FEATURE EXTRACTION AND CLASSIFICATION OF GREEN MUNG BEAN USING MACHINE LEARNING TECHNIQUES
Domain:
agricultureRecord type:
paper
Creator:
TEK
Editor:
Dr.
Publisher:
Nat
Host:
Development of a machine learning vision system aiming in the establishment of technological
and innovative approaches towards sample green mung bean raw quality value
classification by extracting the relevant green mung bean features is the focal issue of
this exploratory research. Of paramount significance in this regard is addressing the
identified problems of the tedious and inefficient manual grading and sorting mechanisms
of one of the important agricultural products in Ethiopia, green mung. Prevalent
sorting and classification approaches are characterized by subjective assessments of the
features and nature of this huge economy representing crop, thereby influencing quality
control and productivity aspects of the product. The major objective of the research
spans extraction and selection of the important green mung bean morphological and
color features that are useful for the purpose of classification of the raw quality grade
level of sample green mung beans by designing, analyzing and testing a digital image
processing model.
The automated raw quality value classification experimentation comprised the analysis
of images of green mung bean using major attributes of morphological structures (shape
and size), and color features. The sample of green mung bean providing a total of 65
samples, which yielded 386 sample images after a series of re-sampling measures of
same into 3 sub-samples. The overall image processing work to develop models and
depict trends for an efficient raw quality value classification involved sequential phases
of image acquisition, image enhancement and segmentation, feature extraction, attribute
selection, classification and performance evaluation.
The Logistic Regression, k-Nearest Neighbors (kNN), Support Vector Machine (SVM),
Naive Bayes and Random Forest (RF) were implemented for such classification purposes.
A combined morphological and color features aggregate function dataset was
used to develop the base model.
Discretization of the raw quality value in to three interval classes was done to improve
the performance of the model. 80% split evaluation technique was implemented for
the Logistic Regression, kNN, SVM, Naive Bayes and RF classifiers. In kNN classifier
yielded higher model performance (96.89% correctly classified), followed by RF
(93.58%).
and innovative approaches towards sample green mung bean raw quality value
classification by extracting the relevant green mung bean features is the focal issue of
this exploratory research. Of paramount significance in this regard is addressing the
identified problems of the tedious and inefficient manual grading and sorting mechanisms
of one of the important agricultural products in Ethiopia, green mung. Prevalent
sorting and classification approaches are characterized by subjective assessments of the
features and nature of this huge economy representing crop, thereby influencing quality
control and productivity aspects of the product. The major objective of the research
spans extraction and selection of the important green mung bean morphological and
color features that are useful for the purpose of classification of the raw quality grade
level of sample green mung beans by designing, analyzing and testing a digital image
processing model.
The automated raw quality value classification experimentation comprised the analysis
of images of green mung bean using major attributes of morphological structures (shape
and size), and color features. The sample of green mung bean providing a total of 65
samples, which yielded 386 sample images after a series of re-sampling measures of
same into 3 sub-samples. The overall image processing work to develop models and
depict trends for an efficient raw quality value classification involved sequential phases
of image acquisition, image enhancement and segmentation, feature extraction, attribute
selection, classification and performance evaluation.
The Logistic Regression, k-Nearest Neighbors (kNN), Support Vector Machine (SVM),
Naive Bayes and Random Forest (RF) were implemented for such classification purposes.
A combined morphological and color features aggregate function dataset was
used to develop the base model.
Discretization of the raw quality value in to three interval classes was done to improve
the performance of the model. 80% split evaluation technique was implemented for
the Logistic Regression, kNN, SVM, Naive Bayes and RF classifiers. In kNN classifier
yielded higher model performance (96.89% correctly classified), followed by RF
(93.58%).