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Identification of diseases and physiological disorders in potato via multispectral drone imagery using machine learning tools

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Abstract

The rapid and precise detection of diseases and plant disorders is the basis for the adequate and timely design of management strategies. Currently, there are several non-destructive alternatives that allow early detection, highlighting the use of spectral cameras attached to unmanned aerial vehicles (UAVs). The objective of this research was to evaluate the use of multispectral cameras on UAVs to discriminate vascular wilt caused by Verticillium spp., (VW), waterlogging stress (WL), and an unknown alteration (UA) in commercial potato (Solanum tuberosum) variety “Diacol Capiro” crops. Plots were monitored during the crop cycle, performing the visual characterization of the diseases and disorders present. Five spectral band images were acquired using a MicaSense RedEdge spectral camera attached to a Map-T680 hexacopter drone to extract the bands and calculate the vegetation indices that were calibrated and evaluated to determine their ability to discriminate between diseased and healthy plants based on a generalized linear model (GLM) and Kappa index. Additionally, the supervised random forest classification method was implemented, optimized, and evaluated using the accuracy, area under receiver operating characteristic curve (ROC-AUC), kappa index, and inference error based on k-fold cross-validation. After algorithms optimization our results show a classifier accuracy, kappa and ROC-AUC values to VW, WL and UA between 73.5–82.5%, 0.56–0.71, 0.97–0.98, and 35 37.5–51.9%, 0.07–0.06, and 0.88–0.94 for plots 1 and 2, respectively. This study reports an approach to the use of multispectral cameras attached to UAVs as a tool with potential for the detection of diseases and physiological disorders in commercial potato crops.

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Data availability

The data sets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The authors would like to thank the “Semillero de Investigación en Geomática Aplicada” for the loan of the drone and the spectral camera for field work. We would also like to thank several potato farmers and Sumiagro (™) for their valuable information and support during this research. In addition, we want to thank Professor Ivan Alberto Lizarazo Salcedo of the Universidad Nacional for his valuable help in the data analysis.

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Authors and Affiliations

  1. Departamento de Agronomía, Facultad de Ciencias Agrarias, Universidad Nacional de Colombia Sede Bogotá, Bogotá, D.C, Colombia

    William A. León-Rueda, Sandra Gómez- Caro & Joaquín Guillermo Ramírez-Gil

  2. 3D Geoinformation Group, Delft University of Technology, Julianalaan 134, 2628BL, Delft, Netherlands

    Camilo León

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  1. William A. León-Rueda
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  4. Joaquín Guillermo Ramírez-Gil
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Contributions

Conceptualization: JGRG, SGC. Data curation: JGRG, WALR. Formal analysis: JGRG, WALR. Investigation: JGRG, SGC, WALR. Methodology: JGRG, WALR. Software: JGRG, WALR. Writing, original draft preparation: JGRG, WALR. Writing, review and editing: JGRG. Supervision: JGRG, CL. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Joaquín Guillermo Ramírez-Gil.

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León-Rueda, W.A., León, C., Caro, S.G. et al. Identification of diseases and physiological disorders in potato via multispectral drone imagery using machine learning tools. Trop. plant pathol. 47, 152–167 (2022). https://doi.org/10.1007/s40858-021-00460-2

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