A Hybrid Approach for the Detection and Classification of Tomato Leaf Diseases
Source abstract
Abstract
In this paper, we proposed a hybrid deep learning approach for detecting and classifying tomato plant leaf diseases early. This hybrid system is a combination of a convolutional neural network (CNN), convolutional attention module (CBAM), and support vector machines (SVM). Initially, the proposed model can detect nine different tomato diseases but is not limited to this. The proposed system is tested using a database containing images of tomato leaves. The obtained results were very encouraging, giving us accuracy up to 97.2%, which can be improved with the improvement of learning processes. The proposed system is very efficient and lightweight, so the farmer can install it on any smart device having a digital camera and processing capabilities. With a bit of training, a farmer can detect any disease immediately, which will help him take timely pre-emptive action.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Smart Agriculture and AI · Spectroscopy and Chemometric Analyses · Leaf Properties and Growth Measurement
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Computer Science, Faculty of Computer & Information Technology, University of Tabuk, Tabuk 71491, Saudi Arabia
Sensor Networks and Cellular Systems (SNCS) Research Center, University of Tabuk, Tabuk 71491, Saudi Arabia
References (19)
Show source-provided references
1. Food and Agriculture Organization of the United Nations [FAO] (2019). FAO Publications Catalogue 2019, FAO.
2. Automatic and Reliable Leaf Disease Detection Using Deep Learning Techniques10.3390/agriengineering3020020 ↗
3. Novel Fusion of Color Balancing and Superpixel Based Approach for Detection of Tomato Plant Diseases in Natural Complex Environment
4. Agricultural Plant Leaf Disease Detection Using Image Processing
5. Machine Vision-Based Papaya Disease Recognition
6. A New Proposal for Automatic Identification of Multiple Soybean Diseases10.1016/j.compag.2019.105060 ↗
7. A Segmentation Method for Greenhouse Vegetable Foliar Disease Spots Images Using Color Information and Region Growing10.1016/j.compag.2017.08.023 ↗
8. Zhao, S., Peng, Y., Liu, J., and Wu, S. (2021). Tomato Leaf Disease Diagnosis Based on Improved Convolution Neural Network by Attention Module. Agriculture, 11.10.3390/agriculture11070651 ↗
9. Bhujel, A., Kim, N.E., Arulmozhi, E., Basak, J.K., and Kim, H.T. (2022). A Lightweight Attention-Based Convolutional Neural Networks for Tomato Leaf Disease Classification. Agriculture, 12.10.3390/agriculture12020228 ↗
10. Kumar, A., and Vani, M. (2019, January 6–8). Image based tomato leaf disease detection. Proceedings of the 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Kanpur, India.10.1109/ICCCNT45670.2019.8944692 ↗
11. Kaur, P., Harnal, S., Tiwari, R., Upadhyay, S., Bhatia, S., Mashat, A., and Alabdali, A.M. (2022). Recognition of Leaf Disease Using Hybrid Convolutional Neural Network by Applying Feature Reduction. Sensors, 22.10.3390/s22020575 ↗
12. Trivedi, N.K., Gautam, V., Anand, A., Aljahdali, H.M., Villar, S.G., Anand, D., Goyal, N., and Kadry, S. (2021). Early Detection and Classification of Tomato Leaf Disease Using High-Performance Deep Neural Network. Sensors, 21.10.3390/s21237987 ↗
13. ToLeD: Tomato Leaf Disease Detection Using Convolution Neural Network10.1016/j.procs.2020.03.225 ↗
14. Hong, H., Lin, J., and Huang, F. (2020, January 12–14). Tomato disease detection and classification by deep learning. Proceedings of the 2020 International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE), Fuzhou, China.10.1109/ICBAIE49996.2020.00012 ↗
15. Tomato Leaf Disease Identification by Restructured Deep Residual Dense Network10.1109/ACCESS.2021.3058947 ↗
16. Kibriya, H., Abdullah, I., and Nasrullah, A. (2021, January 13–14). Plant disease identification and classification using convolutional neural network and SVM. Proceedings of the 2021 International Conference on Frontiers of Information Technology (FIT), Islamabad, Pakistan.10.1109/FIT53504.2021.00056 ↗
17. Classification of Plant Disease Using SVM and Deep Learning10.1016/j.matpr.2021.05.029 ↗
18. Hughes, D.P., and Salathé, M. (2015). An Open Access Repository of Images on Plant Health to Enable the Development of Mobile Disease Diagnostics. arXiv.
19. He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27–30). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.10.1109/CVPR.2016.90 ↗