality. So, seed classification is important for both marketing and production because it helps build sustainable farming systems. The major contribution of this research is to develop a multiclass classification model using machine learning (ML) algorithms to classify the seven varieties of dry beans. The balanced dataset was created using the random undersampling method to avoid classification bias of ML algorithms towards the majority group caused by the unbalanced multiclass dataset. The dataset from the UCI ML repository is utilised for developing the multiclass classification model, and the dataset includes the features of seven distinct varieties of dried beans. To address the skewness of the dataset, a Box-Cox transformation (BCT) was performed on the dataset’s attributes. The 22 ML classification algorithms have been applied to the balanced and preprocessed dataset to identify the best ML algorithm. The ML algorithm results have been validated with a 10-fold cross-validation approach, and during validation, the CatBoost ML algorithm achieved the highest overall mean accuracy of 93.8 percent, with a range of 92.05 percent to 95.35 percent.

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Section: Related Workmentioning

confidence: 99%

Identification of Dry Bean Varieties Based on Multiple Attributes Using CatBoost Machine Learning Algorithm

Krishnan¹,

Aruna

Karthick³

et al. 2023

Scientific Programming

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“…On the other hand, a relatively low average volume characterized the "01_02" and "FAB 6434" accessions compared to the other accessions that recorded intermediate values. By using an artificial neural network-based method, Aboukarima et al [13] have shown that seeds volumes of Egyptian faba bean varieties ranged from 700 to 930 mm 3 . e weight of one hundred seeds is a very important parameter for the estimation of yield in Vicia faba.…”

Section: Physical Properties Of Seedsmentioning

confidence: 99%

“…Moreover, diversity study through the analysis of the physical properties of seeds is a preliminary step for faba bean germplasm characterization and improvement. Several attempts to analyze the morphological diversity and to characterize the physical properties of faba bean seeds have been realized, whether concerning varieties, accessions, cultivars, line, or even mutants [6][7][8][9][10][11][12][13]. However, no one paid great attention to the Tunisian germplasm that is rich in autochthonous accessions scattered among farmers.…”

Section: Introductionmentioning

confidence: 99%

Genetic Variability of Tunisian Faba Beans (Vicia faba L.) Based on Seeds’ Morphophysical Properties as Assessed by Statistical Analysis

Khamassi

Babay

Rouaissi

et al. 2021

Journal of Food Quality

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Faba bean (Vicia faba L.) is a legume crop cultivated for its nutritious seeds that are an important worldwide source of human food and feed. Seeds characterization is a prerequisite step for faba bean quality improvement. The morphophysical characterization of the seeds of twenty-four local faba bean accessions following the UPOV descriptors and the AOAC International standards was carried out and assessed with an approach based on Euclidean statistical model. “205 Bulk” was the unique accession harboring white hilum color seed which is linked to low convicine grain content. Irregular seed shape was the most observed character among the studied accessions except “Badii” displaying an elliptical seed shape; therefore, seed shape did not allow discrimination within our Tunisian germplasm. Interestingly, the physical characters of the seeds showed significant diversity between the accessions for all the measured parameters. A highly significant variability was observed for axial, length, and width dimensions of seeds, with “Memdouh” being the longest and largest seed accession, whereas “01-02” was the shortest and narrowest. Classification of the studied faba bean germplasm accessions based on morphophysical characters using clustering by Euclidean distance revealed three different groups. Moreover, multivariate PCA analysis further classified the faba bean accessions into four main clusters. Correlation study performed by using Spearman’s test established positive correlations within physical parameters of seeds such as between mean length and mean width of seeds. Therefore, using morphophysical parameters screening, valuable phenotypes have been selected for deeper physiological characterization and further breeding programs.

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“…This has contributed to a more accurate evaluation of certain seed physiological quality markers. These markers include: seed size, evaluation of seed surface color space by image analysis, computer-assisted spectrometry, X-ray inspection combined with quantitative imaging, and improved detection of chlorophyll fluorescence signal by laser technology, and the evaluation of seeds by thermographic images through infrared, among others (Dell'Aquila, 2009b;Arruda et al, 2016;Huang et al 2015;Brunes et al 2019;Monteiro et al, 2019;Xia et al, 2019;Aboukarima et al, 2020;Liu et al, 2020b;Medeiros, et al, 2020;Torres et al, 2020;Monteiro et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Prediction of Ranking of Lots of Corn Seeds by Artificial Intelligence

et al. 2022

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The seed sector faces several challenges when it comes to ensuring a quick and accurate decision making when working with large amounts of data on physiological quality of seed lots, which makes the process time-consuming and inefficient. Thus, artificial intelligence (AI) emerges as a new technological option in the seed sector to solve database problems in the post-harvest stages. This study aims to use machine learning to classify maize seed lots. Data were obtained from eight maize seed crops from a private company. These data were mined using the following classifiers: J48 (DecisionTree), RandomForest, CVR (ClassificationViaRegression), lBk (lazy.IBK), MLP (MultiLayerPercepton), and NäiveBayes. Cross-validation was used for data measurement, with the data set, including training and testing data, being divided into 10 subsets. The described steps were performed using the Weka software. It is concluded that results obtained allow the classification of maize seed lots with high accuracy and precision, and these algorithms can better classify the maize seed lot through vigor attributes, thus enabling more accurate decision making based on vigor tests on a reduced evaluation time.

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Artificial Neural Network-Based Method to Identify Five Varieties of Egyptian Faba Bean According to Seed Morphological Features

Cited by 5 publications

References 15 publications

Identification of Dry Bean Varieties Based on Multiple Attributes Using CatBoost Machine Learning Algorithm

Identification of Dry Bean Varieties Based on Multiple Attributes Using CatBoost Machine Learning Algorithm

Genetic Variability of Tunisian Faba Beans (Vicia faba L.) Based on Seeds’ Morphophysical Properties as Assessed by Statistical Analysis

Prediction of Ranking of Lots of Corn Seeds by Artificial Intelligence

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