The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Deep leaning approach with colorimetric spaces and vegetation indices for vine diseases detection in UAV images
Abstract
Abstract Detection of symptoms in grape leaves is a very important factor in preventing a serious disease. An epidemic spread in vineyards has huge economic consequences and therefore it is considered a major challenge for viticulture. Automatic detection of vine diseases can play an important role in addressing the issue of diseases management. This study deals with the problem of identifying infected areas of grapevines using Unmanned Aerial Vehicles (UAV) images in the visible domain. In this paper we propose a method based on Convolutional neural network (CNN) and color information to detect symptoms in the vine yards. We studied and compared performances of CNNs using different color spaces, vegetation indices, as well as the combination of both information. The obtained results showed that CNNs with YUV color space combined with ExGR vegetation index, and CNNs with a combination of ExG, ExR, ExGR vegetation indices yield the best results with accuracy more than 95.8 % .
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Cites
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks
Dosovitskiy, A., Fischer, P., Springenberg, J. T., Riedmiller, M., & Brox, T. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2496141 · Cited by 762
Cited by
Plant Disease Detection and Classification by Deep Learning—A Review
Li, L., Zhang, S., & Wang, B. (2021) · IEEE Access · doi:10.1109/access.2021.3069646 · Cited by 1,008
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
Barbedo, J. (2019) · Drones · doi:10.3390/drones3020040 · Cited by 391
A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases
Dhaka, V. S., Meena, S. V., Rani, G., Sinwar, D., Kavita, K., Ijaz, M. F., & Woźniak, M. (2021) · Sensors · doi:10.3390/s21144749 · Cited by 386
Ouhami, M., Hafiane, A., Es-Saady, Y., El Hajji, M., & Canals, R. (2021) · Remote Sensing · doi:10.3390/rs13132486 · Cited by 264
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Metadata
- Authors
- Kerkech, M., Hafiane, A., & Canals, R.
- Venue
- Computers and Electronics in Agriculture
- Publisher
- Elsevier BV
- Published
- 2018-12
- Volume
- 155
- Pages
- 237-243
- ISSN
- 0168-1699 (ISSN-L)
- OpenAlex
- W2895994027
- Cited by
- 271
- References
- 2
- FWCI
- 19.42
- Type
- journal article
- Language
- en
- Open access
- closed
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- Kerkech, M., Hafiane, A., & Canals, R. (2018). Deep leaning approach with colorimetric spaces and vegetation indices for vine diseases detection in UAV images. Computers and Electronics in Agriculture. https://doi.org/10.1016/j.compag.2018.10.006
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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.
