Abstract:
Haricot bean is a very important legume growing worldwide with higher market demands. It is a
very important foreign exchange earning crop in Ethiopia. It has been observed that the markets
of this crop are gradually increasing. In Ethiopia, the defect inspections are performed by experts
manually. It is labour-intensive, time-consuming, and suffers from the problem of inconsistency
and inaccuracy. In this study, we develop a model to inspect defect detection of haricot beans
using computer vision and machine learning approaches. The required images of haricot beans
were captured from Bure Ethiopian Commodity exchange (ECX) centre in the Amhara region of
Ethiopia. 1000 for each class defect and non-defect haricot bean were taken. The images were
taken directly using smart phone by placing on the white paper. After image acquisition pre-processing had been used to get an enhanced image. For feature extraction the grey level co-occurrence matrix (GLCM) and the convolutional neural network (CNN) method had been
considered. Besides, for classification three classifiers random forest (RF) , support vector
machine (SVM) and end to end CNN were applied to classify to their predefined class. For
developing a prototype and conducting experiment, Python programming language was used in
this study.
In this study, three groups of experiment have been conducted (CNN features with SVM, RF;
GLCM features with SVM, RF; finally and end to end CNN). From the experiment, the result
revealed that the CNN method for feature extraction achieved an accuracy of 94% and 97%
using SVM and RF classifiers, respectively. Further, using GLCM textural features methods
were showed an accuracy of 88% and 97% for SVM, and RF classifiers, respectively. When
using CNN as a classifier, an accuracy of 99% was achieved. It was concluded that, in all applied
approaches, the model can identify defects and non-defect haricot bean with the highest
accuracy. It is recommended that the developed approach should be implemented to other types
of haricot beans, such as white beans and speckled beans.
Key words: GLCM, CNN, SVM, RF, haricot bean.