References
- Chen J, Chen J, Zhang D, Sun Y, Nanehkaran YA, Using deep transfer learning for image-based plant disease identification, Computers and Electronics in Agriculture, Vol. 173, Article 105393, June 2020.
- Xie X, Ma Y, Liu B, He J, Li S, Wang H, A deep-learning-based real-time detector for grape leaf diseases using improved convolutional neural networks, Frontiers in Plant Science, Vol. 11, Article 751, May 2020.
- Ramesh S et al., Plant disease detection using machine learning, Proceedings of the International Conference on Design Innovations for 3Cs, pp. 1–6, April 2018.
- Ahmed AA, Reddy GH, A mobile-based system for detecting plant leaf diseases using deep learning, AgriEngineering, Vol. 3, No. 3, pp. 478–493, September 2021.
- Chollet F, Deep learning with Python, 2nd ed., Packt Publishing, Birmingham, January 2021.
- Iftikhar M, Kandhro IA, Kausar N, Kehar A, Uddin M, Dandoush A, Plant disease management: A fine-tuned enhanced CNN approach with mobile app integration, Artificial Intelligence Review, Vol. 57, No. 7, July 2024.
- Zhao T et al., A survey of deep learning on mobile devices: Applications, optimizations, and challenges, Proceedings of the IEEE, Vol. 110, No. 3, pp. 334–354, March 2022.
- Bhadana D, Purandhar N, An enhanced deep learning framework integrating Vision Transformers–CNN for accurate seed-borne disease prediction in crops, International Journal of Business Management and Economic Review, Vol. 4, No. 2, pp. 260, 2021.
- Wang R et al., A real-time object detector for autonomous vehicles based on YOLOv4, Computational Intelligence and Neuroscience, Vol. 2021, Article 9218137, January 2021.
- Ketabforoosh KAME, Parfitt J, Nikan S, Pearce JM, From blender to farm: Transforming controlled environment agriculture with synthetic data and SwinUNet, PLOS ONE, Vol. 20, No. 4, Article e0322189, April 2025.
- Lorig F, Timm IJ, Simulation-based data acquisition, Transactions on Computational Science and Computational Intelligence, pp. 1–15, June 2020.
- Sokolova M, Lapalme G, A systematic analysis of performance measures for classification tasks, Information Processing & Management, Vol. 45, No. 4, pp. 427–437, July 2009.
- Sandler M, Howard A, Zhu M, Zhmoginov A, Chen LC, MobileNetV2: Inverted residuals and linear bottlenecks, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510–4520, June 2018.
- Mishra S, Sachan R, Rajpal D, Deep convolutional neural network-based detection system for real-time corn plant disease recognition, Procedia Computer Science, Vol. 167, pp. 2003–2010, April 2020.
- Sharma A, Jain A, Gupta P, Chowdary V, Machine learning applications for precision agriculture: A comprehensive review, IEEE Access, Vol. 9, pp. 4843–4873, January 2021.
- Saranya T, Deisy C, Sridevi S, Anbananthen KSM, A comparative study of deep learning and Internet of Things for precision agriculture, Engineering Applications of Artificial Intelligence, Vol. 122, Article 106034, August 2023.
- Ahmad A, Saraswat D, El Gamal A, A survey on using deep learning techniques for plant disease diagnosis, Smart Agricultural Technology, Vol. 3, Article 100083, February 2023.
- Pattnaik G, Shrivastava VK, Parvathi K, Transfer learning-based framework for classification of pest in tomato plants, Applied Artificial Intelligence, Vol. 34, pp. 1–13, May 2020.
- Ferentinos KP, Deep learning models for plant disease detection and diagnosis, Computers and Electronics in Agriculture, Vol. 145, pp. 311–318, February 2018.
- Termritthikun C, Muneesawang P, NU-LiteNet: Mobile landmark recognition using convolutional neural networks, ECTI Transactions on Computer and Information Technology, Vol. 13, No. 1, pp. 21–28, January 2019.
- Zhou Y, Wen S, Wang D, Meng J, Mu J, Irampaye R, MobileYOLO: Real-time object detection algorithm in autonomous driving scenarios, Sensors, Vol. 22, No. 9, Article 3349, May 2022.
- Mirjalili S, Python machine learning, 3rd ed., Packt Publishing, Birmingham, January 2019.
- Kowshik B, Savitha V, Madhav MN, Karpagam G, Sangeetha K, Plant disease detection using deep learning, International Research Journal on Advanced Science Hub, Vol. 3, Special Issue, pp. 30–33, March 2021.
- Chen Y et al., Mobile-Former: Bridging MobileNet and transformer, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022.
- Dwivedi R, Dey S, Chakraborty C, Tiwari S, Grape disease detection network based on multi-task learning and attention features, IEEE Sensors Journal, 2021.
- Harakannanavar SS, Rudagi JM, Puranikmath VI, Siddiqui A, Pramodhini R, Plant leaf disease detection using computer vision and machine learning algorithms, Global Transitions Proceedings, 2022.
- Agarwal M, Bohat VK, Ansari MD, Sinha A, Gupta SK, Garg D, A convolution neural network-based approach to detect disease in corn crop, Proceedings of the IEEE Conference, 2019.
- Pan C, Lou Q, Niemier M, Hu S, Naeemi A, Energy-efficient convolutional neural network based on cellular neural network using beyond-CMOS technologies, IEEE Journal on Exploratory Solid-State Computational Devices and Circuits, Vol. 5, No. 2, pp. 85–93, December 2019.
- Mehta S, Rastegari M, Separable self-attention for mobile vision transformers, arXiv Preprint, June 2022.
- Naidu G, Zuva T, Sibanda EM, A review of evaluation metrics in machine learning algorithms, Lecture Notes in Networks and Systems, Vol. 724, pp. 15–25, 2023.
- Shruthi U, Nagaveni V, Raghavendra BK, A review on machine learning classification techniques for plant disease detection, Proceedings of the IEEE Conference, 2019.
- Waheed A, Goyal M, Gupta D, Khanna A, Hassanien AE, Pandey HM, An optimized dense convolutional neural network model for disease recognition in corn leaf, Computers and Electronics in Agriculture, Vol. 175, Article 105456, July 2020.
- Padilla DA, Pajes RAI, De Guzman JT, Detection of corn leaf diseases using convolutional neural network with OpenMP implementation, Proceedings of the IEEE Conference, 2020.
- Rashid R, Aslam W, Aziz R, Aldehim G, An early and smart detection of corn plant leaf diseases using IoT and deep learning multi-models, IEEE Access, Vol. 12, pp. 23149–23162, February 2024.
- Chen J, Chen J, Zhang D, Sun Y, Nanehkaran YA, Using deep transfer learning for image-based plant disease identification, Computers and Electronics in Agriculture, Vol. 173, Article 105393, June 2020.
- Ahmed AA, Reddy GH, A mobile-based system for detecting plant leaf diseases using deep learning, AgriEngineering, Vol. 3, No. 3, pp. 478–493, September 2021.
- Iftikhar M, Kandhro IA, Kausar N, Kehar A, Uddin M, Dandoush A, Plant disease management: A fine-tuned enhanced CNN approach with mobile app integration, Artificial Intelligence Review, Vol. 57, No. 7, Article 184, July 2024.
- Umathe S, Daigavane P, Daigavane M, Improving fault classification accuracy using wavelet transform and random forest with STATCOM integration, EAI Endorsed Transactions on Energy Web, Vol. 12, Article eX, 2024.
DOI: https://doi.org/10.2478/ijssis-2026-0047 | Journal eISSN: 1178-5608
Language: English
Submitted on: Jan 2, 2026
Published on: Jul 15, 2026
Published by: Macquarie University, Australia
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
Keywords:
Related subjects:
© 2026 Qi Jing, published by Macquarie University, Australia
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.