EMBEDDED RESIDUAL NEURAL NETWORKS FOR REAL-WORLD PLANT DISEASE IDENTIFICATION IN DIGITAL AGRICULTURE
DOI:
https://doi.org/10.15282/ijsecs.10.2.2024.12.0130Keywords:
Plant disease identification, Embedded platform, ResNet, Batch size, NVIDIA Jetson Orin, Real-time processingAbstract
This study addresses the challenge of real-time plant disease identification on resource-constrained embedded platforms, a critical need for improving agricultural productivity. Using the NVIDIA Jetson Orin Developer Kit and the PlantVillage dataset, the research evaluates Residual Neural Networks (ResNets), focusing on ResNet-50, ResNet-101, and ResNet-152. The study highlights the balance between model depth, batch size, accuracy, and computational efficiency. ResNet-101, optimized with a batch size of 64, achieved 90.62% accuracy and an average identification time of 17.6 milliseconds, emerging as the most effective configuration. These findings demonstrate the feasibility of deploying deep learning models on embedded devices and provide insights into optimizing architectures for real-time agricultural applications.
References
[1] R. Gajjar, N. Gajjar, V. J. Thakor, N. P. Patel, and S. Ruparelia, “Real-time detection and identification of plant leaf diseases using convolutional neural networks on an embedded platform,” Vis Comput, pp. 1–16, 2022.
[2] A. M. Roy and J. Bhaduri, “A deep learning enabled multi-class plant disease detection model based on computer vision,” Ai, vol. 2, no. 3, pp. 413–428, 2021.
[3] X. Sun, G. Li, P. Qu, X. Xie, X. Pan, and W. Zhang, “Research on plant disease identification based on CNN,” Cognitive Robotics, vol. 2, pp. 155–163, 2022.
[4] M. R. Howlader, U. Habiba, R. H. Faisal, and M. M. Rahman, “Automatic recognition of guava leaf diseases using deep convolution neural network,” in 2019 international conference on electrical, computer and communication engineering (ECCE), IEEE, 2019, pp. 1–5.
[5] I. Ahmed and P. K. Yadav, “Plant disease detection using machine learning approaches,” Expert Syst, vol. 40, no. 5, p. e13136, 2023.
[6] S. Patidar, A. Pandey, B. A. Shirish, and A. Sriram, “Rice plant disease detection and classification using deep residual learning,” in Machine Learning, Image Processing, Network Security and Data Sciences: Second International Conference, MIND 2020, Silchar, India, July 30-31, 2020, Proceedings, Part I 2, Springer, 2020, pp. 278–293.
[7] S. Ashok, G. Kishore, V. Rajesh, S. Suchitra, S. G. G. Sophia, and B. Pavithra, “Tomato leaf disease detection using deep learning techniques,” in 2020 5th International Conference on Communication and Electronics Systems (ICCES), IEEE, 2020, pp. 979–983.
[8] P. Alagumariappan, N. J. Dewan, G. N. Muthukrishnan, B. K. B. Raju, R. A. A. Bilal, and V. Sankaran, “Intelligent plant disease identification system using Machine Learning,” Engineering Proceedings, vol. 2, no. 1, p. 49, 2020.
[9] S. Sanga, V. Mero, D. Machuve, and D. Mwanganda, “Mobile-based deep learning models for banana diseases detection,” arXiv preprint arXiv:2004.03718, 2020.
[10] A. Adedoja, P. A. Owolawi, and T. Mapayi, “Deep learning based on nasnet for plant disease recognition using leave images,” in 2019 international conference on advances in big data, computing and data communication systems (icABCD), IEEE, 2019, pp. 1–5.
[11] D. Shah, V. Trivedi, V. Sheth, A. Shah, and U. Chauhan, “ResTS: Residual deep interpretable architecture for plant disease detection,” Information Processing in Agriculture, vol. 9, no. 2, pp. 212–223, 2022.
[12] A. Pandian J, K. K, N. R. Rajalakshmi, and G. Arulkumaran, “An improved deep residual convolutional neural network for plant leaf disease detection,” Comput Intell Neurosci, vol. 2022, no. 1, p. 5102290, 2022.
[13] S. P. Mohanty, D. P. Hughes, and M. Salathé, “Using deep learning for image-based plant disease detection,” Front Plant Sci, vol. 7, p. 1419, 2016.
[14] S. Sladojevic, M. Arsenovic, A. Anderla, D. Culibrk, and D. Stefanovic, “Deep neural networks based recognition of plant diseases by leaf image classification,” Comput Intell Neurosci, vol. 2016, no. 1, p. 3289801, 2016.
[15] A. Ramcharan, K. Baranowski, P. McCloskey, B. Ahmed, J. Legg, and D. P. Hughes, “Deep learning for image-based cassava disease detection,” Front Plant Sci, vol. 8, p. 1852, 2017.
[16] C. Zhang et al., “A new CNN-Bayesian model for extracting improved winter wheat spatial distribution from GF-2 imagery,” Remote Sens (Basel), vol. 11, no. 6, p. 619, 2019.
[17] V. Gonzalez-Huitron, J. A. León-Borges, A. E. Rodriguez-Mata, L. E. Amabilis-Sosa, B. Ramírez-Pereda, and H. Rodriguez, “Disease detection in tomato leaves via CNN with lightweight architectures implemented in Raspberry Pi 4,” Comput Electron Agric, vol. 181, p. 105951, 2021.
[18] D. S. Joseph, P. M. Pawar, and R. Pramanik, “Intelligent plant disease diagnosis using convolutional neural network: a review,” Multimed Tools Appl, vol. 82, no. 14, pp. 21415–21481, 2023.
[19] H. Alaeddine and M. Jihene, “Deep residual network in network,” Comput Intell Neurosci, vol. 2021, no. 1, p. 6659083, 2021.
[20] D. Udekwe, O. Ajayi, O. Ubadike, K. Ter, and E. Okafor, “Comparing actor-critic deep reinforcement learning controllers for enhanced performance on a ball-and-plate system,” Expert Syst Appl, vol. 245, p. 123055, 2024.
[21] E. Okafor, D. Udekwe, Y. Ibrahim, M. Bashir Mu’azu, and E. G. Okafor, “Heuristic and deep reinforcement learning-based PID control of trajectory tracking in a ball-and-plate system,” Journal of Information and Telecommunication, vol. 5, no. 2, pp. 179–196, 2021.
[22] A. O. Adetifa, P. P. Okonkwo, B. B. Muhammed, and D. A. Udekwe, “Deep reinforcement learning for aircraft longitudinal control augmentation system,” Nigerian Journal of Technology, vol. 42, no. 1, pp. 144–151, 2023.
[23] E. Okafor, D. Udekwe, O. C. Ubadike, E. Okafor, P. O. Jemitola, and M. T. Abba, “Photovoltaic System MPPT Evaluation Using Classical, Meta-Heuristics, and Reinforcement Learning-Based Controllers: A Comparative Study,” Journal of Southwest Jiaotong University, vol. 56, no. 3, 2021.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Daniel Udekwe

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