The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
Early Disease Classification of Mango Leaves Using Feed-Forward Neural Network and Hybrid Metaheuristic Feature Selection
Open accessAbstract
Plant disease, especially crop plants, is a major threat to global food security since many diseases directly affect the quality of the fruits, grains, and so on, leading to a decrease in agricultural productivity. Farmers have to observe and determine whether a leaf was infected by naked eyes. This process is unreliable, inconsistent, and error prone. Several works on deep learning techniques for detecting leaf diseases had been proposed. Most of them built their models based on limited resolution images using convolutional neural networks (CNNs). In this research, we aim at detecting early disease on plant leaves with small disease blobs, which can only be detected with higher resolution images, by an artificial neural network (ANN) approach. After a pre-processing step using a contrast enhancement method, all the infested blobs are segmented for the whole dataset. A list of several measurement-based features that represents the blobs are chosen and then selected based on their influences on the model's performance using a wrapper-based feature selection algorithm, which is built based on a hybrid metaheuristic. The chosen features are used as inputs for an ANN. We compare the results obtained using our methods with another approach using popular CNN models (AlexNet, VGG16, ResNet-50) enhanced with transfer learning. The ANN's results are better than those of CNNs using a simpler network structure (89.41% vs 78.64%, 79.92%, and 84.88%, respectively). This shows that our approach can be implemented on low-end devices such as smartphones, which will be of great assistance to farmers on the field.
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“In 2020, some researchers have utilized same-domain TL and achieved excellent results [86–88, 157].”
From Review of deep learning: concepts, CNN architectures, challenges, applications, future directions (2021) · cites this paper as [157]
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · cited 1×
“After performing contrast enhancement step, we investigate four popular CNN models, namely AlexNet [30], VGG16 [31], ResNet [32] (ResNet-50 variant).”
MethodVery Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148 · cited 1×
“After performing contrast enhancement step, we investigate four popular CNN models, namely AlexNet [30], VGG16 [31], ResNet [32] (ResNet-50 variant).”
MethodImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · cited 1×
“APPROACH WITH CNNS After performing contrast enhancement step, we investigate four popular CNN models, namely AlexNet [30], VGG16 [31], ResNet [32] (ResNet-50 variant).”
MethodAdam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · cited 1×
“Moreover, the optimization algorithm for the training process is done by Adam optimizer[36].”
MethodKennedy, J., & Eberhart, R. (2002) · Proceedings of ICNN'95 - International Conference on Neural Networks · doi:10.1109/icnn.1995.488968 · Cited by 58,192
Particle swarm optimization: An overview
Poli, R., Kennedy, J., & Blackwell, T. (2007) · Swarm Intelligence · doi:10.1007/s11721-007-0002-0 · Cited by 21,101
Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014) · Advances in Engineering Software · doi:10.1016/j.advengsoft.2013.12.007 · Cited by 20,442 · cited 3×
“In this study, we implement a wrapper-based method with a hybrid meta-heuristic called Adaptive Particle – Grey Wolf Optimization (APGWO)[39], which is combined from Particle Swarm Optimization (PSO)[40] and Grey Wolf Optimization (GWO) [22].”
MethodUsing Deep Learning for Image-Based Plant Disease Detection
Mohanty, S. P., Hughes, D. P., & Salathé, M. (2016) · Frontiers in Plant Science · doi:10.3389/fpls.2016.01419 · Cited by 5,712 · cited 3×
“Secondly, when testing with images taken under conditions that are different from the training images, the CNN models’ performance decreased signifi-cantly, as stated by Mohanty et al. [7].”
MethodInfluentialA Comparative Study on Feature Selection in Text Categorization
Yang, Y., & Pedersen, J. (1997) · Cited by 4,749
Convolutional neural networks: an overview and application in radiology
Yamashita, R., Nishio, M., Do, R. K. G., & Togashi, K. (2018) · Insights into Imaging · doi:10.1007/s13244-018-0639-9 · Cited by 4,682 · cited 1×
“They consist of multiple layers with later ones built on top of previously learned features [3].”
Background
Research works printed but not linked: 3
- kulkarni, 2012, Applying image processing technique to detect plant diseases, International Journal of Modern Engineering Research, 2, 3661
- pedregosa, 2011, Scikit-learn: Machine learning in Python title, J Mach Learn Res, 12, 2825
- rahman, 2015, Image contrast enhancement for brightness preservation based on dynamic stretching, Int J Image Process, 9, 241
Cited by
Sorted by citations
Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
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) · Journal of Big Data · doi:10.1186/s40537-021-00444-8 · Cited by 7,906
Alzubaidi, L., Bai, J., Al-Sabaawi, A., Santamaría, J., Albahri, A. S., Al-dabbagh, B. S. N., Fadhel, M. A., Manoufali, M., Zhang, J., Al-Timemy, A. H., et al. (2023) · Journal Of Big Data · doi:10.1186/s40537-023-00727-2 · Cited by 1,057
Qiu, S., Zhao, H., Jiang, N., Wang, Z., Liu, L., An, Y., Zhao, H., Miao, X., Liu, R., & Fortino, G. (2022) · Information Fusion · doi:10.1016/j.inffus.2021.11.006 · Cited by 671
A systematic review of deep learning techniques for plant diseases
Paçal, İ., Kunduracıoğlu, İ., Alma, M. H., Deveci, M., Kadry, S., Nedoma, J., Slaný, V., & Martínek, R. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10944-7 · Cited by 353
“Pham et al. (2020) used an ANN model to classify mango leaves in the PlantVillage dataset, achieving an 89.41% accuracy rate.”
InfluentialLeaf disease detection using machine learning and deep learning: Review and challenges
Sarkar, C., Gupta, D., Gupta, U. C., & Hazarika, B. B. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110534 · Cited by 290
Metadata
- Authors
- Pham, T. N., Tran, L. V., & Dao, S. V. T.
- Affiliations
- Vietnam National University Ho Chi Minh City (VN)
- Venue
- IEEE Access
- Published
- 2020
- Volume
- 8
- Pages
- 189960-189973
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W3093991637
- Cited by
- 262
- References
- 39
- Citations by year
- 2026: 28 · 2025: 43 · 2024: 79 · 2023: 61 · 2022: 37 · 2021: 13 · 2020: 1
- FWCI
- 18.08
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- IEEE Access (publishedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Keywords
- mango leaf disease, plant disease detection, feedforward neural networks, wrapper-based feature selection, contrast enhancement, low-resolution images, transfer learning comparison
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