FOREX Press I. J. of Electrical & Electronics Research
Support Open Access
  • Home/
  • IJEER-110317

Research Article |

Disease Detection and Diagnosis of Agricultural Plant Leaf Using Machine Learning

Author(s): Aadhitya S V, Ashwin Hariharan R and Sriharipriya K C*

Published In : International Journal of Electrical and Electronics Research (IJEER)        Volume 11, Issue 3

Publisher : FOREX Publication

Published : 20 September 2023

e-ISSN : 2347-470X

Page(s) : 749-753

DOI: https://doi.org/10.37391/ijeer.110317

Abstract

Agriculture and allied activities still continue to be one of the major occupations in world. Various modern methods and inventions have been incorporated to make it more efficient and successful. One of the main problems the farmers are facing are plant diseases. This can affect the entire yield of a season, so to tackle that problem we are proposing a ResNet based Convolutional neural network model which can detect the various disease in plants in early stage itself. For this purpose, ‘New plant village’ dataset to train and test the model. The proposed Resnet based approach has achieved high accuracy in detecting diseases as well as suggesting a proper solution and possible causes for a plant disease.

Keywords: ResNet, CNN, Plant Disease, Accuracy.




Aadhitya S V, Department of Embedded Technology, School of Electronics Engineering, Vellore Institute of Technology, Vellore –32014, Tamil Nadu, India; Email: aadhitya.sv2022@vitstudent.ac.in

Ashwin Hariharan R, Department of Embedded Technology, School of Electronics Engineering, Vellore Institute of Technology, Vellore – 632014, Tamil Nadu, India; Email: ashwinhariharan.r2022@vitstudent.ac.in

Sriharipriya K C, Department of Embedded Technology, School of Electronics Engineering Vellore Institute of Technology, Vellore – 632014, Tamil Nadu, India; Email: sriharipriya.kc@vit.ac.in

    [1] Ahmad, T., Khan, I., Irshad, A., Ahmad, S., T. Soliman, A., Abid Gardezi, A., Shafiq, M., & Choi, J.-G. (2022). EfficientNet-based robust recognition of peach plant diseases in field images. Computers, Materials &Continua,71(1),2073–2089. https://doi.org/10.32604/cmc.2022.018961.
    [2] Jwo, D.-J., & Chiu, S.-F. (2022). Deep learning based automated detection of diseases from apple leaf images. Computers, Materials & Continua, 71(1), 1849–1866. https://doi.org/10.32604/cmc.2022.021875.
    [3] Kurmi, Y., & Gangwar, S. (2022). A leaf image localization based algorithm for different crops disease classification. Information Processing in Agriculture, 9(3), 456–474. https://doi.org/10.1016/j.inpa.2021.03.001.
    [4] Sunil, Jaidhar, & Patil, N. (2022). Cardamom plant disease detection approach using EfficientNetV2. IEEE Access: Practical Innovations, Open Solutions, 10, 789–804. https://doi.org/10.1109/access.2021.3138920.
    [5] Gadekallu, T. R., Rajput, D. S., Reddy, M. P. K., Lakshmanna, K., Bhattacharya, S., Singh, S., Jolfaei, A., & Alazab, M. (2021). A novel PCA–whale optimization-based deep neural network model for classification of tomato plant diseases using GPU. Journal of Real-Time Image Processing, 18(4), 1383–1396. https://doi.org/10.1007/s11554-020-00987-8.
    [6] Yang, R., Wu, Z., Fang, W., Zhang, H., Wang, W., Fu, L., Majeed, Y., Li, R., & Cui, Y. (2021). Detection of abnormal hydroponic lettuce leaves based on image processing and machine learning. Information Processing in Agriculture. https://doi.org/10.1016/j.inpa.2021.11.001.
    [7] Bhatia, A., Chug, A., & Prakash Singh, A. (2020). Application of extreme learning machine in plant disease prediction for highly imbalanced dataset. Journal of Statistics and Management Systems, 23(6), 1059–1068. https://doi.org/10.1080/09720510.2020.1799504.
    [8] Hatuwal, B., Shakya, A., & Joshi, B. (2020). Plant Leaf Disease Recognition Using Random Forest, KNN, SVM and CNN. POLIBITS, vol. 62, 2020, pp. 13–19, https://doi.org/10.17562/PB-62-2.
    [9] Lee, S. H., Goëau, H., Bonnet, P., & Joly, A. (2020). New perspectives on plant disease characterization based on deep learning. Computers and Electronics in Agriculture, 170(105220), 105220. https://doi.org/10.1016/j.compag.2020.105220.
    [10] Verma, S., Chug, A., & Singh, A. P. (2020). Exploring capsule networks for disease classification in plants. Journal of Statistics and Management Systems, 23(2), 307–315. https://doi.org/10.1080/09720510.2020.1724628.
    [11] Ferentinos, K. P. (2018). Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture, 145, 311–318. https://doi.org/10.1016/j.compag.2018.01.009.
    [12] Sardogan, M., Tuncer, A., & Ozen, Y. (2018). Plant leaf disease detection and classification based on CNN with LVQ algorithm. 2018 3rd International Conference on Computer Science and Engineering (UBMK).
    [13] Dubey, S. R., & Jalal, A. S. (2016). Apple disease classification using color, texture and shape features from images. Signal, Image and Video Processing, 10(5), 819–826. https://doi.org/10.1007/s11760-015-0821-1.
    [14] He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
    [15] Kawasaki, Y., Uga, H., Kagiwada, S., & Iyatomi, H. (2015). Basic study of automated diagnosis of viral plant diseases using convolutional neural networks. In Advances in Visual Computing (pp. 638–645). Springer International Publishing.

Aadhitya S V, Ashwin Hariharan R and Sriharipriya K C (2023), Disease Detection and Diagnosis of Agricultural Plant Leaf Using Machine Learning. IJEER 11(3), 749-753. DOI: 10.37391/ijeer.110317.