The PowerPoint presentation outlines the research titled "Deep-Shallow Framework for automatic identification and classification of Ethiopian Coffee bean varieties using image processing." It discusses the use of hybrid feature mining to enhance accuracy and efficiency in identifying and classifying coffee bean varieties.

1. Deep-shallow Framework-based Hybrid
Feature Extractor For Ethiopian Coffee
Bean Varieties Classification using Image
Processing
By: Yirga Kegne Molla
Advisor: Abebe Alemu (Asst. prof.)
Department of
Computer Science

2. Table
of
Contents
Introduction
Motivation
Problem Statement
Objective
Scope and delimitation
Significance
Related work
Research methodologies
Result and discussion
Conclusion and recommendation
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o5
o6

3. Introduction
 Agriculture plays an important role in the sustainable
development of world’s economy [1].
The two species commonly grown globally are.
 Arabica and
Robusta coffee [3][4].
 Due to, their economic importance for coffee beverage
production.
 Nowadays, Arabica and Robusta coffee accounts (70%, 30%)
of global coffee production respectively [2].

4. Cont..
 Ethiopia is the birthplace of coffee Arabica [8].
 Ethiopia is Africa’s leading coffee producer and the fifth-largest in the world next
to Brazil, Vietnam, Colombia, Indonesia [11], [14]–[16].
 Currently, around 15% of the country's entire population, rely on coffee [12][13].
 Ethiopian coffee is characterized by a good flavor & quality[9].
 In Ethiopia variety of coffee with a variety of quality and grades is produced, But
the common ones are Sidama, Limu, Wellega, Jimma, Harar [15].
 Coffee produced have distinct size or flavor [18].

5. Cont..
 Coffees exported based on specific geographical origins, organoleptic characters.
ECX has mandated to assure the quality of the coffee from beans to cup before
export with the purpose to .
 Check coffee's origin,
 Countries reputation for coffee quality,
 Client’s interest, and
 Export standard
 However, classifying coffee based on their region manually is a worthful task.
 Therefore, to solve subject heterogeneity, we used a hybrid of shallow & deep
learning technique.

6. Motivation
Coffee bean classification and grading is done manually at ECX
 It costs a lot of money and
Degrades the company's reputation.
We need to improve the existing technique and enhance our foreign income.
 To balance consumption rate with production rate.
 The advancement of image analysis in detection of agricultural seeds.

7. Problem statement
In Ethiopia coffees are cultivated under different weather
and climatic conditions [28].
 So, CB’s have different ingredients and flavors with
distinct physical makeups[18].
Having this, coffee classified manually at ECX, with
teams that have experience with it [22][20].
 However, this process is highly biased, subjective, and
prone to error [21], [23], [29]. Anxiety of Manual work

8. Cont..
The size, shape, texture, color, and defects are the main quality criteria used [31].
 However, due to the natural similarity of the coffee, the human perception could easily
be biased [32].
Inconsistent nature of coffee shape, texture, and color, makes manual classification
highly challenging.
Image analysis technique was used by [21]–[23], [29], [30] using hand-crafted image
feature extractors.
The shape of a coffee bean has natural rotation variation, including edges and curves.
 So, extracting information by hand-crafted techniques are challenging [27].

9. Cont..
Exploring advanced techniques to determine the form similarity of beans is critical.
 So, we used HOG to detect the information contained in edges and curves to get
local features and deep learning to get high-level features.
 CNN is rotation invariant, on the other hand, HOG is rotation variant and can give
an edge direction to detect features [27].
 Therefore, we used HOG as an additive to the CNN to extract discriminative
features.
 The previous researches on coffee bean varieties use low-level image feature
extractors.

10. Cont..
However, low-level features are less successful at generalizing and
distinguishing significant properties in similar classes when used alone [27].
 Where as, DL requires a huge dataset to train and it is not computationally
feasible.
 Therefore, this work aims at a deep-shallow hybrid feature extractor for
accurate classification of ECB varieties.

11. Research question
RQ1. Which image preprocessing techniques are better for smoothing
and removing noise to enhance the classification of coffee bean varieties?
RQ2. To what extent does hybrid feature extraction improve coffee bean
variety classification?
RQ3. What is the suitable classification algorithm for constructing a
model for coffee bean varieties classification?
RQ4. To what extent the proposed model performs better in the
identification and classification of coffee bean varieties?

12. Objective
To collect coffee bean varieties, prepare, and organize our data.
To investigate and apply different preprocessing techniques.
To study and investigate (HOG) for hand-crafted features and
CNN for High-level features.
To develop a hybrid feature extraction algorithm (HOG-CNN)
To study and investigate different classification algorithms.
To build a model better than pre-trained state-of-the-art deep
learning models.
To evaluate the performance of our model using a test dataset

13. Scope and Significance
Scope
 Data collected at ECX
 Coffee data – 2020/21
 Washed & unwashed
coffee
 Raw coffee
 Physical property
 Deep-shallow
framework.
Delimitation
 Roasted in type
 No cup test (chemical
property)
 Limited regions
Significance
 Economic significance
 Social aspect
 Scientific contribution

14. Methods
Data collection
Classification
Tools
Feature Extraction
Data preprocess
Segmentation
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2
5
3
4

15. Related works
Title Author FE Classification Limitation
Sorting and Grading of
agricultural fruit products
[81] Morphology and
shape feature
descriptor
SVM  The model where not correctly
identify all varieties due to size
and shape similarities of fruits.
 Shape and size features
considered are poor in
discriminating similar classes
[76].
Using texture features for
fruit classification.
[63] GLCM, HOG,
LBP
DT  No noise removal technique was
used.
 Only shape and texture
information were used which has
limited generalization potential
when used alone.
Detecting Fruit
Information Using ML
Techniques
[74] CNN CNN  The model was computationally
intensive.
 CNN features were used
separately which has difficult for
small dataset problems.

16. Cont..
Title Author FE Classification Limitation
Automatic Ethiopian maize
quality Assessment
[25] Morphology,
Texture, and
color feature
descriptor.
ANN  Shadows and illuminations
influence the color feature value
extracted.
 Classic features used.
Classification of rice grain
varieties.
[82] GLCM texture
feature
descriptor
ANN  GLCM texture feature used which
have less recognizing power.
 The model was trained with a
small dataset that has low
discriminating power in similar
classes.
Malt-Barley Seed
Identification using image
processing
[24] Morphology,
Texture, and
color feature
descriptor.
Ensemble of
KNN and ANN
 Only considered seeds in the
ventral side which results in a
poor discrimination rate of malt-
barely.
 Color, morphology, and texture
features were used which are less
generalizing ability.

17. Cont..
Title Author FE Classification Limitation
Ethiopian Coffee
Classification using image
processing
[30] Morphology,
Texture, and
color feature
descriptor.
ANN  The manual threshold value is
used.
 Color features are extracted
including the background.
 Image enhancement, filtering, and
color space conversion are not yet
applied.
The effects of
segmentation techniques
for identification of
Ethiopian coffee variety
[23] Morphology,
Texture, and
color feature
descriptor.
ANN  The texture and color features of
an image are hand-crafted features
that are less effective to generalize
related class features.
Raw quality value
classification of Ethiopian
Coffee in Wellega region.
[22] Morphology,
Texture, and
color feature
descriptor.
Naive Bayes,
C4.5, and ANN.
 Considers only healthy coffee
beans.
 Used only color and texture
features which have the less
generalizing ability when used
alone.

18. Proposed Model Architecture for classification of coffee bean varieties

19. Experiment and result
Dataset acquisition
and preparation
• Data collected at ECX.
• We used Redmi Note 6 pro with 12MP + 5MP Dual rear camera
• 120 image samples taken per class of Harar,Jimma,Limu,Sidama,
&Wellega .
• Camera stand used with distance of 120 mm and spatial resolution
of 360x360.
• Augmentation were used
Experimental
setups
• Python 3.8
• Intel(R) Core(TM) i3-7020U CPU @ 2.30GHz 2.30 GHz.
• train-test split of 80/20
• Eleven were experiments conducted

20. CNN model Construction
 We have conducted a different experiment on the proposed end-to-end CNN model
 To refine suitable model architecture for our dataset
 Because the complexity of the CNN model is highly dependent on the data [73].
 We carried out two broad experiments to construct an end-to-end CNN model.
 The first experiment is conducted to determine parameters and components of CNN.
 The second experiment is conducted to enhance the performance CNN by applying
preprocessing, segmentation, and histogram equalization.

21. CNN model construction
I. CNN model parameter selection.
The following table shows summary of experimental result obtained.
Parameter Value Accuracy
Image size 128,224,292, & 360 74.16,81.33, 77.49, 82.49
Train-test split 70/30, 80/20, 90/10 77.22, 89.99, 75.85
Convolution layers 3, 4, 5, 6, 7 80, 83.31, 79.43, 77.57, 78.26
Pooling operations Max, Average, Combined 81.34, 79.45, 82.49
Filter size 3x3, 5x5, 7x7, combined 77.98, 78.42, 79.24, 84.86
Activation and
optimizers
Adam+Relu, Adam+Tanh,SGD+Relu,SGD+Tanh 82.49, 80, 82.29, 79.69

22. Cont..
II. CNN model performance enhancement
86.16 84.12 84.2 81.83
82.81 80.84
79.17 79.46
84.58 82.91 83.74 80.89
11 11 11 11
0
10
20
30
40
50
60
70
80
90
100
Median Gaussian Bilateral Unfiltered
Comparison of filtering Techniques
Training_acc(%) Val_acc(%) Test_acc(%) Time/epoch(sec.)
92.8
88.84 89.66
11
86.16 82.81 84.58
11
0
10
20
30
40
50
60
70
80
90
100
Training_acc(%) Val_acc(%) Testing_acc(%) Time/epoch(sec)
The effect o Histogram Equalization(AHE)
With Contrast enhancement Without enhancement

23. Cont..
Proposed
model
Architecture

24. Accuracy and loss of end-to-end CNN model

25. Performance evaluation of CNN model

26. Classification of CB verities using SVM classifier
 We used SVM classifier instead of SoftMax.
 We extract features using HOG, CNN and Hybrid of the two.
 A non-linear kernel function RBF are used over multiclass SVM CB
classification.
 We used a 10-fold cross-validation.

27. HOG feature over SVM classifier

28. CNN feature over SVM classifier

29. CNN-HOG feature over SVM classifier

30. Comparisons of HOG, CNN, and the hybrid feature vector
Feature extraction algorithm Accuracy
HOG 74.17 %
CNN 85.83 %
CNN-HOG(Deep-shallow) 97.5 %
Comparison end-to-end CNN and HOG-CNN algorithms
Algorithm Classifier Accuracy
End-to-end CNN SoftMax 89.99 %
Deep-shallow SVM 97.5 %

31. Comparison of our model with pre-trained VGG16 and ResNet50
Model Type Accuracy
VGG16 87.5 %
ResNet50 81.2 %
CNN 89.99 %
Discussion of results
 We conduct an experiment to choose building parameters of CNN model
because the default setting of the model did not perform well for all types of
the dataset [36].

32. Cont..
 We assess input dimension of our data by using 128, 224, 292, & 360.
 Image size 360 performs better than others due to
 Additive pixels during feature extraction [53].
 But elapsed time increases
We used Adam and SGD optimizer with ReLU and Tanh activation function
 Adam + ReLU performs better than Adam + Tanh, SGD + ReLU, and SGD +Tanh by
0.2 %, 2.49 %, 2.8 % respectively.
 ReLU has lower processing time than Tanh in our experiment, because the Tanh range
is between -1 to 1 to adjust the weights of the network whereas ReLU is 0 and 1 [73].

33. Cont..
 We used a combination of Average and Max pooling and the performance of the model
enhanced by 2.49%, this is because feature loss is reduced when combined.
 We employed Median filtering, Gaussian filtering, and Bilateral filtering during the
experiment to get the best results.
 The use of Median filtering improves model performance by 1.97% due to
 Effective at reducing impulsive noises such as salt and paper noises.
 keeping the edge of the coffee beans.
 After MF, we enhance the contrast of blurred image by using AHE and the model
performance enhanced by 5.08 %.

34. Cont..
 We used a combination of Average and Max pooling and the performance of the model
enhanced by 2.49%, this is because feature loss is reduced when combined.
 We employed Median filtering, Gaussian filtering, and Bilateral filtering during the
experiment to get the best results.
 The use of Median filtering improves model performance by 1.97% due to
 Effective at reducing impulsive noises such as salt and paper noises.
 keeping the edge of the coffee beans.
 After MF, we enhance the contrast of blurred image by using AHE and the model performance
enhanced by 5.08 %.
 End-to-end CNN model performs 89.99% after K-Means segmentation technique are applied.

35. Cont..
 However, CNN with SoftMax classifier are data intensive when trained with complex
networks, so we used multi-class SVM classifier to enhance class prediction ability.
 We used HOG local feature descriptor But the performance is highly degraded,
because local features are not successful at generalizing related classes.
 So, we used CNN as FE, shows an improved accuracy of 85.83%.
 But, CNN feature are not powerful as the hybrid deep-shallow features.
 The hybrid deep-shallow feature outperforms by 11.67% (97.5%) accuracy.
 This shows that, usage of hybrid features improves the performance of classification
than local or deep features used separately on a small dataset.

36. Cont..
 Finally, we compare our model with state-of-the-art pretrained models VGG16 and
ResNet50.
The performance of the proposed model performs 8.79% better than ResNet50 and
2.49 % than VGG16.
 Because, VGG16 and ResNet50 were trained with more than 138 million trainable
parameters.
Millions of trainable parameters require thousands of training datasets to get enough
training features [95].
However, our model is limited to 240 images per class, which has 383k trainable
parameters. The proposed model's smaller size makes it more efficient.

37. Conclusion
 Coffee contributes to Ethiopia's foreign currency earnings.
 Nowadays, the sub-sector is attracting governmental and nonprofit attention.
 So, coffee should be identified in a uniform manner
 To this, we use end-to-end CNN and hybrid feature extraction models
 Data collected at ECX and labeled to Harar, Jimma, Limu, Sidama, Wellega.
 We used 80/20 train-test split
 We used cross validation
 End-to-end CNN model achieved 89.99%.
 We used CNN-HOG to increase class prediction by using deep and handcraft features.

38. Cont..
 Multiclass SVM classifier with RBF kernel were used to classify HOG, CNN
and CNN-HOG features.
 We obtained 74.17%, 85.83%, and 97.5% accuracy respectively.
 Our model performs better with the combined Deep-shallow features.
 The challenge was the confusion error of Limu coffee with Sidama coffee and
Jima coffee with Harar, as well as Sidama, and Jimma coffee.
 Selected coffee regions' color and textural similarities.

39. Contribution
Hybrid Feature
Extraction
Image
Segmentation
Dataset
preparation

40. Recommendation
Identification of coffee bean varieties from mixed components.
Developing a GUI-based model for identification of CBV using smartphones.
The detection and counting of the number of defects.
Increasing class labels.