EffiMob-Net: A Deep Learning-Based Hybrid Model for Detection and Identification of Tomato Diseases Using Leaf Images
- A deep hybrid model was proposed that combines the architectures of two pretrained models, EfficientNet and MobileNet, for extracting the significant features of tomato leaves. Their outputs were then concatenated for the detection and classification of tomato leaf diseases.
- In the proposed method, the softmax layers of both pretrained models were removed, and the output achieved from the dense layers of both models was combined. In addition, three FC layers of size 512, 256, and 128 channels were added after the concatenation process. The classification was performed using the softmax layer which was added at the end of the proposed model.
- The dataset was preprocessed and prepared for training the proposed hybrid model using various preprocessing steps.
- The proposed model was trained using the extracted features.
- The study ensured the prevention of the proposed model’s overfitting by using various techniques, such as regularization, dropout, and BN.
- The proposed hybrid model was evaluated, and the classification report with descriptions is presented.
2. Related Work
3. Deep Learning Architectures
3.3. Proposed Hybrid Model
5. Experimental Setup
6. Results and Discussion
Institutional Review Board Statement
Data Availability Statement
Conflicts of Interest
- FAO Food and Agriculture Organization of United States. Available online: https://www.fao.org/land-water/databases-and-software/crop-information/tomato/en/ (accessed on 2 January 2023).
- Bhujel, A.; Kim, N.E.; Arulmozhi, E.; Basak, J.K.; Kim, H.T. A Lightweight Attention-Based Convolutional Neural Networks for Tomato Leaf Disease Classification. Agriculture 2022, 12, 228. [Google Scholar] [CrossRef]
- Chen, H.C.; Widodo, A.M.; Wisnujati, A.; Rahaman, M.; Lin, J.C.W.; Chen, L.; Weng, C.E. AlexNet Convolutional Neural Network for Disease Detection and Classification of Tomato Leaf. Electronics 2022, 11, 951. [Google Scholar] [CrossRef]
- Al-Gaashani, M.S.A.M.; Shang, F.; Muthanna, M.S.A.; Khayyat, M.; Abd El-Latif, A.A. Tomato leaf disease classification by exploiting transfer learning and feature concatenation. IET Image Process. 2022, 16, 913–925. [Google Scholar] [CrossRef]
- Zhao, S.; Peng, Y.; Liu, J.; Wu, S. Tomato leaf disease diagnosis based on improved convolution neural network by attention module. Agriculture 2021, 11, 651. [Google Scholar] [CrossRef]
- Vadivel, T.; Suguna, R. Automatic recognition of tomato leaf disease using fast enhanced learning with image processing. Acta Agric. Scand. Sect. B Soil Plant Sci. 2022, 72, 312–324. [Google Scholar] [CrossRef]
- Elnaggar, S.; Mohamed, A.M.; Bakeer, A.; Osman, T.A. Current status of bacterial wilt (Ralstonia solanacearum) disease in major tomato (Solanum lycopersicum L.) growing areas in Egypt. Arch. Agric. Environ. Sci. 2018, 3, 399–406. [Google Scholar] [CrossRef]
- Chowdhury, M.E.H.; Rahman, T.; Khandakar, A.; Ayari, M.A.; Khan, A.U.; Khan, M.S.; Al-Emadi, N.; Reaz, M.B.I.; Islam, M.T.; Ali, S.H.M. Automatic and Reliable Leaf Disease Detection Using Deep Learning Techniques. AgriEngineering 2021, 3, 294–312. [Google Scholar] [CrossRef]
- Zhang, K.; Wu, Q.; Liu, A.; Meng, X. Can deep learning identify tomato leaf disease? Adv. Multimed. 2018, 2018, 6710865. [Google Scholar] [CrossRef][Green Version]
- Alshammari, H.; Gasmi, K.; Ben Ltaifa, I.; Krichen, M.; Ben Ammar, L.; Mahmood, M.A. Olive Disease Classification Based on Vision Transformer and CNN Models. Comput. Intell. Neurosci. 2022, 2022, 3998193. [Google Scholar] [CrossRef]
- Khatoon, S.; Hasan, M.M.; Asif, A.; Alshmari, M.; Yap, Y.K. Image-based automatic diagnostic system for tomato plants using deep learning. Comput. Mater. Contin. 2021, 67, 595–612. [Google Scholar] [CrossRef]
- Ksibi, A.; Ayadi, M.; Soufiene, B.O.; Jamjoom, M.M.; Ullah, Z. MobiRes-Net: A Hybrid Deep Learning Model for Detecting and Classifying Olive Leaf Diseases. Appl. Sci. 2022, 12, 10278. [Google Scholar] [CrossRef]
- Tan, L.; Lu, J.; Jiang, H. Tomato Leaf Diseases Classification Based on Leaf Images: A Comparison between Classical Machine Learning and Deep Learning Methods. AgriEngineering 2021, 3, 542–558. [Google Scholar] [CrossRef]
- Bahhar, C.; Ksibi, A.; Ayadi, M.; Jamjoom, M.M.; Ullah, Z.; Soufiene, B.O.; Sakli, H. Wildfire and Smoke Detection Using Staged YOLO Model and Ensemble CNN. Electronics 2023, 12, 228. [Google Scholar] [CrossRef]
- Wagle, S.A.; Harikrishnan, R. A deep learning-based approach in classification and validation of tomato leaf disease. Trait. Signal 2021, 38, 699–709. [Google Scholar] [CrossRef]
- Nagamani, H.S.; Sarojadevi, H. Tomato Leaf Disease Detection using Deep Learning Techniques. Int. J. Adv. Comput. Sci. Appl. 2022, 13, 305–311. [Google Scholar]
- Kaur, P.; Harnal, S.; Gautam, V.; Singh, M.P.; Singh, S.P. An approach for characterization of infected area in tomato leaf disease based on deep learning and object detection technique. Eng. Appl. Artif. Intell. 2022, 115, 105210. [Google Scholar] [CrossRef]
- Islam, M.S.; Sultana, S.; Al Farid, F.; Islam, M.N.; Rashid, M.; Bari, B.S.; Hashim, N.; Husen, M.N. Multimodal Hybrid Deep Learning Approach to Detect Tomato Leaf Disease Using Attention Based Dilated Convolution Feature Extractor with Logistic Regression Classification. Sensors 2022, 22, 6079. [Google Scholar] [CrossRef] [PubMed]
- Garg, N.; Gupta, R.; Kaur, M.; Kukreja, V.; Jain, A.; Tiwari, R.G. Classification of Tomato Diseases using Hybrid Model (CNN-SVM). In Proceedings of the International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), Noida, India, 4–5 June 2020; IEEE: Noida, India, 2020. [Google Scholar]
- Bhatia, A.; Chug, A.; Singh, A.P. Hybrid SVM-LR classifier for powdery mildew disease prediction in tomato plant. In Proceedings of the International Conference on Signal Processing and Integrated Networks, Noida, India, 27–28 February 2020; pp. 218–223. [Google Scholar]
- Tarek, H.; Aly, H.; Eisa, S.; Abul-Soud, M. Optimized Deep Learning Algorithms for Tomato Leaf Disease Detection with Hardware Deployment. Electronics 2022, 11, 140. [Google Scholar] [CrossRef]
- Sareen, N.; Chug, A.; Singh, A.P. An image based prediction system for early blight disease in tomato plants using deep learning algorithm. J. Inf. Optim. Sci. 2022, 43, 761–779. [Google Scholar] [CrossRef]
- Sareen, N.; Chug, A.; Singh, A.P. An Image-Based Tomato Early Blight Disease Prediction Using Optimized Transfer. Vivekananda J. Res. 2021, 10, 1–13. [Google Scholar]
- Mondal, D.; Roy, K.; Pal, D.; Kole, D.K. Deep Learning-Based Approach to Detect and Classify Signs of Crop Leaf Diseases and Pest Damage. SN Comput. Sci. 2022, 3, 433. [Google Scholar] [CrossRef]
- Patokar, A.M.; Gohokar, V.V. Classification of Tomato Leaf Diseases: A Comparison of Different Optimizers. In Intelligent Systems and Applications, Proceedings of the ICISA 2022, Pune, India, 4–6 May 2022; Lecture Notes in Electrical Engineering; Springer: Singapore, 2023; pp. 27–33. [Google Scholar]
- Abbas, A.; Jain, S.; Gour, M.; Vankudothu, S. Tomato plant disease detection using transfer learning with C-GAN synthetic images. Comput. Electron. Agric. 2021, 187, 106279. [Google Scholar] [CrossRef]
- Habiba, S.U.; Islam, M.K. Tomato Plant Diseases Classification Using Deep Learning Based Classifier From Leaves Images. In Proceedings of the International Conference on Information and Communication Technology for Sustainable Development (ICICT4SD), Dhaka, Bangladesh, 27–28 February 2021; IEEE: Dhaka, Bangladesh, 2021. [Google Scholar]
- Fuentes, A.; Yoon, S.; Kim, S.C.; Park, D.S. A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition. Sensors 2017, 17, 2022. [Google Scholar] [CrossRef][Green Version]
- Afify, M.; Loey, M.; Elsawy, A. A Robust Intelligent System for Detecting Tomato Crop Diseases Using Deep Learning. Int. J. Softw. Sci. Comput. Intell. 2022, 14, 1–21. [Google Scholar] [CrossRef]
- Rangarajan, A.K.; Purushothaman, R.; Ramesh, A. Tomato crop disease classification using pre-trained deep learning algorithm. Procedia Comput. Sci. 2018, 133, 1040–1047. [Google Scholar] [CrossRef]
- Brahimi, M.; Boukhalfa, K.; Moussaoui, A. Deep Learning for Tomato Diseases: Classification and Symptoms Visualization. Appl. Artif. Intell. 2017, 31, 299–315. [Google Scholar] [CrossRef]
- Ashqar, B.A.M.; Abu-Naser, S.S. Image-Based Tomato Leaves Diseases Detection Using Deep Learning. Int. J. Acad. Eng. Res. 2018, 2, 10–16. [Google Scholar]
- Tan, M.; Le, Q.V. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. In Proceedings of the International Conference on Machine Learning, PMLR, Long Beach, CA, USA, 9–15 June 2019; pp. 6105–6114. [Google Scholar]
- Salian, S.R.; Sawarkar, S.D. Melanoma skin lesion classification using improved efficientnetb3. Jordanian J. Comput. Inf. Technol. 2022, 8, 45–57. [Google Scholar] [CrossRef]
- Intellipaat What Is Kernel in CNN? Available online: https://intellipaat.com/community/46826/what-is-kernel-in-cnn#:~:text=In%20Convolutional%20neural%20network%2C%20the,the%20matrix%20of%20dot%20products (accessed on 14 March 2023).
- Howard, A.G.; Zhu, M.; Chen, B.; Kalenichenko, D.; Wang, W.; Weyand, T.; Andreetto, M.; Adam, H. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv 2017, arXiv:1704.04861. [Google Scholar]
- Wang, W.; Li, Y.; Zou, T.; Wang, X.; You, J.; Luo, Y. A novel image classification approach via dense-mobilenet models. Mob. Inf. Syst. 2020, 2020, 7602384. [Google Scholar] [CrossRef][Green Version]
- Burns, E. Ensemble Modeling. Available online: https://www.techtarget.com/searchbusinessanalytics/definition/Ensemble-modeling (accessed on 4 February 2023).
- Ahmed, S.; Choi, K.Y.; Lee, J.J.; Kim, B.C.; Kwon, G.R.; Lee, K.H.; Jung, H.Y. Ensembles of Patch-Based Classifiers for Diagnosis of Alzheimer Diseases. IEEE Access 2019, 7, 73373–73383. [Google Scholar] [CrossRef]
- Simplilearn. The Best Guide to Regularization in Machine Learning. Available online: https://www.simplilearn.com/tutorials/machine-learning-tutorial/regularization-in-machine-learning#:~:text=Regularizationreferstotechniquesthat,andpreventoverfittingorunderfitting.&text=UsingRegularization%2Cwecanfit,re (accessed on 14 March 2023).
- Khan, Q. Tomato Disease Multiple Sources. Available online: https://www.kaggle.com/datasets/cookiefinder/tomato-disease-multiple-sources?resource=download-directory (accessed on 13 December 2022).
- Lawton, G. Data Preprocessing. Available online: https://www.techtarget.com/searchdatamanagement/definition/data-preprocessing (accessed on 28 December 2022).
- Al-Mudimigh, A.S.; Ullah, Z. Prevention of Dirty Data and the Role of MADAR Project. In Proceedings of the UKSim 5th European Symposium on Computer Modeling and Simulation, Madrid, Spain, 16–18 November 2011. [Google Scholar]
- Ullah, Z.; Al-Mudimigh, A.S. Integration and Communication to Prevent Dirty Data: The Role of MADAR Project. Information 2012, 15, 3459. [Google Scholar]
- Ullah, Z.; Jamjoom, M. An intelligent approach for Arabic handwritten letter recognition using convolutional neural network. PeerJ Comput. Sci. 2022, 8, e995. [Google Scholar] [CrossRef]
- Gholamy, A.; Kreinovich, V.; Kosheleva, O. Why 70/30 or 80/20 Relation between Training and Testing Sets: A Pedagogical Explanation. Dep. Tech. Rep. 2018, 1–6. [Google Scholar]
|Septoria leaf spots||99.86%||99.31%||99.39%||99.35%|
|Two spider mites||99.86%||98.99%||98.86%||98.93%|
|Tomato yellow leaf curl virus||99.89%||99.27%||99.39%||99.33%|
|Tomato mosaic virus||99.87%||99.19%||99.30%||99.25%|
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Ullah, Z.; Alsubaie, N.; Jamjoom, M.; Alajmani, S.H.; Saleem, F. EffiMob-Net: A Deep Learning-Based Hybrid Model for Detection and Identification of Tomato Diseases Using Leaf Images. Agriculture 2023, 13, 737. https://doi.org/10.3390/agriculture13030737
Ullah Z, Alsubaie N, Jamjoom M, Alajmani SH, Saleem F. EffiMob-Net: A Deep Learning-Based Hybrid Model for Detection and Identification of Tomato Diseases Using Leaf Images. Agriculture. 2023; 13(3):737. https://doi.org/10.3390/agriculture13030737Chicago/Turabian Style
Ullah, Zahid, Najah Alsubaie, Mona Jamjoom, Samah H. Alajmani, and Farrukh Saleem. 2023. "EffiMob-Net: A Deep Learning-Based Hybrid Model for Detection and Identification of Tomato Diseases Using Leaf Images" Agriculture 13, no. 3: 737. https://doi.org/10.3390/agriculture13030737