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AgriScanNet-18: A Robust Multilayer CNN for Identification of Potato Plant Diseases

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Intelligent Systems and Applications (IntelliSys 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 823))

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Abstract

This paper presents a novel approach for the early detection of potato plant diseases using deep learning techniques. The proposed method, AgriScanNet-18, is a multilayer convolutional neural network (CNN) that uses image-based analysis to identify various plant diseases. By training and evaluating the model on a potato leaf disease dataset, we achieved high accuracy of 99.30% for training and 99.28% for testing. Additionally, we developed a web app that facilitates the diagnosis of potato plant diseases by easily uploading images of leaves. In comparison with state-of-the-art models such as, VGG16, ResNet50, and VGG19, AgriScanNet-18 demonstrated improved identification accuracy of 8.66%, 3.61%, and 7.45%. In addition, Potato plant diseases can be managed and controlled using this technology to increase crop production and profitability.

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References

  1. Iqbal, Z., Khan, M.A., Sharif, M., Shah, J.H., ur Rehman, M.H., Javed, K.: An automated detection and classification of citrus plant diseases using image processing techniques: a review. Comput. Electron. Agric.; Elsevier B.V. 153, 12–32 (2018). https://doi.org/10.1016/j.compag.2018.07.032

  2. Oerke, E.C.: Crop losses to pests. J. Agric. Sci. 144(1), 31–43 (2006). https://doi.org/10.1017/S0021859605005708

    Article  Google Scholar 

  3. Golhani, K., Balasundram, S.K., Vadamalai, G., Pradhan, B.: A review of neural networks in plant disease detection using hyperspectral data. In: Information Processing in Agriculture, vol. 5, no. 3. China Agricultural University, pp. 354–371 (2018). https://doi.org/10.1016/j.inpa.2018.05.002

  4. Chouhan, S.S., Kaul, A., Singh, U.P., Jain, S.: Bacterial foraging optimization based radial basis function neural network (BRBFNN) for identification and classification of plant leaf diseases: an automatic approach towards plant pathology. IEEE Access 6, 8852–8863 (2018). https://doi.org/10.1109/ACCESS.2018.2800685

    Article  Google Scholar 

  5. Singh, U.P., Chouhan, S.S., Jain, S., Jain, S.: Multilayer convolution neural network for the classification of mango leaves infected by anthracnose disease. IEEE Access 7, 43721–43729 (2019). https://doi.org/10.1109/ACCESS.2019.2907383

    Article  Google Scholar 

  6. Deepa, N.R., Nagarajan, N.: Kuan noise filter with Hough transformation based reweighted linear program boost classification for plant leaf disease detection. J. Ambient. Intell. Humaniz. Comput. 12, 5979–5992 (2021)

    Article  Google Scholar 

  7. Karthik, R., Hariharan, M., Anand, S., Mathikshara, P., Johnson, A., Menaka, R.: Attention embedded residual CNN for disease detection in tomato leaves. Appl. Soft Comput. 86, 105933 (2020)

    Article  Google Scholar 

  8. Aamir, M., et al.: An adoptive threshold-based multi-level deep convolutional neural network for glaucoma eye disease detection and classification. Diagnostics 10(8) 2020. https://doi.org/10.3390/diagnostics10080602

  9. Chen, S.W., et al.: Counting apples and oranges with deep learning: a data-driven approach. IEEE Robot. Autom. Lett. 2(2), 781–788 (2017)

    Article  Google Scholar 

  10. Dias, P.A., Tabb, A., Medeiros, H.: Multispecies fruit flower detection using a refined semantic segmentation network. IEEE Robot. Autom. Lett. 3(4), 3003–3010 (2018)

    Article  Google Scholar 

  11. Ubbens, J., Cieslak, M., Prusinkiewicz, P., Stavness, I.: The use of plant models in deep learning: an application to leaf counting in rosette plants. Plant Methods 14, 1–10 (2018)

    Article  Google Scholar 

  12. Hassanien, A.E., Darwish, A. (eds.). http://www.springer.com/series/11970

  13. Rozaqi, A.J., Sunyoto, A.: Identification of disease in potato leaves using Convolutional Neural Network (CNN) algorithm. In: 2020 3rd International Conference on Information and Communications Technology, ICOIACT 2020, pp. 72–76 (2020). https://doi.org/10.1109/ICOIACT50329.2020.9332037

  14. Sanjeev, K., Gupta, N.K., Jeberson, W.J., Paswan, S.: Early prediction of potato leaf diseases using ANN classifier. Orient. J. Comput. Sci. Technol. 13(0203), 129–134 (2021). https://doi.org/10.13005/ojcst13.0203.11

    Article  Google Scholar 

  15. Barman, U., Sahu, D., Barman, G.G., Das, J.: Comparative assessment of deep learning to detect the leaf diseases of potato based on data augmentation. In: 2020 International Conference on Computational Performance Evaluation ComPE 2020, pp. 682–687 (2020). https://doi.org/10.1109/ComPE49325.2020.9200015

  16. Kumar, S.: Automatic Grading of Potato Leaf Using Machine Learning & Computer Vision, pp. 1–11 (2022)

    Google Scholar 

  17. Leemans, V., Magein, H., Destain, M.-F.: Defects segmentation on ‘Golden Delicious’ apples by using colour machine vision (1998)

    Google Scholar 

  18. Islam, M.A., Rahman Shuvo, N., Shamsojjaman, M., Hasan, S., Hossain, S., Khatun, T.: An automated convolutional neural network based approach for paddy leaf disease detection. Int. J. Adv. Comput. Sci. Appl. 12(1), 280–288 (2021). https://doi.org/10.14569/IJACSA.2021.0120134

  19. Dubey, S.R., Jalal, A.S.: Detection and classification of apple fruit diseases using complete local binary patterns. In: Proceedings of the 2012 3rd International Conference on Computer and Communication Technology, ICCCT 2012, 2012, pp. 346–351. https://doi.org/10.1109/ICCCT.2012.76

  20. Ferentinos, K.P.: Deep learning models for plant disease detection and diagnosis. Comput. Electron. Agric. 145, 311–318 (2018). https://doi.org/10.1016/j.compag.2018.01.009

    Article  Google Scholar 

  21. Li, Y., Yang, J.: Few-shot cotton pest recognition and terminal realization. Comput. Electron. Agric. 169 (2020). https://doi.org/10.1016/j.compag.2020.105240

  22. Geetharamani, G., Pandian, A.: Identification of plant leaf diseases using a nine-layer deep convolutional neural network. Comput. Electr. Eng. 76, 323–338 (2019). https://doi.org/10.1016/j.compeleceng.2019.04.011

  23. Amara, J., Bouaziz, B., Algergawy, A.: A Deep Learning-based Approach for Banana Leaf Diseases Classification

    Google Scholar 

  24. Liu, B., Zhang, Y., He, D.J., Li, Y.: Identification of apple leaf diseases based on deep convolutional neural networks. Symmetry (Basel) 10(1) (2018). https://doi.org/10.3390/sym10010011

  25. Arnal Barbedo, J.G.: Plant disease identification from individual lesions and spots using deep learning. Biosyst. Eng. 180, 96–107 (2019). https://doi.org/10.1016/j.biosystemseng.2019.02.002

  26. Rashid, G.A., Imran Khan, J.: Potato Leaf Diseases Dataset(PLD) (2021). https://drive.google.com/drive/folders/1FpcQA66pEg0XR8y5uEzWU__REPpqSAPD

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Correspondence to Jalil Boudjadar .

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All authors have contributed equally to developing the concept, defining the system architecture, and identifying the key performance indicators to consider for assessing AgriScanNet-18. However, Author1 and Author2 conducted the implementation and the draft writing, whereas Author3 and Author4 provided expertise for validating the results and paper review.

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Authors have no conflicts of interest to declare relevant to this article's publication.

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Manzoor, S., Manzoor, S.H., Islam, S.u., Boudjadar, J. (2024). AgriScanNet-18: A Robust Multilayer CNN for Identification of Potato Plant Diseases. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2023. Lecture Notes in Networks and Systems, vol 823. Springer, Cham. https://doi.org/10.1007/978-3-031-47724-9_20

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