Abstract
Selection of sugar beet (Beta vulgaris L.) cultivars that are resistant to Cercospora Leaf Spot (CLS) disease is critical to increase yield. Such selection requires an automatic, fast, and objective method to assess CLS severity on thousands of cultivars in the field. For this purpose, we compare the use of submillimeter scale RGB imagery acquired from an Unmanned Ground Vehicle (UGV) under active illumination and centimeter scale multispectral imagery acquired from an Unmanned Aerial Vehicle (UAV) under passive illumination. Several variables are extracted from the images (spot density and spot size for UGV, green fraction for UGV and UAV) and related to visual scores assessed by an expert. Results show that spot density and green fraction are critical variables to assess low and high CLS severities, respectively, which emphasizes the importance of having submillimeter images to early detect CLS in field conditions. Genotype sensitivity to CLS can then be accurately retrieved based on time integrals of UGV- and UAV-derived scores. While UGV shows the best estimation performance, UAV can show accurate estimates of cultivar sensitivity if the data are properly acquired. Advantages and limitations of UGV, UAV, and visual scoring methods are finally discussed in the perspective of high-throughput phenotyping.
Highlights
Cercospora Leaf Spot (CLS) caused by Cercospora beticola is one of the most damaging foliar diseases for sugar beet (Beta vulgaris L.) crops
We first evaluated the variability between the green fraction (GF), spot density (SD), and spot size (SS) values derived from single images as compared to their averages per microplot
Results show that such variability remained limited for 2016, with relative root mean square error of prediction (RMSE) computed over all the microplots and observation dates lower than 23% and r2 higher than 0.61 (Figure 5)
Summary
Cercospora Leaf Spot (CLS) caused by Cercospora beticola is one of the most damaging foliar diseases for sugar beet (Beta vulgaris L.) crops. It can induce losses of 30 to 48% in recoverable sucrose as reported by [1]. Wet, and humid conditions, fungus conidia infect leaves, resulting in the appearance of millimeter-scale brown round spots. A significant reduction of the use of fungicides is highly desired since they affect the environment while being expensive [3] Their efficacy has already decreased as resistance to fungicides has been reported [4,5,6]. In addition to crop rotation, such reduction may be achieved with the selection of resistant cultivars and with an early detection of the symptoms enabling a more effective use of fungicides
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