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. Sensor-based image analysis including visible spectrum and hyperspectral and multispectral sensors are established technologies that promise to substitute, 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 issues for automating severity measurement under hyper-variable field conditions. The adoption of appropriate scales, training, instruction and aids (standard area diagrams) has contributed to improved accuracy of visual estimates. The apogee of accuracy for visual estimation is likely being approached, and any remaining increases in accuracy 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.

Plant disease quantification, mainly the intensity of disease symptoms on individual units (severity), is the basis for a plethora of research and applied purposes in plant pathology and related disciplines. These include evaluating treatment effect, monitoring epidemics, understanding yield loss, and phenotyping for host resistance. Although sensor technology has been available to measure disease severity using the visible spectrum or other spectral range imaging, it is visual sensing and perception that still dominates, especially in field research. Awareness of the importance of accuracy of visual estimates of severity began in 1892, when Cobb developed a set of diagrams as an aid to guide estimates of rust severity in wheat. Since that time, various approaches, some of them based on principles of psychophysics, have provided a foundation to understand sources of error during the estimation process as well as to develop different disease scales and disease-specific illustrations indicating the diseased area on specimens, similar to that developed by Cobb, and known as standard area diagrams (SADs). Several rater-related (experience, inherent ability, training) and technology-related (instruction, scales, and SADs) characteristics have been shown to affect accuracy. This review provides a historical perspective of visual severity assessment, accounting for concepts, tools, changing paradigms, and methods to maximize accuracy of estimates. A list of best-operating practices in plant disease quantification and future research on the topic is presented based on the current knowledge.

Standard area diagrams (SAD) have long been used as a tool to aid the estimation of plant disease severity, an essential variable in phytopathometry. Formal validation of SAD was not considered prior to the early 1990s, when considerable effort began to be invested developing SAD and assessing their value for improving accuracy of estimates of disease severity in many pathosystems. Peer-reviewed literature post-1990 was identified, selected, and cataloged in bibliographic software for further scrutiny and extraction of scientometric, pathosystem-related, and methodological-related data. In total, 105 studies (127 SAD) were found and authored by 327 researchers from 10 countries, mainly from Brazil. The six most prolific authors published at least seven studies. The scientific impact of a SAD article, based on annual citations after publication year, was affected by disease significance, the journal’s impact factor, and methodological innovation. The reviewed SAD encompassed 48 crops and 103 unique diseases across a range of plant organs. Severity was quantified largely by image analysis software such as QUANT, APS-Assess, or a LI-COR leaf area meter. The most typical SAD comprised five to eight black-and-white drawings of leaf diagrams, with severity increasing nonlinearly. However, there was a trend toward using true-color photographs or stylized representations in a range of color combinations and more linear (equally spaced) increments of severity. A two-step SAD validation approach was used in 78 of 105 studies for which linear regression was the preferred method but a trend toward using Lin’s correlation concordance analysis and hypothesis tests to detect the effect of SAD on accuracy was apparent. Reliability measures, when obtained, mainly considered variation among rather than within raters. The implications of the findings and knowledge gaps are discussed. A list of best practices for designing and implementing SAD and a website called SADBank for hosting SAD research data are proposed.