The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
PlantDoc: A Dataset for Visual Plant Disease Detection
Open accessAbstract
India loses 35% of the annual crop yield due to plant diseases. Early detection of plant diseases remains difficult due to the lack of lab infrastructure and expertise. In this paper, we explore the possibility of computer vision approaches for scalable and early plant disease detection. The lack of availability of sufficiently large-scale non-lab data set remains a major challenge for enabling vision based plant disease detection. Against this background, we present PlantDoc: a dataset for visual plant disease detection. Our dataset contains 2,598 data points in total across 13 plant species and up to 17 classes of diseases, involving approximately 300 human hours of effort in annotating internet scraped images. To show the efficacy of our dataset, we learn 3 models for the task of plant disease classification. Our results show that modelling using our dataset can increase the classification accuracy by up to 31%. We believe that our dataset can help reduce the entry barrier of computer vision techniques in plant disease detection.
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Cites
ImageNet 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
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
Cited by
A comparative study of deep learning and internet of things for precision agriculture
Saranya, T., Deisy, C., Sridevi, S., & Anbananthen, K. S. M. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106034 · Cited by 232
Review of the State of the Art of Deep Learning for Plant Diseases: A Broad Analysis and Discussion
Hasan, R. I., Yusuf, S. M., & Alzubaidi, L. (2020) · Plants · doi:10.3390/plants9101302 · Cited by 213
Metadata
- Authors
- Singh, D., Jain, N., Jain, P., Kayal, P., Kumawat, S., & Batra, N.
- Venue
- Proceedings of the 7th ACM IKDD CoDS and 25th COMAD
- Publisher
- ACM
- Published
- 2020-01-05
- Pages
- 249-253
- OpenAlex
- W2989646980
- Cited by
- 758
- References
- 2
- FWCI
- 86.20
- Type
- conference paper
- Language
- en
- Open access
- green
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- Singh, D., Jain, N., Jain, P., Kayal, P., Kumawat, S., & Batra, N. (2020). PlantDoc: A Dataset for Visual Plant Disease Detection. Proceedings of the 7th ACM IKDD CoDS and 25th COMAD. https://doi.org/10.1145/3371158.3371196
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