The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Solving Current Limitations of Deep Learning Based Approaches for Plant Disease Detection
Open accessAbstract
Plant diseases cause great damage in agriculture, resulting in significant yield losses. The recent expansion of deep learning methods has found its application in plant disease detection, offering a robust tool with highly accurate results. The current limitations and shortcomings of existing plant disease detection models are presented and discussed in this paper. Furthermore, a new dataset containing 79,265 images was introduced with the aim to become the largest dataset containing leaf images. Images were taken in various weather conditions, at different angles, and daylight hours with an inconsistent background mimicking practical situations. Two approaches were used to augment the number of images in the dataset: traditional augmentation methods and state-of-the-art style generative adversarial networks. Several experiments were conducted to test the impact of training in a controlled environment and usage in real-life situations to accurately identify plant diseases in a complex background and in various conditions including the detection of multiple diseases in a single leaf. Finally, a novel two-stage architecture of a neural network was proposed for plant disease classification focused on a real environment. The trained model achieved an accuracy of 93.67%.
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Cites
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Cited by
Machine learning in agriculture domain: A state-of-art survey
Meshram, V., Patil, K., Meshram, V., Hanchate, D., & Ramkteke, S. D. (2021) · Artificial Intelligence in the Life Sciences · doi:10.1016/j.ailsci.2021.100010 · Cited by 367
Jackulin, C., & Murugavalli, S. (2022) · Measurement: Sensors · doi:10.1016/j.measen.2022.100441 · Cited by 319
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
Maski, P., & Thondiyath, A. (2021) · 2021 6th International Conference on Image, Vision and Computing (ICIVC) · doi:10.1109/icivc52351.2021.9526944 · Cited by 34
Metadata
- Authors
- Arsenovic, M., Karanovic, M., Sladojevic, S., Anderla, A., & Stefanovic, D.
- Venue
- Symmetry
- Publisher
- MDPI AG
- Published
- 2019-07-19
- Volume
- 11
- Issue
- 7
- Pages
- 939
- ISSN
- 2073-8994 (ISSN-L)
- OpenAlex
- W2963820222
- Cited by
- 519
- References
- 16
- FWCI
- 34.83
- Type
- journal article
- Language
- en
- Open access
- gold
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