UAV hyperspectral remote sensor images for mango plant disease and pest identification using MD-FCM and XCS-RBFNN-Reference-Cited by-同舟云学术

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UAV hyperspectral remote sensor images for mango plant disease and pest identification using MD-FCM and XCS-RBFNN

Published:2023-08-31 Issue:9 Volume:195 Page:
ISSN:0167-6369
Container-title:Environmental Monitoring and Assessment
language:en
Short-container-title:Environ Monit Assess

Author:

Pansy D. Lita,Murali M.

Publisher

Springer Science and Business Media LLC

Subject

Management, Monitoring, Policy and Law,Pollution,General Environmental Science,General Medicine

Link

https://link.springer.com/content/pdf/10.1007/s10661-023-11678-9.pdf

Reference36 articles.

1. Aasen, H., Honkavaara, E., Lucieer, A., & Zarco-Tejada, P. J. (2018). Quantitative remote sensing at ultra-high resolution with UAV spectroscopy: a review of sensor technology, measurement procedures, and data correction workflows. Remote Sensing, 10(7), 1–42. https://doi.org/10.3390/rs10071091

2. Abdulridha, J., Ampatzidis, Y., Qureshi, J., & Roberts, P. (2020). Laboratory and UAV-based identification and classification of tomato yellow leaf curl, bacterial spot, and target spot diseases in tomato utilizing hyperspectral imaging and machine learning. Remote Sensing, 12(17), 1–17. https://doi.org/10.3390/rs12172732

3. Ashwinkumar, S., Rajagopal, S., Manimaran, V., & Jegajothi, B. (2022). Automated plant leaf disease detection and classification using optimal MobileNet based convolutional neural networks. Materials Today: Proceedings, 51, 480–487. https://doi.org/10.1016/j.matpr.2021.05.584

4. Barbedo, J. G. A. (2019). A review on the use of unmanned aerial vehicles and imaging sensors for monitoring and assessing plant stresses. Drones, 3(2), 1–27. https://doi.org/10.3390/drones3020040

5. Candiago, S., Remondino, F., De Giglio, M., Dubbini, M., & Gattelli, M. (2015). Evaluating multispectral images and vegetation indices for precision farming applications from UAV images. Remote Sensing, 7(4), 4026–4047. https://doi.org/10.3390/rs70404026

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