AI-driven plant disease detection with tailored convolutional neural network

Sk Mahmudul Hassan; Keshab Nath; Michal Jasinski; Arnab Kumar Maji

doi:10.1080/0954898x.2025.2537680

journal article Jul 31, 2025

AI-driven plant disease detection with tailored convolutional neural network

Sk Mahmudul Hassan Keshab Nath Michal Jasinski

Arnab Kumar Maji

Network: Computation in Neural Systems pp. 1-26 · Informa UK Limited

View at Publisher Save 10.1080/0954898x.2025.2537680

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References

46

[1]

10.1155/2020/8812019

[2]

10.1007/s00500-021-06176-4

[3]

10.1016/j.ecoinf.2020.101182

[4]

Random forest in remote sensing: A review of applications and future directions

Mariana Belgiu, Lucian Drăguț

ISPRS Journal of Photogrammetry and Remote Sensing 10.1016/j.isprsjprs.2016.01.011

[5]

A deep learning based approach for automated plant disease classification using vision transformer

Yasamin Borhani, Javad Khoramdel, Esmaeil Najafi

Scientific Reports 10.1038/s41598-022-15163-0

[6]

10.1109/tase.2020.3041499

[7]

Using deep transfer learning for image-based plant disease identification

Junde Chen, Jinxiu Chen, Defu Zhang et al.

Computers and Electronics in Agriculture 10.1016/j.compag.2020.105393

[8]

10.1016/j.asoc.2021.107901

[9]

10.1007/978-3-319-91086-4_10

[10]

10.1016/j.eswa.2023.120411

[11]

Deep learning models for plant disease detection and diagnosis

Konstantinos P. Ferentinos

Computers and Electronics in Agriculture 10.1016/j.compag.2018.01.009

[12]

10.1016/j.compeleceng.2019.04.011

[13]

Towards automatic field plant disease recognition

Penghui Gui, Wenjie Dang, Feiyu Zhu et al.

Computers and Electronics in Agriculture 10.1016/j.compag.2021.106523

[14]

Plant leaf disease detection using computer vision and machine learning algorithms

Sunil S. Harakannanavar, Jayashri M. Rudagi, Veena I Puranikmath et al.

Global Transitions Proceedings 10.1016/j.gltp.2022.03.016

[15]

He K, Zhang X, Ren S, Sun J. 2016. Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition p. 770–778. doi:10.1109/cvpr.2016.90.

[16]

10.1016/j.suscom.2019.100353

[17]

Huang G, Liu Z, Van Der Maaten L, Weinberger KQ. 2017. Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, IEEE, Honolulu, HI, USA (p. 4700–4708). doi:10.1109/cvpr.2017.243.

[18]

10.1080/19475705.2017.1407368

[19]

ImageNet classification with deep convolutional neural networks

Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton

Communications of the ACM 10.1145/3065386

[20]

10.1007/s11042-023-17610-0

[21]

10.1016/j.compag.2020.105803

[22]

10.1016/j.chaos.2004.11.095

[23]

10.1016/j.jksuci.2022.03.006

[24]

Evolutionary Algorithms for Constrained Parameter Optimization Problems

Zbigniew Michalewicz, Marc Schoenauer

Evolutionary Computation 10.1162/evco.1996.4.1.1

[25]

Using Deep Learning for Image-Based Plant Disease Detection

Sharada P. Mohanty, David P. Hughes, Marcel Salathé

Frontiers in Plant Science 10.3389/fpls.2016.01419

[26]

10.7717/peerj-cs.352

[27]

Panchal P, Raman VC, Mantri S. 2019. Plant diseases detection and classification using ma- chine learning models. In 2019 4th international conference on computational systems and information technology for sustainable solution (CSITSS). IEEE. p. 1–6. doi:10.1109/csitss47250.2019.9031029.

[28]

10.1016/j.compag.2018.04.002

[29]

10.3389/fpls.2019.00272

[30]

10.1016/j.aiia.2023.11.001

[31]

10.17632/fwcj7stb8r.1

[32]

Particle Swarm Optimization: A Comprehensive Survey

Tareq M. Shami, Ayman A. El-Saleh, Mohammed Alswaitti et al.

IEEE Access 10.1109/access.2022.3142859

[33]

Simonyan K, Zisserman A. 2014. Very deep convolutional networks for large-scale image recognition. arxiv Prepr arXiv: 14091556.

[34]

10.1109/access.2019.2907383

[35]

10.1016/j.engappai.2023.105836

[36]

10.1016/j.compag.2021.106379

[37]

Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Novich A R. 2015. Going deeper with convolutions. Proceedings of the IEEE conference on computer vision and pattern recognition IEEE, Boston, MA, USA. 1–9. doi:10.1109/cvpr.2015.7298594.

[38]

Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. 2016. Rethinking the inception architec- ture for computer vision. Proceedings of the IEEE conference on computer vision and pattern recognition ,IEEE, Las Vegas, NV, USA. 2818–2826. doi:10.1109/cvpr.2016.308.

[39]

10.1016/j.ecoinf.2023.102245

[40]

A comparative study of fine-tuning deep learning models for plant disease identification

Edna Chebet Too, Li Yujian, Sam Njuki et al.

Computers and Electronics in Agriculture 10.1016/j.compag.2018.03.032

[41]

10.1007/s12652-021-03289-4

[42]

10.1016/j.iot.2022.100650

[43]

10.1016/j.iot.2022.100650

[44]

10.1007/s00530-022-01034-1

[45]

10.1016/j.compag.2020.105341

[46]

10.1016/j.compag.2021.106644

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Details

Published: Jul 31, 2025
Pages: 1-26

Authors

S

Sk Mahmudul Hassan

Manipal Academy of Higher Education

Bhattadev University

Wroclaw University of Science and Technology; Faculty of Electrical Engineering and Computer Science, VSB – Technical University of Ostrava, Ostrava, Czech Republic

A

Arnab Kumar Maji

North Eastern Hill University

Cite This Article

Sk Mahmudul Hassan, Keshab Nath, Michal Jasinski, et al. (2025). AI-driven plant disease detection with tailored convolutional neural network. Network: Computation in Neural Systems, 1-26. https://doi.org/10.1080/0954898x.2025.2537680

AI-driven plant disease detection with tailored convolutional neural network

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