CSXAI: a lightweight 2D CNN-SVM model for detection and classification of various crop diseases with explainable AI visualization

Reazul Hasan Prince; Abdul Al Mamun; Hasibul Islam Peyal; Shafiun Miraz; Md Nahiduzzaman; Amith Khandakar; Mohamed Arselene Ayari

doi:10.3389/fpls.2024.1412988

CSXAI: a lightweight 2D CNN-SVM model for detection and classification of various crop diseases with explainable AI visualization

Front Plant Sci. 2024 Jul 5:15:1412988. doi: 10.3389/fpls.2024.1412988. eCollection 2024.

Authors

Reazul Hasan Prince¹, Abdul Al Mamun², Hasibul Islam Peyal^{1

3}, Shafiun Miraz³, Md Nahiduzzaman¹, Amith Khandakar⁴, Mohamed Arselene Ayari^{5

6}

Affiliations

¹ Department of Electrical and Computer Engineering, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh.
² Department of Computer Science and Engineering, Tejgaon College, Dhaka, Bangladesh.
³ Department of Computer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh.
⁴ Department of Electrical Engineering, College of Engineering, Qatar University, Doha, Qatar.
⁵ Department of Civil and Architectural Engineering, Qatar University, Doha, Qatar.
⁶ Technology Innovation and Engineering Education Unit, Qatar University, Doha, Qatar.

Abstract

Plant diseases significantly impact crop productivity and quality, posing a serious threat to global agriculture. The process of identifying and categorizing these diseases is often time-consuming and prone to errors. This research addresses this issue by employing a convolutional neural network and support vector machine (CNN-SVM) hybrid model to classify diseases in four economically important crops: strawberries, peaches, cherries, and soybeans. The objective is to categorize 10 classes of diseases, with six diseased classes and four healthy classes, for these crops using the deep learning-based CNN-SVM model. Several pre-trained models, including VGG16, VGG19, DenseNet, Inception, MobileNetV2, MobileNet, Xception, and ShuffleNet, were also trained, achieving accuracy ranges from 53.82% to 98.8%. The proposed model, however, achieved an average accuracy of 99.09%. While the proposed model's accuracy is comparable to that of the VGG16 pre-trained model, its significantly lower number of trainable parameters makes it more efficient and distinctive. This research demonstrates the potential of the CNN-SVM model in enhancing the accuracy and efficiency of plant disease classification. The CNN-SVM model was selected over VGG16 and other models due to its superior performance metrics. The proposed model achieved a 99% F1-score, a 99.98% Area Under the Curve (AUC), and a 99% precision value, demonstrating its efficacy. Additionally, class activation maps were generated using the Gradient Weighted Class Activation Mapping (Grad-CAM) technique to provide a visual explanation of the detected diseases. A heatmap was created to highlight the regions requiring classification, further validating the model's accuracy and interpretability.

Keywords: convolutional neural network (CNN); gradient-weighted class activation mapping (GRAD-CAM); plant diseases; pre-trained models; support vector machine (SVM).

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. The paper publication has been possible with the support of Qatar National Library funding and the work was supported by Qatar Research, Development and Innovation (QRDI) fund HSREP05-1012-230035.