Deep Transformer-Based Visual Framework for Early Detection of Plant Leaf Pathologies

Abhishek Mathur; Shailendra Kumar Shrivastava

doi:10.15832/ankutbd.1738222

Deep Transformer-Based Visual Framework for Early Detection of Plant Leaf Pathologies

Abstract

Accurate and quick detection of plant leaf diseases is essential for precision agriculture to intervene promptly and boost crop yields. A new deep learning model called ResVNet has been introduced in this study. It combines the powerful local feature detection of ResNet152 with the global attention capabilities of Vision Transformer (ViT) and utilises Low-Rank Adaptation (LoRA) to accelerate fine-tuning. The PlantVillage dataset, which contains both healthy and diseased tomato samples, was used to train and test ResVNet. Experimental evaluation on the PlantVillage tomato dataset using stratified 5-fold cross-validation demonstrates that the proposed ResVNet model achieves a mean classification accuracy of 97.45%, along with superior macro-precision, macro-recall, and macro-F1 scores compared to existing deep learning architectures. The results of the confusion matrix and the ROC analysis validate its discriminatory power. The results highlight the potential of architectures strengthened with transformers in agricultural diagnostics. For real-time disease detection in the field, ResVNet is perfect for edge device deployment on drones and smartphones thanks to its high accuracy and adaptability. The application of Explainable AI (XAI) technologies for interpretability, integration with the Internet of Things (IoT), and multi-crop classification will all be explored in future studies. We will also look into model compression approaches so we can deploy efficiently in low-resource settings without sacrificing performance.

Keywords

References

Demilie W B (2024). Plant disease detection and classification techniques: a comparative study of the performances. Journal of Big Data, 11(1). https://doi.org/10.1186/s40537-023-00863-9
Gavhale M K R & Gawande P U (2014). An Overview of the Research on Plant Leaves Disease detection using Image Processing Techniques. IOSR Journal of Computer Engineering, 16(1): 10–16. https://doi.org/10.9790/0661-16151016
Hana B & Yassmina S (2024). Deep Learning-Based Detection of Apple Leaf Diseases using Image Analysis. 2024 1st International Conference on Innovative and Intelligent Information Technologies (IC3IT), 1–6. https://doi.org/10.1109/IC3IT63743.2024.10869435
Hanif M A, Khadimul Islam Zim M & Kaur H (2024). ResNet vs Inception-v3 vs SVM: A Comparative Study of Deep Learning Models for Image Classification of Plant Disease Detection. 2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), 2: 1–6. https://doi.org/10.1109/IATMSI60426.2024.10502832
J A, Eunice J, Popescu D E, Chowdary M K & Hemanth J (2022). Deep Learning-Based Leaf Disease Detection in Crops Using Images for Agricultural Applications. Agronomy 12(10). https://doi.org/10.3390/agronomy12102395 Conference
Joshi A, Badola A & Saini R (2024). Deep Learning Based Fruit Crop Disease Classification Using Plants Leaf Imagery. 2024 First International on Electronics, https://doi.org/10.1109/ICECSP61809.2024.10698492 Communication and Signal Processing (ICECSP), 1–6.
Jung M, Song J S, Shin A Y, Choi B, Go S, Kwon S Y, Park J, Park S G & Kim Y M (2023). Construction of deep learning-based disease detection model in plants. Scientific Reports, 13(1): 1–13. https://doi.org/10.1038/s41598-023-34549-2
Kaur A, Sharma R, Chattopadhyay S & Joshi K (2024). Cotton Leaf Disease Classification Using Fine-Tuned VGG16 Deep Learning Model. 2024 2nd World Conference on Communication & Computing (WCONF), 1–4. https://doi.org/10.1109/WCONF61366.2024.10692185

Khade V C & Patil S B (2024). International Journal of INTELLIGENT SYSTEMS AND APPLICATIONS IN ENGINEERING A Hybrid Deep Learning Approach for Crop Disease Severity Level Prediction. Original Research Paper International Journal of Intelligent Systems and Applications in Engineering IJISAE, 2024(9s): 215–224
Krishna M S, Machado P, Otuka R I, Yahaya S W, dos Santos F & Ihianle I K (2025). Plant Leaf Disease Detection Using Deep Learning: A Multi-Dataset Approach. J, 8(1). https://doi.org/10.3390/j8010004
Li L, Zhang S & Wang B (2021). Plant Disease Detection and Classification by Deep Learning—A Review. IEEE Access, 9, 56683–56698. https://doi.org/10.1109/ACCESS.2021.3069646
Li W, Zhu L & Liu J (2024). PL-DINO: An Improved Transformer-Based Method for Plant Leaf Disease Detection. Agriculture, 14(5). https://doi.org/10.3390/agriculture14050691
Meeradevi V R, Mundada M R, Sawkar S P, Bellad R S & Keerthi P S (2020). Design and Development of Efficient Techniques for Leaf Disease Detection using Deep Convolutional Neural Networks. 2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER), 153–158. https://doi.org/10.1109/DISCOVER50404.2020.9278067
Murthy T S, Krishnamoorthi K, Selvarani P, Srinivasulu R, Saranya G & Kausalya A K (2024). MDISL: Experimental Evaluation of Learning Based Potato Plant Disease Detection Using Modified Deep Image Segmentation Logic. 2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), 1–6. https://doi.org/10.1109/ICSES63760.2024.10910396
Nagpal J, Goel L & Shekhawat P S (2024). A hybrid deep learning neural network for early plant disease diagnosis using a real-world Wheat Barley vision dataset: challenges and solutions. International Journal of Data Science and Analytics, March. https://doi.org/10.1007/s41060-024-00578-x
Poornima S, Sripriya N, Alrasheedi A F, Askar S S & Abouhawwash M (2023). Hybrid Convolutional Neural Network for Plant Diseases Prediction. Intelligent Automation and Soft Computing, 36(2): 2393–2409. https://doi.org/10.32604/iasc.2023.024820
Saini A, Guleria K & Sharma S (2023). Multiclass Classification of Rice Leaf Disease Using Deep Learning Based Model. 2023 3rd Asian Conference on Innovation in Technology (ASIANCON), 1–6. https://doi.org/10.1109/ASIANCON58793.2023.10270229
Salman R D & Ali Y H (2025). Hybrid Model for Plant Disease Diagnosis: Transfer Learning with Dynamic Feature Selection. 10.Samundiswary P (2023). Wheat Disease Detection and Classification Using Resnet152. International Journal of Creative Research Thoughts, 11(5): 2320–2882
Shinde, R. P., Shah, S. B., Belani, R., & Nune, N. C. (2024). Hybrid Deep Learning Approach for Automated Plant Disease Detection in Precision Agriculture 7(12): 114–121
Thotad P N, Kallur S & Nandeppanavar A (2023). An Efficient Model for Plant Disease Detection in Agriculture Using Deep Learning Approaches. 2023 4th IEEE Global Conference for Advancement in Technology (GCAT), 1–6. https://doi.org/10.1109/GCAT59970.2023.10353343
Zhang X, Dong H, Gong L, Cheng X, Ge Z & Guo L (2023). Multiple paddy disease recognition methods based on deformable transformer attention mechanism in complex scenarios. International Journal of Computers and Applications 45(10): 660–672

Details

Primary Language

English

Subjects

Artificial Intelligence (Other), Plant Pathology, Genetically Modified Horticulture Plants

Journal Section

Research Article

Authors

Abhishek Mathur ^*
0009-0004-3167-4822
India

Shailendra Kumar Shrivastava This is me
0000-0003-4943-550X
India

Publication Date

July 28, 2026

Submission Date

July 9, 2025

Acceptance Date

March 3, 2026

Published in Issue

Year 2026 Volume: 32 Number: 3

DOI

https://doi.org/10.15832/ankutbd.1738222

IZ

https://izlik.org/JA77JK48MH

Cite

RIS / Bibtex

APA

Mathur, A., & Kumar Shrivastava, S. (2026). Deep Transformer-Based Visual Framework for Early Detection of Plant Leaf Pathologies. Journal of Agricultural Sciences, 32(3), 759-775. https://doi.org/10.15832/ankutbd.1738222

AMA

1.Mathur A, Kumar Shrivastava S. Deep Transformer-Based Visual Framework for Early Detection of Plant Leaf Pathologies. J Agr Sci-Tarim Bili. 2026;32(3):759-775. doi:10.15832/ankutbd.1738222

Chicago

Mathur, Abhishek, and Shailendra Kumar Shrivastava. 2026. “Deep Transformer-Based Visual Framework for Early Detection of Plant Leaf Pathologies”. Journal of Agricultural Sciences 32 (3): 759-75. https://doi.org/10.15832/ankutbd.1738222.

EndNote

Mathur A, Kumar Shrivastava S (July 1, 2026) Deep Transformer-Based Visual Framework for Early Detection of Plant Leaf Pathologies. Journal of Agricultural Sciences 32 3 759–775.

IEEE

[1]A. Mathur and S. Kumar Shrivastava, “Deep Transformer-Based Visual Framework for Early Detection of Plant Leaf Pathologies”, J Agr Sci-Tarim Bili, vol. 32, no. 3, pp. 759–775, July 2026, doi: 10.15832/ankutbd.1738222.

ISNAD

Mathur, Abhishek - Kumar Shrivastava, Shailendra. “Deep Transformer-Based Visual Framework for Early Detection of Plant Leaf Pathologies”. Journal of Agricultural Sciences 32/3 (July 1, 2026): 759-775. https://doi.org/10.15832/ankutbd.1738222.

JAMA

1.Mathur A, Kumar Shrivastava S. Deep Transformer-Based Visual Framework for Early Detection of Plant Leaf Pathologies. J Agr Sci-Tarim Bili. 2026;32:759–775.

MLA

Mathur, Abhishek, and Shailendra Kumar Shrivastava. “Deep Transformer-Based Visual Framework for Early Detection of Plant Leaf Pathologies”. Journal of Agricultural Sciences, vol. 32, no. 3, July 2026, pp. 759-75, doi:10.15832/ankutbd.1738222.

Vancouver

1.Abhishek Mathur, Shailendra Kumar Shrivastava. Deep Transformer-Based Visual Framework for Early Detection of Plant Leaf Pathologies. J Agr Sci-Tarim Bili. 2026 Jul. 1;32(3):759-75. doi:10.15832/ankutbd.1738222