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Identification of the onset of disease within a potato crop using a UAV equipped with un-modified and modified commercial off-the-shelf digital cameras

  • S. Gibson-Poole (a1), S. Humphris (a2), I. Toth (a2) and A. Hamilton (a1)


This paper investigates the effectiveness of using a UAV with dual commercial off-the-shelf (COTS) cameras, one un-modified and one modified to sense near infra-red (NIR) wavelengths to identify the onset of disease within a trial crop of potatoes. The trial was composed of 2 plots of 16 drills containing 12 tubers exposed to the blackleg disease-causing bacterial pathogen (Pectobacterium atrosepticum) in order to demonstrate best practise tuber storage and haulm destruction methods. Eleven sets of aerial data were gathered between 27/5/2016~29/7/2016 and compared with ground truth data collected on 14/7/2016. Visual analysis of the data could only detect the onset of disease and not the specific infection and resulted in a user accuracy (UA) of 83% and producer accuracy (PA) of 78%, with a total accuracy (TA) of 91% and Kappa coefficient (K) of 0.75. The building blocks of an automated classification routine have been constructed using pixel and object based image analysis (OBIA) methods, which have shown promising first results (UA 65%, PA 73%, TA 87%, K 0.61) but requires further refinement to achieve an equivalent level of accuracy as that of the visual analysis.


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Berra, E, Gibson-Poole, S, MacArthur, A, Gaulton, R and Hamilton, A 2015. Estimation of the spectral sensitivity functions of un-modified and modified commercial off-the-shelf digital cameras to enable their use as a multispectral imaging system for UAVs. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences 40 (1), 207.
Bussan, AJ, Mitchell, PD, Copas, ME and Drilias, MJ 2007. Evaluation of the effect of density on potato yield and tuber size distribution. Crop Science 47 (6), 24622472.
Camargo, FF, Almeida, CM, Costa, GAOP, Feitosa, RQ, Oliveira, DAB, Heipke, C and Ferreira, RS 2012. An open source object-based framework to extract landform classes. Expert Systems with Applications 39 (1), 541554.
Coffin, D 2016. DCRAW Application. Available at: (accessed 12/12/2016).
Charkowski, AO 2015. Biology and control of Pectobacterium in potato. American Journal of Potato Research 92 (2), 223229.
Czajkowski, R, Perombelon, MC, van Veen, JA and van der Wolf, JM 2011. Control of blackleg and tuber soft rot of potato caused by Pectobacterium and Dickeya species: a review. Plant pathology 60 (6), 9991013.
Foody, GM 2002. Status of land cover classification accuracy assessment. Remote sensing of environment 80 (1), 185201.
Niemann, T 2016. PTLens Application. Available at: (accessed 12/12/2016).
Pérombelon, MCM 2002. Potato diseases caused by soft rot erwinias: an overview of pathogenesis. Plant Pathology 51 (1), 112.
Rabatel, G, Gorretta, N and Labbé, S 2014. Getting simultaneous red and near-infrared band data from a single digital camera for plant monitoring applications: Theoretical and practical study. Biosystems Engineering 117, pp. 214.
Rasmussen, J, Ntakos, G, Nielsen, J, Svensgaard, J, Poulsen, R N and Christensen, S 2016. Are vegetation indices derived from consumer-grade cameras mounted on UAVs sufficiently reliable for assessing experimental plots. European Journal of Agronomy 74, 7592.
Rouse, J Jr, Haas, RH, Schell, JA and Deering, DW 1974. Monitoring vegetation systems in the Great Plains with ERTS. NASA special publication 351, 309.
Schindelin, J, Arganda-Carreras, I, Frise, E, Kaynig, V, Longair, M, Pietzsch, T and Tinevez, JY 2012. Fiji: an open-source platform for biological-image analysis. Nature methods 9 (7), 676682.
Shahbazi, M, Théau, J and Ménard, P 2014. Recent applications of unmanned aerial imagery in natural resource management. GIScience & Remote Sensing 51 (4), 339365.
Skelsey, P, Elphinstone, JG, Saddler, GS, Wale, SJ and Toth, IK 2016. Spatial analysis of blackleg-affected seed potato crops in Scotland. Plant Pathology 65, 570576.
Sugiura, R, Tsuda, S, Tamiya, S, Itoh, A, Nishiwaki, K, Murakami, N and Nuske, S 2016. Field phenotyping system for the assessment of potato late blight resistance using RGB imagery from an unmanned aerial vehicle. Biosystems Engineering 148, 110.
Toth, IK, Van Der Wolf, JM, Saddler, G, Lojkowska, E, Hélias, V, Pirhonen, M and Elphinstone, JG 2011. Dickeya species: an emerging problem for potato production in Europe. Plant Pathology 60 (3), 385399.
Verhoeven, GJJ 2010. It’s all about the format-unleashing the power of RAW aerial photography. Int. Journal of Remote Sensing 31 (8), 20092042.
Zhang, C and Kovacs, JM 2012. The application of small unmanned aerial systems for precision agriculture: a review. Precision Agriculture 13 (6), 693712.
Zhou, J, Pavek, MJ, Shelton, SC, Holden, ZJ and Sankaran, S 2016. Aerial multispectral imaging for crop hail damage assessment in potato. Computers and Electronics in Agriculture 127, 406412.


Identification of the onset of disease within a potato crop using a UAV equipped with un-modified and modified commercial off-the-shelf digital cameras

  • S. Gibson-Poole (a1), S. Humphris (a2), I. Toth (a2) and A. Hamilton (a1)


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