Open Access
Issue
EPJ Web Conf.
Volume 344, 2025
AI-Integrated Physics, Technology, and Engineering Conference (AIPTEC 2025)
Article Number 01020
Number of page(s) 8
Section AI-Integrated Physics, Technology, and Engineering
DOI https://doi.org/10.1051/epjconf/202534401020
Published online 22 December 2025
  1. C. Winarti, Widaningrum, M. W. Siti, S. Nurdi, Qanytah, A. S. Esty, W. S, Nutrient Composition of Indonesian Specialty Cereals: Rice, Corn, and Sorghum as Alternatives to Combat Malnutrition. Prev Nutr Food Sci. 28, 4, 471–482 (2023). https://doi.org/10.3746/pnf.2023.28.4.471 [Google Scholar]
  2. Badan Pusat Statistik (BPS - Statistics Indonesia), Maize Harvested Area and Production in Indonesia 2024. Badan Pusat Statistik (BPS - Statistics Indonesia). (2025). https://www.bps.go.id/en/publication/2025/08/15/2bb49a0af17ec89ac16971de/maize-harvested-area-and-production-in-indonesia-2024.html [Google Scholar]
  3. A. Guo, W. Huang, K. Wang, B. Qian, X. Cheng, Early Monitoring of Maize Northern Leaf Blight Using Vegetation Indices and Plant Traits from Multiangle Hyperspectral Data. Agriculture. 14, 8, 1311 (2024). https://doi.org/10.3390/agriculture14081311 [Google Scholar]
  4. Z. Ma, H. Hongyan, H. Yufei, Y. Yuan, S. Yangqiu, L. Bo, G. Zenggui, Evaluation of Maize Hybrids for Identifying Resistance to Northern Corn Leaf Blight in Northeast China. Plant Dis. 106, 3, 1003–1008 (2022). https://doi.org/10.1094/PDIS-09-21- 1914-RE [Google Scholar]
  5. A. Abera, E. Mendesil, J. Temesgen, W. Bayissa, Foliar disease-causing fungal isolates associated with maize: pathogen variability and species attributes in Southwestern Ethiopia. Discover Agriculture. 3, 1, 154 (2025). https://doi.org/10.1007/s44279-025-00275-8 [Google Scholar]
  6. T. Nyawose, R. C. Maswanganyi, P. Khumalo, A Review on the Detection of Plant Disease Using Machine Learning and Deep Learning Approaches. J Imaging. 11, 10, 326 (2025). https://doi.org/10.3390/jimaging11100326 [Google Scholar]
  7. M. Shoaib, A. Sadeghi-Niaraki, F. Ali, I. Hussain, S. Khalid, Leveraging deep learning for plant disease and pest detection: a comprehensive review and future directions. Front Plant Sci. 16 (2025). https://doi.org/10.3389/fpls.2025.1538163 [Google Scholar]
  8. P. Bachhal, V. Kukreja, S. Ahuja, U. K. Lilhore, S. Simaiya, A. Bijalwan, R. Alroobaea, S. Algarni, Maize leaf disease recognition using PRF-SVM integration: a breakthrough technique. Sci Rep. 14, 1, 10219 (2024). https://doi.org/10.1038/s41598- 024-60506-8 [Google Scholar]
  9. R. T. Wahyuningrum, A. Kusumaningsih, D. M. Yousi, Classification of Corn Leaf Diseases using Loss-Fused Convolutional Neural Network. IEEE ICIMTech. 696–701 (2023). https://doi.org/10.1109/ICIMTech59029.2023.102 77763 [Google Scholar]
  10. T. Zhao, Y. Xie, Y. Wang, J. Cheng, X. Guo, B. Hu, A Survey of Deep Learning on Mobile Devices: Applications, Optimizations, Challenges, and Research Opportunities. Proceedings of the IEEE. 110, 3, 334–354 (2022). https://doi.org/10.1109/JPROC.2022.3153408 [Google Scholar]
  11. M. Tan, Q. V. Le, EfficientNetV2: Smaller Models and Faster Training. (2021) [Google Scholar]
  12. R. S. Sandhya Devi, V. R. Vijay Kumar, P. Sivakumar, EfficientNetV2 Model for Plant Disease Classification and Pest Recognition. Computer Systems Science and Engineering. 45, 2, 2249–2263 (2023). https://doi.org/10.32604/csse.2023.032231 [Google Scholar]
  13. Y. Sun, L. Ning, B. Zhao, J. Yan, Tomato Leaf Disease Classification by Combining EfficientNetv2 and a Swin Transformer. Applied Sciences. 14, 17, 7472 (2024). https://doi.org/10.3390/app14177472 [Google Scholar]
  14. P. E. C. da Silva, J. Almeida, An Edge Computing- Based Solution for Real-Time Leaf Disease Classification using Thermal Imaging. (2024). https://doi.org/10.1109/LGRS.2024.3456637 [Google Scholar]
  15. A. Gao, A. Geng, Y. Song, L. Ren, Y. Zhang, X. Han, Detection of maize leaf diseases using improved MobileNet V3-small. International Journal of Agricultural and Biological Engineering. 16, 3, 225–232 (2023). https://doi.org/10.25165/j.ijabe.20231603.7799 [Google Scholar]
  16. M. Tariq, U. Ali, S. Abbas, S. Hassan, R. A. Naqvi, M. A. Khan, D. Jeong, Corn leaf disease: insightful diagnosis using VGG16 empowered by explainable AI. Front Plant Sci. 15 (2024). https://doi.org/10.3389/fpls.2024.1402835 [Google Scholar]
  17. K. Gopalan, S. Srinivasan, P. M. Singh, S. K. Mathivanan, U. Moorthy, Corn leaf disease diagnosis: enhancing accuracy with resnet152 and grad-cam for explainable AI. BMC Plant Biol. 25, 1, 440 (2025). https://doi.org/10.1186/s12870-025- 06386-0 [Google Scholar]
  18. A. Gao, A. J. Geng, Y. P. Song, L. L. Ren, Y. Zhang, X. Han, Detection of maize leaf diseases using improved MobileNet V3-small. International Journal of Agricultural and Biological Engineering. 16, 3, 225–232 (2023). https://doi.org/10.25165/j.ijabe.20231603.7799 [Google Scholar]
  19. Y. A. Abdillah, K. Kusrini, Corn Leaf Disease Classification Optimization Using Resnet50 Architecture Utilizing Bayesian Optimization. Journal of Electrical Engineering and Computer. 7, 1, 8–15 (2025). https://doi.org/10.33650/jeecom.v7i1.9809 [Google Scholar]
  20. R. Firmansyah, N. Nafi’iyah, Identifying Types of Corn Leaf Diseases with Deep Learning. Journal of Intelligent System and Computation. 6, 1, 18–23 (2024). https://doi.org/10.52985/insyst.v6i1.347 [Google Scholar]
  21. J. Gao, M. Ding, Q. Sun, J. Dong, H. Wang, Z. Ma, Classification of Southern Corn Rust Severity Based on Leaf-Level Hyperspectral Data Collected under Solar Illumination. Remote Sens (Basel). 14, 11 (2022). https://doi.org/10.3390/rs14112551 [Google Scholar]
  22. G. T. Askale, A. B. Yibel, B. M. Taye, G. D. Wubneh, Mobile based deep CNN model for maize leaf disease detection and classification. Plant Methods. 21, 1 (2025). https://doi.org/10.1186/s13007-025-01386-5 [Google Scholar]
  23. S. P. Mohanty, D. P. Hughes, M. Salathé, Using deep learning for image-based plant disease detection. Front Plant Sci. 7, September, 1–10 (2016). https://doi.org/10.3389/fpls.2016.01419 [Google Scholar]
  24. K. Gencer, A Comparative Analysis Of EfficientNetB0 And EfficientNetv2 Variants For MRI. 9, 1, 1–7 (2025) [Google Scholar]
  25. A. A. Abd El-Aziz, M. A. Mahmood, S. Abd El-Ghany, A Robust EfficientNetV2-S Classifier for Predicting Acute Lymphoblastic Leukemia Based on Cross Validation. Symmetry (Basel). 17, 1 (2025). https://doi.org/10.3390/sym17010024 [Google Scholar]
  26. H. Zhao, X. Zhang, Y. Gaa, L. Wang, L. Xiao, S. Liu, B. Huang, Z. Li, Diagnostic performance of EfficientNetV2-S method for staging liver fibrosis based on multiparametric MRI. Heliyon. 10, 15, e35115 (2024). https://doi.org/10.1016/j.heliyon.2024.e35115 [Google Scholar]
  27. I. Kandel, M. Castelli, Transfer learning with convolutional neural networks for diabetic retinopathy image classification. Applied Sciences (Switzerland). 10, 6 (2020). https://doi.org/10.3390/app10062021 [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.