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Lite-Agro: Exploring Light-Duty Computing Platforms for IoAT-Edge AI in Plant Disease Identification

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Internet of Things. Advances in Information and Communication Technology (IFIPIoT 2023)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 684))

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Abstract

The Lite-Agro study aims to deploy deep learning neural network models for pear disease identification through tree leaf image analysis on TinyML device. A case study on pear leaves is conducted with publicly available pear disease dataset. Quantitative comparisons are made between different datasets. Lite-Agro is a light-duty image computing detection solution that is tested for deployment on a microcontroller. The novelty of Lite-Agro, lies in the export of a lightweight TinyML, Tensorflow Lite model that is geared for low power applications on battery powered hardware. The goal is to find the best model that is custom selected for the application and achieves the highest accuracy. The study emphasizes finding a balance between size, accuracy and performance. In future iterations of the study, Lite-Agro is to be mounted on an unmanned aerial vehicle to be powered with solar panels. Modern low powered microcontroller devices are to be a staple implementation in Smart Villages.

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References

  1. Chatzidimopoulos, M., Pappas, A.: Epidemiology and control of Septoria pyricola in pear leaf and fruit. J. Plant Pathol. 98, 447–452 (2016). https://doi.org/10.4454/JPP.V98I3.020

    Article  Google Scholar 

  2. Chollet, F.: Xception: deep learning with depthwise separable convolutions (2017). https://doi.org/10.1109/cvpr.2017.195

  3. David, R., et al.: TensorFlow lite micro: embedded machine learning on TinyML systems. CoRR abs/2010.08678 (2020). https://arxiv.org/abs/2010.08678

  4. Espressif Systems: EspressIDF: IoT Development Framework. https://docs.espressif.com/projects/esp-idf/en/latest/esp32/get-started/linux-macos-setup.html. Accessed 8 May 2023

  5. Fenu, G., Malloci, F.M.: Classification of pear leaf diseases based on ensemble convolutional neural networks. AgriEngineering 5, 141–152 (2023). https://doi.org/10.3390/agriengineering5010009

  6. Fenu, G., Malloci, F.M.: DiaMOS plant: a dataset for diagnosis and monitoring plant disease. Agronomy 11, 2107 (2021). https://doi.org/10.3390/agronomy11112107

    Article  Google Scholar 

  7. Google Inc.: TensorFlow: large-scale machine learning on heterogeneous systems. https://www.tensorflow.org/. Accessed 8 May 2023

  8. Han, H., Siebert, J.: TinyML: a systematic review and synthesis of existing research (2022). https://doi.org/10.1109/icaiic54071.2022.9722636

  9. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778, June 2016. https://doi.org/10.1109/CVPR.2016.90

  10. Karar, M.E., Alotaibi, F., Al-Rasheed, A., Reyad, O.: A pilot study of smart agricultural irrigation using Unmanned Aerial Vehicles and IoT-Based Cloud System. Inf. Sci. Lett. 10, 131–140 (2021). https://doi.org/10.18576/isl/100115

  11. Lāce, B.: Gymnosporangium species - an important issue of plant protection. Proc. Latvian Acad. Sci. Sect. B. Nat. Exact Appl. Sci. 71, 95–102 (2017). https://doi.org/10.1515/prolas-2017-0017

    Article  Google Scholar 

  12. Mathworks Inc.: Pretrained deep neural networks. https://www.mathworks.com/help/deeplearning/ug/pretrained-convolutional-neural-networks.html. Accessed 8 May 2023

  13. Mitra, A., Mohanty, S.P., Kougianos, E.: A smart agriculture framework to automatically track the spread of plant diseases using mask region-based convolutional neural network. In: Proceedings of the 5th IFIP International Internet of Things Conference (IFIP-IoT), pp. 68–85 (2022). https://doi.org/10.1007/978-3-031-18872-5_5

  14. Mitra, A., Mohanty, S.P., Kougianos, E.: aGROdet: a novel framework for plant disease detection and leaf damage estimation. In: Proceedings of the 5th IFIP International Internet of Things Conference (IFIP-IoT), pp. 3–22 (2022). https://doi.org/10.1007/978-3-031-18872-5_1

  15. Mitra, A., Singhal, A., Mohanty, S.P., Kougianos, E., Ray, C.: eCrop: a novel framework for automatic crop damage estimation in smart agriculture. SN Comput. Sci. 3(4), 16 (2022). https://doi.org/10.1007/s42979-022-01216-8

  16. Mitra, A., et al.: Everything you wanted to know about smart agriculture. CoRR abs/2201.04754 (2022). https://arxiv.org/abs/2201.04754

  17. Rehman, Z.U., et al.: Recognizing apple leaf diseases using a novel parallel real-time processing framework based on mask RCNN and transfer learning: an application for smart agriculture. IET Image Process. 15, 2157–2168 (2021). https://doi.org/10.1049/ipr2.12183

  18. Russakovsky, O., et al.: ImageNet large scale visual recognition challenge (2015)

    Google Scholar 

  19. Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: MobileNetV2: inverted residuals and linear bottlenecks (2018). https://doi.org/10.1109/cvpr.2018.00474

  20. Schizas, N., Karras, A., Karras, C., Sioutas, S.: TinyML for ultra-low power AI and large scale IoT deployments: a systematic review. Future Internet 14, 363 (2022). https://doi.org/10.3390/fi14120363

    Article  Google Scholar 

  21. Silaparasetty, N.: Machine learning programming with TensorFlow 2.0 (2020). https://doi.org/10.1007/978-1-4842-5967-2_11

  22. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, 7–9 May 2015, Conference Track Proceedings (2015). http://arxiv.org/abs/1409.1556

  23. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision (2016). https://doi.org/10.1109/cvpr.2016.308

  24. Tan, M., Le, Q.V.: EfficientNet: rethinking model scaling for convolutional neural networks. In: Proceedings of the 36th International Conference on Machine Learning, pp. 6105–6114 (2019)

    Google Scholar 

  25. Thomidis, T., Katerinis, S.: Occurrence of a fruit spot disease of pear caused by Septoria pyricola in Tyrnavos Larissa, Northern Greece. Plant Dis. 98, 845–845 (2014). https://doi.org/10.1094/pdis-09-13-0960-pdn

    Article  Google Scholar 

  26. Udutalapally, V., Mohanty, S.P., Pallagani, V., Khandelwal, V.: sCrop: a novel device for sustainable automatic disease prediction, crop selection, and irrigation in Internet-of-Agro-Things for smart. Agriculture 21, 17525–17538 (2021). https://doi.org/10.1109/jsen.2020.3032438

    Article  Google Scholar 

  27. Warden, P., Situnayake, D.: TinyML: Machine Learning with TensorFlowLite on Arduino and Ultra Low Power Microcontrollers. O’Reilly Media, Inc., London (2019)

    Google Scholar 

  28. Yang, F., Li, F., Zhang, K., Zhang, W., Li, S.: Influencing factors analysis in pear disease recognition using deep learning. Peer-to-Peer Networking Appl. 14(3), 1816–1828 (2020). https://doi.org/10.1007/s12083-020-01041-x

    Article  Google Scholar 

  29. Zaman, F.: TFLite architecture (2020). https://doi.org/10.1007/978-1-4842-6666-3_4

  30. Ziaul Haque Zim, M.: TinyML: analysis of Xtensa LX6 microprocessor for neural network applications by ESP32 SoC. arXiv e-prints arXiv:2106.10652, June 2021. https://doi.org/10.48550/arXiv.2106.10652

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, University of North Texas, Denton, USA

    Catherine Dockendorf & Saraju P. Mohanty

  2. Nebraska Water Center, Institute of Agriculture and Natural Resource, University of Nebraska-Lincoln, Lincoln, USA

    Alakananda Mitra

  3. Department of Electrical Engineering, University of North Texas, Denton, USA

    Elias Kougianos

Authors
  1. Catherine Dockendorf
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  2. Alakananda Mitra
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  3. Saraju P. Mohanty
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  4. Elias Kougianos
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Corresponding author

Correspondence to Saraju P. Mohanty .

Editor information

Editors and Affiliations

  1. Khalifa University, Abu Dhabi, United Arab Emirates

    Deepak Puthal

  2. University of North Texas, Denton, TX, USA

    Saraju Mohanty

  3. University of Missouri at Kansas City, Kansas City, MO, USA

    Baek-Young Choi

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Dockendorf, C., Mitra, A., Mohanty, S.P., Kougianos, E. (2024). Lite-Agro: Exploring Light-Duty Computing Platforms for IoAT-Edge AI in Plant Disease Identification. In: Puthal, D., Mohanty, S., Choi, BY. (eds) Internet of Things. Advances in Information and Communication Technology. IFIPIoT 2023. IFIP Advances in Information and Communication Technology, vol 684. Springer, Cham. https://doi.org/10.1007/978-3-031-45882-8_25

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  • DOI: https://doi.org/10.1007/978-3-031-45882-8_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-45881-1

  • Online ISBN: 978-3-031-45882-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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