Identification of Corn Leaf Diseases Comprising of Blight, Grey Spot and Rust Using DenseNet-201
Corn is a vital commodity in Malaysia because it is a key component of animal feed. The retention of the wholesome corn yield is essential to satisfy the rising demand. Like other plants, corn is susceptible to pathogens infection during the growing period. Manual observation of the diseases nevertheless takes time and requires a lot of work. The aim of this study was to propose an automatic approach to identify corn leaf diseases. The dataset used comprises of the images of diseased corn leaf comprising of blight, grey spot and rust as well as healthy corn leaf in YCbCr colour space representation. The DenseNet-201 algorithm was utilised in the proposed method of identifying corn leaf diseases. The training and validation analysis of distinctive epoch values of DenseNet-201 were also used to validate the proposed method, which resulted in significantly higher identification accuracy. DenseNet-201 succeeded 95.11% identification accuracy and it outperformed the prior identification methods such as ResNet-50, ResNet-101 and Bag of Features. The DenseNet-201 also has been validated to function as anticipated in identifying corn leaf diseases based on the algorithm validation assessment.
Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M.A., Al-Amidie, M. & Farhan, L. (2021). Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. Journal of Big Data, 8(1): 28–49.
Amna, N., Mohammad, A., Rabu, M.R., Alif, M. & Hifzan, M. (2019). An overview of the grain corn industry in Malaysia. Food and Fertilizer Technology Center Agricultural Policy Platform, 8(2): 1–7.
Aravind, K.R., Raja, P., Mukesh, K.V, Aniirudh, R., Ashiwin, R. & Szczepanski, C. (2018). Disease classification in maize crop using bag of features and multiclass support vector. International Conference on Inventive Systems and Control, 10(2): 1191–1196.
Attallah, O. (2021). CoMB-Deep: Composite deep learning-based pipeline for classifying Childhood Medulloblastoma and its classes. Frontiers in Neuroinformatics, 15(2): 1-19.
Chauhan, T., Palivela, H. & Tiwari, S. (2020). Optimization and fine-tuning of DenseNet model for classification of Covid-19 cases in medical imaging. International Journal of Information Management Data Insights, 1(7): 1-13.
Foley, D.J., Thenkabail, P.S., Aneece, I.P., Pardhasaradhi, G., Oliphant, A.J., Foley, D.J., Thenkabail, P.S., Aneece, I.P. & Pardhasaradhi, G. (2019). A meta-analysis of global crop water productivity of three leading world crops (wheat, corn, and rice) in the irrigated areas over three decades. International Journal of Digital Earth, 28(5): 1–37.
Fuentes, A., Yoon, S., Kim, C.S. & Park, S.D. (2017). A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition. International Journal of Sensors, 17(2): 1–21.
Gawade, A. (2021). Early-stage apple leaf disease prediction using deep learning. Bioscience Biotechnology Research Communications, 14(5): 40–43.
Hiba, C., Hamid, Z. & Omar, A. (2016). Bag of features model using the new approaches: a comprehensive study. International Journal of Advanced Computer Science and Applications, 7(1): 226–234.
Huang, G., Liu, Z., Pleiss, G., Van Der-Maaten, L. & Weinberger, K.Q. (2019). Convolutional networks with dense connectivity. IEEE Transactions on Pattern Analysis and Machine Intelligence, 3(1): 1–12.
Hughes, D.P. & Salathé, M. (2016). An open access repository of images on plant health to enable the development of mobile disease diagnostics. Computer and Society, 11(1), 2–13.
Iqbal, A., Qudoos, A., Çetingül, I.S., Shah, S.R.A. & Bayram, I. (2019). Looking at some animal feeds with respect to halal concept. Journal of Animal Science and Products, 2(1): 46–53.
Ji, L., Zhang, J., Zhang, C., Ma, C., Xu, S. & Sun, K. (2021). CondenseNet with exclusive lasso regularization. Neural Computing and Applications, 7(2): 1–15.
Kandel, I. & Castelli, M. (2020). Transfer learning with convolutional neural networks for diabetic retinopathy image classification. A review. Applied Sciences, 10(6): 1–24.
Kupidura, P. (2019). The comparison of different methods of texture analysis for their efficacy for land use classification in satellite imagery. Remote Sensing, 11(10): 1–20.
Lee, S.S., Alias, S.A., Jones, E.G.B., Zainuddin, N. & Chan, H.T. (2012). Checklist of Fungi of Malaysia. Kepong: Forest Research Institute Malaysia,
Maeda-Gutiérrez, V., Galván-Tejada, C.E., Zanella-Calzada, L.A., Celaya-padilla, J.M., Galván-tejada, J.I., Gamboa-rosales, H. & Olvera-olvera, C.A. (2020). Comparison of convolutional neural network architectures for classification of tomato plant diseases. Journal of Applied Sciences, 12(10): 1–15.
Mohanty, S.P., Hughes, D.P. & Salathé, M. (2016). Using deep learning for image-based plant disease detection. Frontiers in Plant Science, 7(4): 1–10.
Noh, S.H. (2021). Performance comparison of CNN models using gradient flow analysis. Informatics, 8(3): 1–13.
Santiago, W.E., Leite, N.J., Teruel, B.J., Karkee, M. & Azania, C.A.M. (2019). Evaluation of bag-of-features (BoF) technique for weed management in sugarcane production. Australian Journal of Crop Science, 3(2): 1819–1825.
Sijam, K., Ahmad, K. & Seman, Z.A. (2017). Characterisation and pathological variability of Exserohilum turcicum responsible for for causing northern corn leaf blight (NCLB) disease in Malaysia. Malaysian Journal of Microbiology, 13(3): 41–49.
Thompson, A. & Johnson, A. (1953). A host list of plant diseases of Malaya. Mycological Papers, 1(3): 38.
Wang, G., Sun, Y. & Wang, J. (2017). Automatic image-based plant disease severity estimation using deep learning. Computational Intelligence, 10(1): 1–9.
Wang, X., Şekercioğlu, Y.A., Drummond, T., Frémont, V., Natalizio, E. & Fantoni, I. (2018). Relative pose based redundancy removal: Collaborative RGB-D data transmission in mobile visual sensor networks. Sensors, 18(8): 1–23.
Xie, S., Yu, Z. & Lv, Z. (2021). Multi-disease prediction based on deep learning: A survey. Computer Modeling in Engineering and Sciences, 128(2): 439–552.
Yang, B. & Xu, Y. (2021). Applications of deep-learning approaches in horticultural research: a review. Horticulture Research, 8(1): 1–31.
Copyright (c) 2022 Borneo Journal of Resource Science and Technology
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Copyright Transfer Statement for Journal
1) In signing this statement, the author(s) grant UNIMAS Publisher an exclusive license to publish their original research papers. The author(s) also grant UNIMAS Publisher permission to reproduce, recreate, translate, extract or summarize, and to distribute and display in any forms, formats, and media. The author(s) can reuse their papers in their future printed work without first requiring permission from UNIMAS Publisher, provided that the author(s) acknowledge and reference publication in the Journal.
2) For open access articles, the author(s) agree that their articles published under UNIMAS Publisher are distributed under the terms of the CC-BY-NC-SA (Creative Commons Attribution-Non Commercial-Share Alike 4.0 International License) which permits unrestricted use, distribution, and reproduction in any medium, for non-commercial purposes, provided the original work of the author(s) is properly cited.
3) For subscription articles, the author(s) agree that UNIMAS Publisher holds copyright, or an exclusive license to publish. Readers or users may view, download, print, and copy the content, for academic purposes, subject to the following conditions of use: (a) any reuse of materials is subject to permission from UNIMAS Publisher; (b) archived materials may only be used for academic research; (c) archived materials may not be used for commercial purposes, which include but not limited to monetary compensation by means of sale, resale, license, transfer of copyright, loan, etc.; and (d) archived materials may not be re-published in any part, either in print or online.
4) The author(s) is/are responsible to ensure his or her or their submitted work is original and does not infringe any existing copyright, trademark, patent, statutory right, or propriety right of others. Corresponding author(s) has (have) obtained permission from all co-authors prior to submission to the journal. Upon submission of the manuscript, the author(s) agree that no similar work has been or will be submitted or published elsewhere in any language. If submitted manuscript includes materials from others, the authors have obtained the permission from the copyright owners.
5) In signing this statement, the author(s) declare(s) that the researches in which they have conducted are in compliance with the current laws of the respective country and UNIMAS Journal Publication Ethics Policy. Any experimentation or research involving human or the use of animal samples must obtain approval from Human or Animal Ethics Committee in their respective institutions. The author(s) agree and understand that UNIMAS Publisher is not responsible for any compensational claims or failure caused by the author(s) in fulfilling the above-mentioned requirements. The author(s) must accept the responsibility for releasing their materials upon request by Chief Editor or UNIMAS Publisher.
6) The author(s) should have participated sufficiently in the work and ensured the appropriateness of the content of the article. The author(s) should also agree that he or she has no commercial attachments (e.g. patent or license arrangement, equity interest, consultancies, etc.) that might pose any conflict of interest with the submitted manuscript. The author(s) also agree to make any relevant materials and data available upon request by the editor or UNIMAS Publisher.