Feature-Level Fusion of CNN and Vision Transformer for Tomato Leaf Disease Identification

Afnan Ahmed; Sadiq Ali

Authors

Afnan Ahmed Department of Electrical Engineering University of Engineering Technology Peshawar, Pakistan
Sadiq Ali Department of Electrical Engineering, University of Engineering and Technology (UET) Peshawar, Pakistan

Keywords:

Leaf disease detection, Hybrid model, Deep learning models

Abstract

Tomato leaf diseases pose a serious threat to crop yield and quality, necessitating timely and accurate detection for effective management. Traditional visual inspection methods are subjective, labor-intensive, and inefficient, highlighting the need for automated solutions. This study explores the use of transfer learning and fine-tuning of deep learning models, ResNet-50 and Vision Transformers (ViT), for tomato leaf disease detection. A novel hybrid model integrating ResNet-50 and ViT through feature-level fusion is proposed to enhance classification accuracy. While ResNet-50 and ViT achieved accuracies of 95.20% and 98%, respectively, the hybrid model outperformed both with 99.07% accuracy. These results demonstrate the effectiveness and scalability of the hybrid model for early disease detection, offering a promising solution to enhance crop health and agricultural productivity.

References

P. L. Tong Li, Jiaxin Cui, Wei Guo, Yingjun She, “The Influence of Organic and Inorganic Fertilizer Applications on Nitrogen Transformation and Yield in Greenhouse Tomato Cultivation with Surface and Drip Irrigation Techniques,” Water, vol. 15, no. 20, p. 3546, 2023, doi: https://doi.org/10.3390/w15203546.

Yuanhui Yu, “Research Progress of Crop Disease Image Recognition Based on Wireless Network Communication and Deep Learning,” Wirel. Commun. Mob. Comput., 2021, doi: https://doi.org/10.1155/2021/7577349.

H. Durmus, E. O. Gunes, and M. Kirci, “Disease detection on the leaves of the tomato plants by using deep learning,” 2017 6th Int. Conf. Agro-Geoinformatics, Agro-Geoinformatics 2017, Sep. 2017, doi: 10.1109/AGRO-GEOINFORMATICS.2017.8047016.

E. D. I. Valenzuela, R. Baldovino, A. Bandala, “Pre-Harvest Factors Optimization Using Genetic Algorithm for Lettuce,” J. Telecommun. Electron. Comput. Eng., vol. 10, no. 1–4, pp. 159–163, 2018, [Online]. Available: https://jtec.utem.edu.my/jtec/article/view/3610

R. G. De Luna et al., “Identification of philippine herbal medicine plant leaf using artificial neural network,” HNICEM 2017 - 9th Int. Conf. Humanoid, Nanotechnology, Inf. Technol. Commun. Control. Environ. Manag., vol. 2018-January, pp. 1–8, Jul. 2017, doi: 10.1109/HNICEM.2017.8269470.

I. C. Valenzuela et al., “Quality assessment of lettuce using artificial neural network,” HNICEM 2017 - 9th Int. Conf. Humanoid, Nanotechnology, Inf. Technol. Commun. Control. Environ. Manag., vol. 2018-January, pp. 1–5, Jul. 2017, doi: 10.1109/HNICEM.2017.8269506.

J. B. U. Dimatira et al., “Application of fuzzy logic in recognition of tomato fruit maturity in smart farming,” IEEE Reg. 10 Annu. Int. Conf. Proceedings/TENCON, pp. 2031–2035, Feb. 2017, doi: 10.1109/TENCON.2016.7848382.

I. C. Valenzuela, R. G. Baldovino, A. A. Bandala, and E. P. Dadios, “Optimization of Photosynthetic Rate Parameters using Adaptive Neuro-Fuzzy Inference System (ANFIS),” 2017 Int. Conf. Comput. Appl. ICCA 2017, pp. 129–134, Oct. 2017, doi: 10.1109/COMAPP.2017.8079734.

J. Shijie, J. Peiyi, H. Siping, and Sl. Haibo, “Automatic detection of tomato diseases and pests based on leaf images,” Proc. - 2017 Chinese Autom. Congr. CAC 2017, vol. 2017-January, pp. 3507–3510, Dec. 2017, doi: 10.1109/CAC.2017.8243388.

Y. H. Zhiwen Tang, Xinyu He, Guoxiong Zhou, Aibin Chen, Yanfeng Wang, Liujun Li, “A Precise Image-Based Tomato Leaf Disease Detection Approach Using PLPNet,” Plant Phenomics, vol. 5, p. 0042, 2023, doi: https://doi.org/10.34133/plantphenomics.0042.

Xuewei Wang & Jun Liu, “An efficient deep learning model for tomato disease detection,” Plant Methods, vol. 20, no. 61, 2024, doi: https://doi.org/10.1186/s13007-024-01188-1.

T. C. Yufei Liu, Yihong Song, Ran Ye, Siqi Zhu, Yiwen Huang, “High-Precision Tomato Disease Detection Using NanoSegmenter Based on Transformer and Lightweighting,” Plants, vol. 12, no. 13, p. 2559, 2023, doi: https://doi.org/10.3390/plants12132559.

X. W. Jun Liu, “Tomato Diseases and Pests Detection Based on Improved Yolo V3 Convolutional Neural Network,” Front. Plant Sci, vol. 11, 2020, doi: https://doi.org/10.3389/fpls.2020.00898.

A. Ramcharan, K. Baranowski, P. McCloskey, B. Ahmed, J. Legg, and D. P. Hughes, “Deep learning for image-based cassava disease detection,” Front. Plant Sci., vol. 8, no. October, pp. 1–7, 2017, doi: 10.3389/fpls.2017.01852.

S. P. Mohanty, D. P. Hughes, and M. Salathé, “Using deep learning for image-based plant disease detection,” Front. Plant Sci., vol. 7, no. September, Sep. 2016, doi: 10.3389/FPLS.2016.01419.

I. Z. Mukti and D. Biswas, “Transfer Learning Based Plant Diseases Detection Using ResNet50,” 2019 4th Int. Conf. Electr. Inf. Commun. Technol. EICT 2019, Dec. 2019, doi: 10.1109/EICT48899.2019.9068805.

A. Tabbakh and S. S. Barpanda, “A Deep Features Extraction Model Based on the Transfer Learning Model and Vision Transformer ‘TLMViT’ for Plant Disease Classification,” IEEE Access, vol. 11, pp. 45377–45392, 2023, doi: 10.1109/ACCESS.2023.3273317.

R. O. Ogundokun, R. Maskeliunas, S. Misra, and R. Damaševičius, “Improved CNN Based on Batch Normalization and Adam Optimizer,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 13381 LNCS, pp. 593–604, 2022, doi: 10.1007/978-3-031-10548-7_43.

A. D. K. Muslih, “Tomato Leaf Diseases Classification using Convolutional Neural Networks with Transfer Learning Resnet-50,” Kinet. Game Technol. Inf. Syst. Comput. Network, Comput. Electron. Control, vol. 9, no. 2, 2024, doi: https://doi.org/10.22219/kinetik.v9i2.1939.

M. J. S. Utpal Barman, Parismita Sarma, Mirzanur Rahman, Vaskar Deka, Swati Lahkar, Vaishali Sharma, “ViT-SmartAgri: Vision Transformer and Smartphone-Based Plant Disease Detection for Smart Agriculture,” Agronomy, vol. 14, no. 2, p. 327, 2024, doi: https://doi.org/10.3390/agronomy14020327.