Identification of Corn Leaf Diseases Comprising of Blight, Grey Spot and Rust Using DenseNet-201

  • CHYNTIA JABY ANAK ENTUNI Department of Electrical and Electronics Engineering, Faculty of Engineering, Universiti Malaysia Sarawak, 94300 Kota Samarahan, Sarawak, Malaysia
  • TENGKU MOHD AFENDI ZULCAFFLE Department of Electrical and Electronics Engineering, Faculty of Engineering, Universiti Malaysia Sarawak, 94300 Kota Samarahan, Sarawak, Malaysia
Keywords: Blight, corn, DenseNet-201, grey spot, rust


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.

How to Cite
ANAK ENTUNI, C. J., & ZULCAFFLE, T. M. A. (2022). Identification of Corn Leaf Diseases Comprising of Blight, Grey Spot and Rust Using DenseNet-201. Borneo Journal of Resource Science and Technology, 12(1), 125-134.