Efficient-ViT B0Net: A high-performance light weight transformer for rice leaf disease recognition and classification

Authors

  • Santosh Kumar Upadhyay
    Department of CSE, Ajay Kumar Garg Engineering College, Ghaziabad, Uttar Pradesh, India
  • Rajesh Prasad
    Department of CSE, Ajay Kumar Garg Engineering College, Ghaziabad, Uttar Pradesh, India
    Department of Computer Science, African University of Science and Technology, Abuja, Nigeria
    https://orcid.org/0000-0002-3456-6980

Keywords:

Plant disease, Deep learning, Efficient net B0, Vision transformer

Abstract

Plant disease detection has become a demanding and challenging task in today’s environment because many different types of plants exist world-wide, and very varied infections are found in them. The proposed work introduced a hybrid architecture to perform plant disease recognition and classification accurately and efficiently. The proposed model utilizes the strengths of CNN and Vision Transformer, where CNN successfully extracts local fine-grained texture features quickly. At the same time, ViT plays a vital role in extracting global and deep features from the leaf images. The suggested model was evaluated on a rice leaf dataset for paddy disease recognition and classification. The dataset consists of images representing four different types of rice leaves, with each class containing 4,000 samples. It includes healthy and diseased leaves, where the diseased category is further divided into three specific classes: Brown Spot, Bacterial Leaf Blight, and Leaf Smut. The suggested model worked well on the input dataset and achieved a testing accuracy of 99.13%. The Precision, recall, and F1 score of the proposed model were recorded as 99.13%, 99.13%, and 99.13%, respectively. The proposed method achieves a classification accuracy of 99.13%, outperforming SOTA models such as ViT-small, DenseNet121, ResNet50, EfficientNet B0 and SqueezeNet by 2–9% on the same dataset. The proposed method was compared with other approaches on the same experimental environment. These results demonstrate the effectiveness of our EfficientNet-ViT-based pipeline in capturing both local and global features for accurate rice disease classification.

Dimensions

[1] Y. Borhani, J. Khoramdel & E. Najafi, “A deep learning based approach for automated plant disease classification using vision transformer”, Sci. Rep. 12 (2022) 1. https://doi.org/10.1038/s41598-022-15163-0.

[2] M. Shoaib, B. Shah, S. EI-Sappagh, A. Ali, A. Ullah, F. Alenezi, T. Gechev, T. Hussain, F. Ali, “An advanced deep learning models-based plant disease detection: A review of recent research”, Frontiers in Plant Science 14 (2023) 1. https://doi.org/10.3389/fpls.2023.1158933.

[3] H. Hamdani, A. Septiarini, A. Sunyoto, S. Suyanto & F. Utaminingrum, “Detection of oil palm leaf disease based on color histogram and supervised classifier”, Optik (Stuttg). 245 (2021) 1. https://doi.org/10.1016/j.ijleo.2021.167753.

[4] C. Hou, J. Zhuang, Y. Tang, Y. He, A. Miao, H. Huang, S. Luo, “Recognition of early blight and late blight diseases on potato leaves based on graph cut segmentation”, J. Agric. Food Res. 5 (2021) 1. https://doi.org/10.1016/j.jafr.2021.100154.

[5] Y. Sun, Z. Jiang, L. Zhang, W. Dong & Y. Rao, “SLIC SVM based leaf diseases saliency map extraction of tea plant”, Comput. Electron. Agric. 157 (2019) 1. https://doi.org/10.1016/j.compag.2018.12.042.

[6] S. Zhang & Z. Wang, “Cucumber disease recognition based on global-local singular value decomposition”, Neurocomputing 205 (2016) 1. https://doi.org/10.1016/j.neucom.2016.04.034.

[7] A. Johannes, A. Picon, A. Alvarez-Gila, J. Echazarra, S. Rodriguez-Vaamonde, A. D. Navajas, A. Ortiz-Barredo, “Automatic plant disease diagnosis using mobile capture devices, applied on a wheat use case”, Comput. Electron. Agric. 138 (2017) 1. https://doi.org/10.1016/j.compag.2017.04.013.

[8] J. G. A. Barbedo, “Impact of dataset size and variety on the effectiveness of deep learning and transfer learning for plant disease classification”, Comput. Electron. Agric. 153 (2018) 1. https://doi.org/10.1016/j.compag.2018.08.013.

[9] S. P. Mohanty, D. P. Hughes & M. Salathé, “Using deep learning for image-based plant disease detection”, Front. Plant Sci. 7 (2016) 1. https://doi.org/10.3389/fpls.2016.01419.

[10] J. Chen, J. Chen, D. Zhang, Y. Sun & Y. A. Nanehkaran, “Using deep transfer learning for image-based plant disease identification”, Comput. Electron. Agric. 173 (2020) 1. https://doi.org/10.1016/j.compag.2020.105393.

[11] P. S. Thakur, T. Sheorey & A. Ojha, “VGG-ICNN: a lightweight CNN model for crop disease identification”, Multimed. Tools Appl. 82 (2023) 1. https://doi.org/10.1007/s11042-022-13144-z.

[12] S. R. Shah, S. Qadri, H. Bibi, S. M. W. Shah, M. I. Sharif & F. Marinello, “Comparing inception V3, VGG 16, VGG 19, CNN, and ResNet 50: a case study on early detection of a rice disease”, Agronomy 13 (2023) 1. https://doi.org/10.3390/agronomy13061633.

[13] Y. Kawasaki, H. Uga, S. Kagiwada & H. Iyatomi, “Basic study of automated diagnosis of viral plant diseases using convolutional neural networks”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2015, pp. 1. [Online]. https://doi.org/10.1007/978-3-319-27863-6_59.

[14] N. Upadhyay, S. K. Singh, R. Kumar, A. Sagar, “Rice leaves disease detection mechanism using VGG16 deep learning architecture”, in Innovations in data analytics, ICIDA 2023, D. Bhattacharya, A., Dutta, S. Dutta, P., Samanta (Ed.), Springer, 2024, p. 1005. https://doi.org/10.1007/978-981-97-4928-7_17.

[15] H. Mavi, S. K. Upadhyay, N. Srivastava, R. Sharma and R. Bhargava, “Crop recommendation system based on soil quality and environmental factors using machine learning”, in 2024 International Conference on Emerging Innovations and Advanced Computing (INNOCOMP), 2024, p. 1. https://doi.org/10.1109/INNOCOMP63224.2024.00089.

[16] B. Rashmi, S. K. Upadhyay, R. Sharma, N. Srivastava, H. Mavi, “Plant disease detection using Machine learning and CNN on leaf images”, in 2024 1st International Conference on Advanced Computing and Emerging Technologies (ACET), 2024, p. 1. https://doi.org/10.1109/ACET61898.2024.10730720.

[17] A. Fuentes, S. Yoon, S. C. Kim & D. S. Park, “A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition”, Sensors (Switzerland) 17 (2017) 1. https://doi.org/10.3390/s17092022.

[18] S. Yu, L. Xie & Q. Huang, “Inception convolutional vision transformers for plant disease identification”, Internet of Things (Netherlands) 21 (2023) 1. https://doi.org/10.1016/j.iot.2022.100650.

[19] W. Albattah, A. Javed, M. Nawaz, M. Masood & S. Albahli, “Artificial Intelligence-Based Drone System for Multiclass Plant Disease Detection Using an Improved Efficient Convolutional Neural Network”, Front. Plant Sci. 13 (2022) 1. https://doi.org/10.3389/fpls.2022.808380.

[20] W. Zeng & M. Li, “Crop leaf disease recognition based on Self-Attention convolutional neural network”, Comput. Electron. Agric. 172 (2020) 1. https://doi.org/10.1016/j.compag.2020.105341.

[21] J. Chen, D. Zhang, A. Zeb & Y. A. Nanehkaran, “Identification of rice plant diseases using lightweight attention networks”, Expert Syst. Appl. 169 (2021) 1. https://doi.org/10.1016/j.eswa.2020.114514.

[22] S. Wu, Y. Sun & H. Huang, “Multi-granularity feature extraction based on vision transformer for tomato leaf disease recognition”, in 2021 3rd International Academic Exchange Conference on Science and Technology Innovation, IAECST 2021, 2021, p. 1. https://doi.org/10.1109/IAECST54258.2021.9695688.

[23] R. Reedha, E. Dericquebourg, R. Canals & A. Hafiane, “Transformer neural network for weed and crop classification of high resolution UAV images”, Remote Sens. 14 (2022) 1. https://doi.org/10.3390/rs14030592.

[24] P. S. Thakur, P. Khannam, T. Sheorey, A. Ojha, “Explainable vision transformer enabled convolutional neural network for plant disease identification: PlantXViT”, 2022. ArXiv [Online]. https://doi.org/10.48550/arXiv.2207.07919.

[25] L. Yang, X. Yu, S. Zhang, H. Long, H. Zhang, S. Xu, Y. Liao, “GoogLeNet based on residual network and attention mechanism identification of rice leaf diseases”, Comput. Electron. Agric. 204 (2023) 1. https://doi.org/10.1016/j.compag.2022.107543.

[26] V. Sharma, A. K. Tripathi & H. Mittal, “DLMC-Net: deeper lightweight multi-class classification model for plant leaf disease detection”, Ecol. Inform. 75 (2023) 1. https://doi.org/10.1016/J.ECOINF.2023.102025.

[27] K. M. Hosny, W. M. El-Hady, F. M. Samy, E. Vrochidou & G. A. Papakostas, “Multi-class classification of plant leaf diseases using feature fusion of deep convolutional neural network and local binary pattern”, IEEE Access 11 (2023) 1. https://doi.org/10.1109/ACCESS.2023.3286730.

[28] B. M. Joshi & H. Bhavsar, “A nightshade crop leaf disease detection using enhance-nightshade-CNN for ground truth data”, Vis. Comput. 40 (2024) 5639. https://doi.org/10.1007/s00371-023-03127-y.

[29] H. T. Thai, K. H. Le & N. L. T. Nguyen, “FormerLeaf: an efficient vision transformer for Cassava Leaf Disease detection”, Comput. Electron. Agric. 204 (2023) 1. https://doi.org/10.1016/j.compag.2022.107518.

[30] K. Taji, A. Sohail, T. Shahzad, B. S. Khan, M. A. Khan, K. Ouahada, “An ensemble hybrid framework: a comparative analysis of metaheuristic algorithms for ensemble hybrid CNN features for plants disease classification”, IEEE Access 12 (2024) 61886. https://doi.org/10.1109/ACCESS.2024.3389648.

[31] A. K. Singh, A. Rao, P. Chattopadhyay, R. Maurya & L. Singh, “Effective plant disease diagnosis using vision transformer trained with leafy-generative adversarial network-generated images”, Expert Syst. Appl. 254 (2024) 1. https://doi.org/10.1016/J.ESWA.2024.124387.

[32] S. Aboelenin, F. A. Elbasheer, M. M. Eltoukhy, W. M. El-Hady & K. M. Hosny, “A hybrid framework for plant leaf disease detection and classification using convolutional neural networks and vision transformer”, Complex Intell. Syst. 11 (2025) 1. https://doi.org/10.1007/s40747-024-01764-x.

[33] E. T. Baek, “Attention score-based multi-vision transformer technique for plant disease classification”, Sensors 25 (2025) 1. https://doi.org/10.3390/s25010270.

[34] “rice-leaf” Accessed: May 05, 2025. [Online]. Available: bahribahri/riceleaf.

[35] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, “An image is worth 16×16 words: transformers for image recognition at scale”, in ICLR 2021 - 9th International Conference on Learning Representations, 2021, p. 1. ArXiv. [Online]. https://doi.org/10.48550/arXiv.2010.11929.

Published

2025-11-01

How to Cite

Efficient-ViT B0Net: A high-performance light weight transformer for rice leaf disease recognition and classification. (2025). Journal of the Nigerian Society of Physical Sciences, 7(4), 2940. https://doi.org/10.46481/jnsps.2025.2940

Issue

Volume 7, Issue 4, November 2025

Section

Computer Science
Copyright & Licensing

Copyright (c) 2025 Santosh Kumar Upadhyay, Rajesh Prasad

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Efficient-ViT B0Net: A high-performance light weight transformer for rice leaf disease recognition and classification. (2025). Journal of the Nigerian Society of Physical Sciences, 7(4), 2940. https://doi.org/10.46481/jnsps.2025.2940

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