From visual estimates to fully automated sensor-based measurements of plant disease severity: status and challenges for improving accuracy

Authors: Bock, Clive; BARBEDO, JAYME - Embrapa; DEL PONTE, EMERSON - Universidade Federal De Viçosa; CHIANG, KUO-SZU - Chung Hsing University; BOHNEMKAMP, DAVID - Institute For Agriculture & Crop Science - Germany; MAHLEIN, ANNE-KATRIN - Beet Sugar Development Foundation

Submitted to: Phytopathology Research
Publication Type: Review Article
Publication Acceptance Date: 3/20/2020
Publication Date: 4/26/2020
Citation: Bock, C.H., Barbedo, J.G., Del Ponte, E.M., Chiang, K., Bohnemkamp, D., Mahlein, A. 2020. From visual estimates to fully automated sensor-based measurements of plant disease severity: status and challenges for improving accuracy. Phytopathology Research. 2:9. https://doi.org/10.1186/s42483-020-00049-8.
DOI: https://doi.org/10.1186/s42483-020-00049-8

Interpretive Summary: Review Article Only.

Technical Abstract: The severity of plant diseases, traditionally the proportion of the plant tissue exhibiting symptoms, is a key quantitative variable to know for many diseases and is prone to error. Good quality disease severity data should be accurate (close to the true value). Earliest quantification of disease severity was by visual estimates. However, sensor-based image analysis including visible spectrum (red-green-blue, RGB) cameras and hyperspectral and multispectral imaging (HSI and MSI) systems are established technologies that promise to substitute for, or complement visual ratings. Indeed, these technologies have measured disease severity accurately under controlled conditions but are yet to demonstrate their full potential for accurate measurement under field conditions. Sensor technology is advancing rapidly, and artificial intelligence may help overcome some issues for automating severity measurement under hyper-variable field conditions. The adoption of appropriate scales, training, instruction and aids (i.e. standard area diagrams) has contributed to improved accuracy of visual estimates so that the apogee of accuracy for visual estimation is likely being approached. Further research is still needed to better understand effects of symptom type, instruction, training etc on accuracy. But any remaining increases in accuracy of visual estimates are likely to be small. Due to automation and rapidity, sensor-based measurement offers potential advantages compared with visual estimates, but the latter will remain important for years to come. Mobile, automated sensor-based systems will become increasingly common in controlled conditions and, eventually, in the field for measuring plant disease severity for the purpose of research and decision making.