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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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
Oerke, E.C.: Crop losses to pests. J. Agric. Sci. 144(1), 31–43 (2006). https://doi.org/10.1017/S0021859605005708
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
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
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
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)
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)
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
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)
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)
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)
Hassanien, A.E., Darwish, A. (eds.). http://www.springer.com/series/11970
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
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
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
Kumar, S.: Automatic Grading of Potato Leaf Using Machine Learning & Computer Vision, pp. 1–11 (2022)
Leemans, V., Magein, H., Destain, M.-F.: Defects segmentation on ‘Golden Delicious’ apples by using colour machine vision (1998)
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
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
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
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
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
Amara, J., Bouaziz, B., Algergawy, A.: A Deep Learning-based Approach for Banana Leaf Diseases Classification
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
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
Rashid, G.A., Imran Khan, J.: Potato Leaf Diseases Dataset(PLD) (2021). https://drive.google.com/drive/folders/1FpcQA66pEg0XR8y5uEzWU__REPpqSAPD
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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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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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