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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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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