The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
Automated Identification of Northern Leaf Blight-Infected Maize Plants from Field Imagery Using Deep Learning
Open accessAbstract
Northern leaf blight (NLB) can cause severe yield loss in maize; however, scouting large areas to accurately diagnose the disease is time consuming and difficult. We demonstrate a system capable of automatically identifying NLB lesions in field-acquired images of maize plants with high reliability. This approach uses a computational pipeline of convolutional neural networks (CNNs) that addresses the challenges of limited data and the myriad irregularities that appear in images of field-grown plants. Several CNNs were trained to classify small regions of images as containing NLB lesions or not; their predictions were combined into separate heat maps, then fed into a final CNN trained to classify the entire image as containing diseased plants or not. The system achieved 96.7% accuracy on test set images not used in training. We suggest that such systems mounted on aerial- or ground-based vehicles can help in automated high-throughput plant phenotyping, precision breeding for disease resistance, and reduced pesticide use through targeted application across a variety of plant and disease categories.
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Cites
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Cited by
Plant diseases and pests detection based on deep learning: a review
Liu, J., & Wang, X. (2021) · Plant Methods · doi:10.1186/s13007-021-00722-9 · Cited by 1,011
Plant Disease Detection and Classification by Deep Learning—A Review
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Jiang, P., Chen, Y., Liu, B., He, D., & Liang, C. (2019) · IEEE Access · doi:10.1109/access.2019.2914929 · Cited by 892
Plant Disease Detection and Classification by Deep Learning
Saleem, M. H., Potgieter, J., & Arif, K. M. (2019) · Plants · doi:10.3390/plants8110468 · Cited by 886
Plant disease identification from individual lesions and spots using deep learning
Arnal Barbedo, J. G. (2019) · Biosystems Engineering · doi:10.1016/j.biosystemseng.2019.02.002 · Cited by 783
Metadata
- Authors
- DeChant, C., Wiesner-Hanks, T., Chen, S., Stewart, E. L., Yosinski, J., Gore, M. A., Nelson, R. J., & Lipson, H.
- Venue
- Phytopathology®
- Publisher
- Scientific Societies
- Published
- 2017-11
- Volume
- 107
- Issue
- 11
- Pages
- 1426-1432
- ISSN
- 0031-949X (ISSN-L)
- 1943-7684
- OpenAlex
- W2730129132
- Cited by
- 428
- References
- 22
- FWCI
- 20.23
- Type
- journal article
- Language
- en
- Open access
- bronze
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- The paper
- DeChant, C., Wiesner-Hanks, T., Chen, S., Stewart, E. L., Yosinski, J., Gore, M. A., Nelson, R. J., & Lipson, H. (2017). Automated Identification of Northern Leaf Blight-Infected Maize Plants from Field Imagery Using Deep Learning. Phytopathology®. https://doi.org/10.1094/phyto-11-16-0417-r
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
