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Generalized framework using Federated Learning for tomato disease classification over unbalanced dataset

Published: 20 August 2023 Publication History

Abstract

Each cuisine required tomato in their kitchen for various food items and this makes tomato most popular crop worldwide and India is in second rank in terms production of tomato. Now a days, production of tomato goes down because of various diseases and to treat these diseases farmer needs to have extensive prior knowledge about the pathogen and along with various factor which promote the disease in the tomato. Due to lack of knowledge, the disease spreads rapidly and destroys all crops. To fill this gap, deep learning (DL) has been playing an important role, and there is much research on DL, how it can be used in medical industry and the agriculture industry for the identification of disease using images. There is a limitation for DL model that it does not work well with small dataset and huge amount of samples are required to train the model. Moreover, the data are not shared openly for security or for any other reason. Therefore, to overcome this challenge a Federated Learning (FL) based approach has been presented in the article. In FL, a deep learning model is shared with organizations which having the data and train the model. After training, the model information is shared with a centralized server which designs a generalized model. After getting the generalized model, it is shared with all other sites. The process is repeated until a generalized model is not designed and well-suited with all the sites. In our study, we tested our model on a tomato leaf disease data set using FL methodology with 10 clients and achieved the best precision with 88. 01%.

References

[1]
Mohit Agarwal, Suneet Kr Gupta, and KK Biswas. 2019. Grape disease identification using convolution neural network. In 2019 23rd International Computer Science and Engineering Conference (ICSEC). IEEE, 224–229.
[2]
Mohit Agarwal, Suneet Kumar Gupta, Deepak Garg, and Mohammad Monirujjaman Khan. 2022. A partcle swarm optimization based approach for filter pruning in convolution neural network for tomato leaf disease classification. In Advanced Computing: 11th International Conference, IACC 2021, Msida, Malta, December 18–19, 2021, Revised Selected Papers. Springer, 646–659.
[3]
Mohit Agarwal, Rohit Kr Kaliyar, and Suneet Kr Gupta. 2022. Differential Evolution based compression of CNN for Apple fruit disease classification. In 2022 International Conference on Inventive Computation Technologies (ICICT). IEEE, 76–82.
[4]
Mohit Agarwal, Abhishek Singh, Siddhartha Arjaria, Amit Sinha, and Suneet Gupta. 2020. ToLeD: Tomato leaf disease detection using convolution neural network. Procedia Computer Science 167 (2020), 293–301. ISBN: 1877-0509 Publisher: Elsevier.
[5]
Tsutomu Arie, Hideki Takahashi, Motoichiro Kodama, and Tohru Teraoka. 2007. Tomato as a model plant for plant-pathogen interactions. Plant Biotechnology 24, 1 (2007), 135–147.
[6]
Mohammed Brahimi, Kamel Boukhalfa, and Abdelouahab Moussaoui. 2017. Deep Learning for Tomato Diseases: Classification and Symptoms Visualization. Applied Artificial Intelligence 31, 4 (April 2017), 299–315. https://doi.org/10.1080/08839514.2017.1315516 Publisher: Taylor & Francis _eprint: https://doi.org/10.1080/08839514.2017.1315516.
[7]
Mohammed Brahimi, Kamel Boukhalfa, and Abdelouahab Moussaoui. 2017. Deep Learning for Tomato Diseases: Classification and Symptoms Visualization. Applied Artificial Intelligence 31, 4 (April 2017), 299–315. https://doi.org/10.1080/08839514.2017.1315516 Publisher: Taylor & Francis _eprint: https://doi.org/10.1080/08839514.2017.1315516.
[8]
G Geetharamani and Arun Pandian. 2019. Identification of plant leaf diseases using a nine-layer deep convolutional neural network. Computers & Electrical Engineering 76 (2019), 323–338.
[9]
David P. Hughes and Marcel Salathe. 2016. An open access repository of images on plant health to enable the development of mobile disease diagnostics. https://doi.org/10.48550/arXiv.1511.08060 arXiv:1511.08060 [cs].
[10]
Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, and Trevor Darrell. 2014. Caffe: Convolutional Architecture for Fast Feature Embedding. In Proceedings of the 22nd ACM international conference on Multimedia(MM ’14). Association for Computing Machinery, New York, NY, USA, 675–678. https://doi.org/10.1145/2647868.2654889
[11]
Yusuke Kawasaki, Hiroyuki Uga, Satoshi Kagiwada, and Hitoshi Iyatomi. 2015. Basic study of automated diagnosis of viral plant diseases using convolutional neural networks. In Advances in Visual Computing: 11th International Symposium, ISVC 2015, Las Vegas, NV, USA, December 14-16, 2015, Proceedings, Part II 11. Springer, 638–645.
[12]
Sharada P. Mohanty, David P. Hughes, and Marcel Salathé. 2016. Using deep learning for image-based plant disease detection. Frontiers in plant science 7 (2016), 1419. ISBN: 1664-462X Publisher: Frontiers Media SA.
[13]
Robin Sharma. 2021. Artificial Intelligence in Agriculture: A Review. In 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS). 937–942. https://doi.org/10.1109/ICICCS51141.2021.9432187
[14]
Vipin Kumar Singh, Amit Kishore Singh, and Ajay Kumar. 2017. Disease management of tomato through PGPB: current trends and future perspective. 3 Biotech 7, 4 (July 2017), 255. https://doi.org/10.1007/s13205-017-0896-1
[15]
Wenxue Tan, Chunjiang Zhao, and Huarui Wu. 2016. Intelligent alerting for fruit-melon lesion image based on momentum deep learning. Multimedia Tools and Applications 75, 24 (Dec. 2016), 16741–16761. https://doi.org/10.1007/s11042-015-2940-7
[16]
Alarsh Tiwari, Swapnil Panwala, Akshita Mehta, Naman Bansal, Mohit Agarwal, Rahul Mishra, and Suneet Gupta. 2021. CDID: Cherry Disease Identification Using Deep Convolutional Neural Network. In Proceedings of International Conference on Innovations in Information and Communication Technologies: ICI2CT 2020. Springer, 123–131.
[17]
Jingxian Wang, Lei Chen, Jian Zhang, Yuan Yuan, Miao Li, and WeiHui Zeng. 2018. CNN transfer learning for automatic image-based classification of crop disease. In Image and Graphics Technologies and Applications: 13th Conference on Image and Graphics Technologies and Applications, IGTA 2018, Beijing, China, April 8–10, 2018, Revised Selected Papers 13. Springer, 319–329.

Cited By

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  • (2024)Intelligent leaf disease diagnosis: image algorithms using Swin Transformer and federated learningThe Visual Computer10.1007/s00371-024-03692-wOnline publication date: 7-Nov-2024
  • (2023)Compressed Deep Learning and Transfer Learning Model for Detecting Brain Tumour2023 3rd International Conference on Innovative Sustainable Computational Technologies (CISCT)10.1109/CISCT57197.2023.10351273(1-6)Online publication date: 8-Sep-2023

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          cover image ACM Other conferences
          ICCTA '23: Proceedings of the 2023 9th International Conference on Computer Technology Applications
          May 2023
          270 pages
          ISBN:9781450399579
          DOI:10.1145/3605423
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          Published: 20 August 2023

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          1. AlexNet.
          2. Federated learning
          3. Image segmentation

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          View all
          • (2024)Intelligent leaf disease diagnosis: image algorithms using Swin Transformer and federated learningThe Visual Computer10.1007/s00371-024-03692-wOnline publication date: 7-Nov-2024
          • (2023)Compressed Deep Learning and Transfer Learning Model for Detecting Brain Tumour2023 3rd International Conference on Innovative Sustainable Computational Technologies (CISCT)10.1109/CISCT57197.2023.10351273(1-6)Online publication date: 8-Sep-2023

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