Innovative Models Built Based on Image Textures Using Traditional Machine Learning Algorithms for Distinguishing Different Varieties of Moroccan Date Palm Fruit (Phoenix dactylifera L.)

نویسندگان

چکیده

The aim of this study was to develop the procedure for varietal discrimination date palm fruit using image analysis and traditional machine learning techniques. images ‘Mejhoul’, ‘Boufeggous’, ‘Aziza’, ‘Assiane’, ‘Bousthammi’ varieties, converted individual color channels, were processed extract texture parameters. After performing attribute selection, textures used build models intended different varieties algorithms from Functions, Bayes, Lazy, Meta, Trees groups. Models developed combining selected a set all channels sets spaces channels. models, including combined distinguished five with an average accuracy reaching 98%, ‘Mejhoul’ completely correctly discriminated SMO (Functions) IBk (Lazy) algorithms. By reducing number correctness classification increased. three most ‘Bousthammi’, revealed 100% each algorithm (SMO, Naive Bayes (Bayes), IBk, LogitBoost (Meta), LMT (Trees)). In case accuracies lower, 97.3% space RGB 99.63% fruits. results can be in practice vision systems sorting distinguishing authenticate variety further processing.

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ژورنال

عنوان ژورنال: Agriculture

سال: 2022

ISSN: ['2077-0472']

DOI: https://doi.org/10.3390/agriculture13010026