POA optimized VGG16-SVM architecture for severity level classification of Ascochyta blight of chickpea

Figures & data

Table 1. Number of samples for each class before and after augmentation.

No	Class	No. of samples before augmentation	No. of samples after augmentation
1.	Asymptomatic (A)	250	1250
2.	Resistant (R)	290	1450
3.	Moderately Resistant (MR)	324	1620
4.	Susceptible (S)	300	1500
5.	Highly Susceptible (HS)	400	2000
Total No of images		1564	7820

Table 2. Number of images used for training, validation, and testing before and after augmentation.

Groups	Before augmentation	After augmentation
Training	1095	5474
Validation	156	782
Testing	313	1564

Figure 1. Architecture of the proposed model.

Table 3. Performance metrics.

Test accuracy	$\frac{\text{Number of predictions correct}(\mathrm{\text{TP}}+\mathrm{\text{TN}})}{\text{Number of predictions made}(\mathrm{\text{TP}}+\mathrm{\text{FP}}+\mathrm{\text{TN}}+\mathrm{\text{FN}})}$
Precision	$\frac{\text{Number of true positive}(\mathrm{\text{TP}})}{\text{Number of true positive}\left(\mathrm{\text{TP}}\right)+\text{Number of false positive}(\mathrm{\text{FP}})}$
Recall	$\frac{\text{Number of true positive}(\mathrm{\text{TP}})}{\text{Number of true positive}\left(\mathrm{\text{TP}}\right)+\text{Number of false negative}(\mathrm{\text{FN}})}$
F1	$\frac{2\times \text{Precision}\times \mathrm{\text{Recall}}}{\mathrm{\text{Precision}}+\mathrm{\text{Recall}}}$

Table 4. Experimental results.

No	Method	Augmentation	Accuracy (%)
1.	VGG16	No	82
2.	VGGNet + Softmax	No	83
3.	VGGNet + SVM	No	84.5
4.	VGGNet + SVM	Yes	85
5.	VGGNet + POA + SVM	Yes	88
6.	VGGNet + POA + Gaussian + SVM	Yes	92
7.	VGGNet + POA + Adaptive median + SVM	Yes	95
8.	VGGNet + POA + (Gaussian- Adaptive median) + SVM	Yes	96

Figure 2. Training and validation accuracy.

Figure 3. Training and validation loss.

Figure 4. Confusion matrix of the proposed model.

Table 5. The results of precision, recall, and f1 score of the proposed model for each class.

Metrics	A	R	MR	S	HS	Average
Precision	0.96	0.95	0.93	0.94	1.0	0.956
Recall	0.95	0.97	0.94	0.97	0.96	0.958
F1-Score	0.96	0.96	0.94	0.95	0.98	0.958

Table 6. Summary of related studies on chickpea plant diseases classification.

Authors	Objective	Model	Accuracy
(Belay et al., 2022)	Classification of chickpea plant diseases	CNN-LSTM	92.55%
(Hayit et al., 2023)	Severity level classification of Fusarium wilt of chickpea	DenseNet-201	90%
Proposed	Severity level classification of Ascochyta blight of chickpea	VGGNet + POA + (Gaussian- Adaptive median) + SVM	96%

Belay, A. J., Salau, A. O., Ashagrie, M., & Haile, M. B. (2022). Development of a chickpea disease detection and classification model using deep learning. Informatics in Medicine Unlocked, 31, 100970. https://doi.org/10.1016/j.imu.2022.100970

 Google Scholar

Hayit, T., Endes, A., & Hayit, F. (2023). The severity level classification of Fusarium wilt of chickpea by pre ‑ trained deep learning models. Journal of Plant Pathology, 106(1), 93–105. https://doi.org/10.1007/s42161-023-01520-z

 Web of Science ®Google Scholar

Data availability statement

The author wants to declare that they can submit the data at any time based on publisher’s request. The datasets used and/or analyzed during the current study will be available from the author on reasonable request.