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Open AccessArticle

Prediction of the Kiwifruit Decline Syndrome in Diseased Orchards by Remote Sensing

1
Department of Agricultural, Food, Environmental and Animal Sciences (DI4A), University of Udine, Via delle Scienze 206, 33100 Udine, Italy
2
CREA - Council for Agricultural Research and Economics, Research Centre for Agriculture and Environment, I-40128 Bologna, Italy
3
Institute of Sugar Beet Research (IfZ), Holtenser Landstraße 77, 37079 Göttingen, Germany
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(14), 2194; https://doi.org/10.3390/rs12142194
Received: 15 May 2020 / Revised: 5 July 2020 / Accepted: 7 July 2020 / Published: 9 July 2020
(This article belongs to the Special Issue Crop Disease Detection Using Remote Sensing Image Analysis)
Eight years after the first record in Italy, Kiwifruit Decline (KD), a destructive disease causing root rot, has already affected more than 25% of the area under kiwifruit cultivation in Italy. Diseased plants are characterised by severe decay of the fine roots and sudden wilting of the canopy, which is only visible after the season’s first period of heat (July–August). The swiftness of symptom appearance prevents correct timing and positioning for sampling of the disease, and is therefore a barrier to aetiological studies. The aim of this study is to test the feasibility of thermal and multispectral imaging for the detection of KD using an unsupervised classifier. Thus, RGB, multispectral and thermal data from a kiwifruit orchard, with healthy and diseased plants, were acquired simultaneously during two consecutive growing seasons (2017–2018) using an Unmanned Aerial Vehicle (UAV) platform. Data reduction was applied to the clipped areas of the multispectral and thermal data from the 2017 survey. Reduced data were then classified with two unsupervised algorithms, a K-means and a hierarchical method. The plant vigour (canopy size and presence/absence of wilted leaves) and the health shifts exhibited by asymptomatic plants between 2017 and 2018 were evaluated from RGB data via expert assessment and used as the ground truth for cluster interpretation. Multispectral data showed a high correlation with plant vigour, while temperature data demonstrated a good potential use in predicting health shifts, especially in highly vigorous plants that were asymptomatic in 2017 and became symptomatic in 2018. The accuracy of plant vigour assessment was above 73% when using multispectral data, while clustering of the temperature data allowed the prediction of disease outbreak one year in advance, with an accuracy of 71%. Based on our results, the unsupervised clustering of remote sensing data could be a reliable tool for the identification of sampling areas, and can greatly improve aetiological studies of this new disease in kiwifruit. View Full-Text
Keywords: disease detection; outbreak prediction; sensor fusion; unsupervised clustering; multispectral imaging; thermal imaging; unmanned aerial vehicle; UAV disease detection; outbreak prediction; sensor fusion; unsupervised clustering; multispectral imaging; thermal imaging; unmanned aerial vehicle; UAV
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MDPI and ACS Style

Savian, F.; Martini, M.; Ermacora, P.; Paulus, S.; Mahlein, A.-K. Prediction of the Kiwifruit Decline Syndrome in Diseased Orchards by Remote Sensing. Remote Sens. 2020, 12, 2194. https://doi.org/10.3390/rs12142194

AMA Style

Savian F, Martini M, Ermacora P, Paulus S, Mahlein A-K. Prediction of the Kiwifruit Decline Syndrome in Diseased Orchards by Remote Sensing. Remote Sensing. 2020; 12(14):2194. https://doi.org/10.3390/rs12142194

Chicago/Turabian Style

Savian, Francesco; Martini, Marta; Ermacora, Paolo; Paulus, Stefan; Mahlein, Anne-Katrin. 2020. "Prediction of the Kiwifruit Decline Syndrome in Diseased Orchards by Remote Sensing" Remote Sens. 12, no. 14: 2194. https://doi.org/10.3390/rs12142194

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