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Article

UAV-Based Classification of Cercospora Leaf Spot Using RGB Images

1
Institute of Geodesy and Geoinformation, University of Bonn, 53115 Bonn, Germany
2
Institute of Sugar Beet Research, 37079 Göttingen, Germany
3
Strube Research GmbH & Co. KG, 38387 Söllingen, Germany
4
Pheno-Inspect GmbH, 46047 Oberhausen, Germany
*
Author to whom correspondence should be addressed.
Current address: Photogrammetry & Robotics Lab, University of Bonn, Nussallee 15, 53115 Bonn, Germany.
Academic Editor: Diego González-Aguilera
Drones 2021, 5(2), 34; https://doi.org/10.3390/drones5020034
Received: 22 March 2021 / Revised: 28 April 2021 / Accepted: 29 April 2021 / Published: 5 May 2021
(This article belongs to the Collection Feature Papers of Drones)
Plant diseases can impact crop yield. Thus, the detection of plant diseases using sensors that can be mounted on aerial vehicles is in the interest of farmers to support decision-making in integrated pest management and to breeders for selecting tolerant or resistant genotypes. This paper investigated the detection of Cercospora leaf spot (CLS), caused by Cercospora beticola in sugar beet using RGB imagery. We proposed an approach to tackle the CLS detection problem using fully convolutional neural networks, which operate directly on RGB images captured by a UAV. This efficient approach does not require complex multi- or hyper-spectral sensors, but provides reliable results and high sensitivity. We provided a detection pipeline for pixel-wise semantic segmentation of CLS symptoms, healthy vegetation, and background so that our approach can automatically quantify the grade of infestation. We thoroughly evaluated our system using multiple UAV datasets recorded from different sugar beet trial fields. The dataset consisted of a training and a test dataset and originated from different fields. We used it to evaluate our approach under realistic conditions and analyzed its generalization capabilities to unseen environments. The obtained results correlated to visual estimation by human experts significantly. The presented study underlined the potential of high-resolution RGB imaging and convolutional neural networks for plant disease detection under field conditions. The demonstrated procedure is particularly interesting for applications under practical conditions, as no complex and cost-intensive measuring system is required. View Full-Text
Keywords: UAV-based monitoring; agricultural robotics; plant disease detection; RGB images; Cercospora leaf spot; CNNs; semantic segmentation; phenotyping UAV-based monitoring; agricultural robotics; plant disease detection; RGB images; Cercospora leaf spot; CNNs; semantic segmentation; phenotyping
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MDPI and ACS Style

Görlich, F.; Marks, E.; Mahlein, A.-K.; König, K.; Lottes, P.; Stachniss, C. UAV-Based Classification of Cercospora Leaf Spot Using RGB Images. Drones 2021, 5, 34. https://doi.org/10.3390/drones5020034

AMA Style

Görlich F, Marks E, Mahlein A-K, König K, Lottes P, Stachniss C. UAV-Based Classification of Cercospora Leaf Spot Using RGB Images. Drones. 2021; 5(2):34. https://doi.org/10.3390/drones5020034

Chicago/Turabian Style

Görlich, Florian, Elias Marks, Anne-Katrin Mahlein, Kathrin König, Philipp Lottes, and Cyrill Stachniss. 2021. "UAV-Based Classification of Cercospora Leaf Spot Using RGB Images" Drones 5, no. 2: 34. https://doi.org/10.3390/drones5020034

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