Maize Leaf Disease Detection using Manta-Ray Foraging Optimization with Deep Learning Model
Received: 14 May 2024 | Revised: 25 May 2024 | Accepted: 27 May 2024 | Online: 26 August 2024
Corresponding author: Shanmugam Vimalkumar
Abstract
Maize (corn) is a major and high yield crop, cultivated worldwide although diseases may cause severe yield reductions. Monitoring and identifying maize diseases throughout the growth cycle are crucial tasks. Accurately detecting diseases is an issue for farmers who need expertise in plant pathology, while professional diagnosis can be time-consuming and expensive. Meanwhile, conventional Deep Learning (DL) and image recognition models are slowly entering the field of plant disease detection. This paper proposes the Intelligent Maize Leaf Disease Detection design using the Manta-Ray Foraging Optimization with a DL (IMLDD-MRFODL) model. The aim of the IMLDD-MRFODL method is to detect and categorize maize leaf diseases. The IMLDD-MRFODL method applies Median Filtering (MF) for image preprocessing, a densely connected network (DenseNet) for feature extraction, and the MRFO technique for hyperparameter tuning. The IMLDD-MRFODL technique exploits a Long Short-Term Memory (LSTM) network for maize leaf disease classification. Experimental evaluation was conducted to validate the IMLDD-MRFODL approach and the comparative analysis exhibited the superior accuracy of the proposed method.
Keywords:
agriculture, maize leaf diseases, disease detection, computer vision, deep learningDownloads
References
C. Ashwini and V. Sellam, "An optimal model for identification and classification of corn leaf disease using hybrid 3D-CNN and LSTM," Biomedical Signal Processing and Control, vol. 9, Jun. 2024, Art. no. 106089.
Md. A. Haque, S. Marwaha, C. K. Deb, S. Nigam, and A. Arora, "Recognition of diseases of maize crop using deep learning models," Neural Computing and Applications, vol. 35, no. 10, pp. 7407–7421, Apr. 2023.
C. K. Rai and R. Pahuja, "Northern maize leaf blight disease detection and segmentation using deep convolution neural networks," Multimedia Tools and Applications, vol. 83, no. 7, pp. 19415–19432, Feb. 2024.
X. Qian, C. Zhang, L. Chen, and K. Li, "Deep Learning-Based Identification of Maize Leaf Diseases Is Improved by an Attention Mechanism: Self-Attention," Frontiers in plant science, vol. 13, Jan. 2022, Art. no. 864486.
R. Rashid, W. Aslam, R. Aziz, and G. Aldehim, "An Early and Smart Detection of Corn Plant Leaf Diseases Using IoT and Deep Learning Multi-Models," IEEE Access, vol. 12, pp. 23149–23162, Jan. 2024.
L. Ma, Q. Yu, H. Yu, and J. Zhang, "Maize Leaf Disease Identification Based on YOLOv5n Algorithm Incorporating Attention Mechanism," Agronomy, vol. 13, no. 2, Feb. 2023, Art. no. 521.
E. Iren, "Comparison of YOLOv5 and YOLOv6 Models for Plant Leaf Disease Detection," Engineering, Technology & Applied Science Research, vol. 14, no. 2, pp. 13714–13719, Apr. 2024.
R. Rajamohanan and B. C. Latha, "An Optimized YOLO v5 Model for Tomato Leaf Disease Classification with Field Dataset," Engineering, Technology & Applied Science Research, vol. 13, no. 6, pp. 12033–12038, Dec. 2023.
A. Abbas, U. Maqsood, S. U. Rehman, K. Mahmood, T. AlSaedi, and M. Kundi, "An Artificial Intelligence Framework for Disease Detection in Potato Plants," Engineering, Technology & Applied Science Research, vol. 14, no. 1, pp. 12628–12635, Feb. 2024.
F. Khan, N. Zafar, M. N. Tahir, M. Aqib, H. Waheed, and Z. Haroon, "A mobile-based system for maize plant leaf disease detection and classification using deep learning," Frontiers in Plant Science, vol. 14, May 2023, Art. no. 1079366.
M. Subramanian, K. Shanmugavadivel, and P. S. Nandhini, "On fine-tuning deep learning models using transfer learning and hyper-parameters optimization for disease identification in maize leaves," Neural Computing and Applications, vol. 34, no. 16, pp. 13951–13968, Aug. 2022.
Y. Xu, B. Zhao, Y. Zhai, Q. Chen, and Y. Zhou, "Maize Diseases Identification Method Based on Multi-Scale Convolutional Global Pooling Neural Network," IEEE Access, vol. 9, pp. 27959–27970, Jan. 2021.
L. Gunisetti, S. B. Koduri, and V. Jagannathan, "Optimized deep learning system for smart maize leaf disease detection in IoT platform via routing algorithm," Multimedia Tools and Applications, vol. 82, no. 9, pp. 13533–13555, Apr. 2023.
K. Ullah, M. A. Jan, and A. Sayyed, "Automatic Diseases Detection and Classification in Maize Crop using Convolution Neural Network," International Journal of Advanced Trends in Computer Science and Engineering, vol. 10, no. 2, pp. 675–679, Apr. 2021.
W. Zeng, H. Li, G. Hu, and D. Liang, "Identification of maize leaf diseases by using the SKPSNet-50 convolutional neural network model," Sustainable Computing: Informatics and Systems, vol. 35, Sep. 2022, Art. no. 100695.
M. Masood et al., "MaizeNet: A Deep Learning Approach for Effective Recognition of Maize Plant Leaf Diseases," IEEE Access, vol. 11, pp. 52862–52876, 2023.
P. B. Madhavi, T. Subaranjani, S. Sigtia, and V. Mehta, "A Hybrid Deep Learning Approach for Accurate and Transparent Maize Plant Disease Classification," in 11th International Conference on Computing for Sustainable Global Development, New Delhi, India, Mar. 2024, pp. 229–234.
P. Pavithra and P. Aishwarya, "Plant leaf disease detection using hybrid grasshopper optimization with modified artificial bee colony algorithm," Multimedia Tools and Applications, vol. 83, no. 8, pp. 22521–22543, Mar. 2024.
N. Win, K. Kyaw, T. Win, and P. Aung, "Image Noise Reduction Using Linear and Nonlinear Filtering Techniques," International Journal of Scientific and Research Publications, vol. 9, no. 8, pp. 816–821, Aug. 2019.
R. Pramanik, B. Banerjee, G. Efimenko, D. Kaplun, and R. Sarkar, "Monkeypox detection from skin lesion images using an amalgamation of CNN models aided with Beta function-based normalization scheme," PLOS ONE, vol. 18, no. 4, Apr. 2023, Art. no. e0281815.
M. Abdullahi, I. H. Hassan, M. D. Abdullahi, I. Aliyu, and J. Kim, "Manta Ray Foraging Optimization Algorithm: Modifications and Applications," IEEE Access, vol. 11, pp. 53315–53343, Jan. 2023.
D. Song and Y. Zhao, "A Data-Driven Approach Using Enhanced Bayesian-LSTM Deep Neural Networks for Picks Wear State Recognition," Electronics, vol. 12, no. 17, Jan. 2023, Art. no. 3593.
"PlantVillage Dataset." https://www.kaggle.com/datasets/emmarex/plantdisease.
C. Ashwini and V. Sellam, "EOS-3D-DCNN: Ebola optimization search-based 3D-dense convolutional neural network for corn leaf disease prediction," Neural Computing and Applications, vol. 35, no. 15, pp. 11125–11139, May 2023.
Downloads
How to Cite
License
Copyright (c) 2024 S. Vimalkumar, R. Latha
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
