Open Access
Subscription Access
An Efficient Tomato Leaf Disease Classification Framework by Background Removal Using Fully Convolutional Network and Residual Transformer Network
Tomato is a widely utilized vegetable that secured a superior place in enhancing the economic rate in agriculture. Production quantity of tomatoes secured seventh place worldwide. Still, leaf diseases attained in the tomato plant are considered a more challenging issue as they affect the normal growth of the plant badly. Here, different kinds of plant diseases have occurred in the plant leaves which generate more losses in crop yield. In the early phase, the accurate prediction of plant disease effectively minimizes the production loss and offered to enhance crop yield. Moreover, the conventional approaches faced more complexity as the existing system needs more computational time and they are costly. To overcome this problem, an effective tomato disease classification model is proposed using a transformer-based network. Initially, the raw tomato plant images are gathered through real-time data. Then, they have undergone a pre-processing stage to remove the unwanted image pixels. Further, the backgrounds of images are removed by using the Fully Convolutional Network (FCN). Finally, the disease classifications are accomplished by using Residual Transformer Network (RTN). The performance is validated with divergent measures and compared with traditional methods. Thus, the results declare that it achieves an impressive classification rate to avoid less crop productivity.
Keywords
Tomato Leaf Disease Classification, Background Removal, Fully Convolutional Network, Residual Transformer Network.
User
Font Size
Information
- C. Zhou, S. Zhou, J. Xing and J. Song, "Tomato Leaf Disease Identification by Restructured Deep Residual Dense Network," IEEE Access, vol. 9, pp. 28822-28831, 2021,
- S. Ahmed, M. B. Hasan, T. Ahmed, M. R. K. Sony, and M. H. Kabir, "Less is More: Lighter and Faster Deep Neural Architecture for Tomato Leaf Disease Classification," IEEE Access, vol. 10, pp. 68868-68884, 2022,
- T.Anandhakrishnan and S.M.Jaisakthi, "Deep Convolutional Neural Networks for image based tomato leaf disease detection,"Sustainable Chemistry and Pharmacy, Vol.30, pp. 100793, December 2022.
- Yukai Zhang, Shuangjie Huanga, Guoxiong Zhou, Yahui Hu and Liujun Lic, "Identification of tomato leaf diseases based on multi-channel automatic orientation recurrent attention network," Computers and Electronics in Agriculture, Vol. 205, pp. 107605, February 2023.
- Harshit Kaushik, Anvi Khanna, Dilbag Singh, Manjit Kaur and Heung-No Lee, "TomFusioNet: A tomato crop analysis framework for mobile applications using the multi-objective optimization based late fusion of deep models and background elimination, "Applied Soft Computing, Volume 133, pp. 109898, January 2023.
- G. Yang, G. Chen, Y. He, Z. Yan, Y. Guo and J. Ding, "Self-Supervised Collaborative Multi-Network for Fine-Grained Visual Categorization of Tomato Diseases," IEEE Access, vol. 8, pp. 211912-211923, 2020,
- N. Schor, A. Bechar, T. Ignat, A. Dombrovsky, Y. Elad and S. Berman, "Robotic Disease Detection in Greenhouses: Combined Detection of Powdery Mildew and Tomato Spotted Wilt Virus," IEEE Robotics and Automation Letters, vol. 1, no. 1, pp. 354-360, Jan. 2016,
- Q. Wu, Y. Chen and J. Meng, "DCGAN-Based Data Augmentation for Tomato Leaf Disease Identification," IEEE Access, vol. 8, pp. 98716-98728, 2020,
- Y. Zhang, C. Song and D. Zhang, "Deep Learning-Based Object Detection Improvement for Tomato Disease," IEEE Access, vol. 8, pp. 56607-56614, 2020
- Jun Liu and Xuewei Wang, "Early recognition of tomato gray leaf spot disease based on MobileNetv2-YOLOv3 model," Plant Methods, vol.16, no. 83, 2020.
- Marriam Nawaz, Tahira Nazir, Ali Javed, Momina Masood, Junaid Rashid, Jungeun Kim and Amir Hussain, "A robust deep learning approach for tomato plant leaf disease localization and classification," Scientific Reports, vol. 12, mo. 18568, 2022.
- Mariam Moussafir, Hasna Chaibi, Rachid Saadane, Abdellah Chehri, Abdessamad El Rharras and Gwanggil Jeon, "Design of efficient techniques for tomato leaf disease detection using genetic algorithm-based and deep neural networks," Plant and Soil, vol. 479, pp. 251–266, 2022.
- Nasim Ahmed, Syed Shan-e-Ali Zaidi, Imran Amin, Brian E. Scheffler and Shahid Mansoor, "Tomato leaf curl Oman virus and associated Betasatellite causing leaf curl disease in tomato in Pakistan," European Journal of Plant Pathology, vol. 160, pp. 249–257, 2021.
- Sayed Sartaj Sohrab, Muhammad Yasir, Sherif Ali El-Kafrawy, Ayman T. Abbas, Magdi Ali Ahmed Mousa and Ahmed A. Bakhashwain, "Association of tomato leaf curl Sudan virus with leaf curl disease of tomato in Jeddah, Saudi Arabia," VirusDisease, vol.27, pp. 145–153, 2016.
- K. V. Ashwathappa, V. Venkataravanappa, M. Nandan, Shridhar Hiremath, C. N. Lakshminarayana Reddy, K. S. Shankarappa & M. Krishna Reddy, "Association of Tomato leaf curl Karnataka virus and satellites with enation leaf curl disease of Pseuderanthemum reticulatum (Radlk.) a new ornamental host for begomovirus infecting tomato in India," Indian Phytopathology, vol. 74, pp. 1065–1073, 2021.
- Xincong Yang, Heng Li, Yantao Yu, Xiaochun Luo, Ting Huang, Xu Yang, "Automatic Pixel-Level Crack Detection and Measurement Using Fully Convolutional Network," 29 August 2018.
- O. Daanouni, B. Cherradi and A. Tmiri, "NSL-MHA-CNN: A Novel CNN Architecture for Robust Diabetic Retinopathy Prediction Against Adversarial Attacks," IEEE Access, vol. 10, pp. 103987-103999, 2022.
- S. Song, J. C. K. Lam, Y. Han and V. O. K. Li, "ResNet-LSTM for Real-Time PM2.5 and PM₁ ₀ Estimation Using Sequential Smartphone Images," IEEE Access, vol. 8, pp. 220069-220082, 2020.
- M. Mansouri, K. Dhibi, M. Hajji, K. Bouzara, H. Nounou and M. Nounou, "Interval-Valued Reduced RNN for Fault Detection and Diagnosis for Wind Energy Conversion Systems," IEEE Sensors Journal, vol. 22, no. 13, pp. 13581-13588, 1 July1, 2022.
Abstract Views: 262
PDF Views: 0