In the past decade, there has been a recognized need for innovative methods to monitor and manage plant diseases, aiming to meet the precision demands of modern agriculture. Over the last 15 years, significant advances in the detection, monitoring, and management of plant diseases have been made, largely propelled by cutting-edge technologies. Recent advances in precision agriculture have been driven by sophisticated tools such as optical sensors, artificial intelligence, microsensor networks, and autonomous driving vehicles. These technologies have enabled the development of novel cropping systems, allowing for targeted management of crops, contrasting with the traditional, homogeneous treatment of large crop areas. The research in this field is usually a highly collaborative and interdisciplinary endeavor. It brings together experts from diverse fields such as plant pathology, computer science, statistics, engineering, and agronomy to forge comprehensive solutions. Despite the progress, translating the advancements in the precision of decision-making or automation into agricultural practice remains a challenge. The knowledge transfer to agricultural practice and extension has been particularly challenging. Enhancing the accuracy and timeliness of disease detection continues to be a priority, with data-driven artificial intelligence systems poised to play a pivotal role. This perspective article addresses critical questions and challenges faced in the implementation of digital technologies for plant disease management. It underscores the urgency of integrating innovative technological advances with traditional integrated pest management. It highlights unresolved issues regarding the establishment of control thresholds for site-specific treatments and the necessary alignment of digital technology use with regulatory frameworks. Importantly, the paper calls for intensified research efforts, widespread knowledge dissemination, and education to optimize the application of digital tools for plant disease management, recognizing the intersection of technology's potential with its current practical limitations.

The severity of plant diseases, traditionally defined as the proportion of the plant tissue exhibiting symptoms, is a key quantitative variable to know for many diseases but is prone to error. Plant pathologists face many situations in which the measurement by nearest percent estimates (NPEs) of disease severity is time-consuming or impractical. Moreover, rater NPEs of disease severity are notoriously variable. Therefore, NPEs of disease may be of questionable value if severity cannot be determined accurately and reliably. In such situations, researchers have often used a quantitative ordinal scale of measurement—often alleging the time saved, and the ease with which the scale can be learned. Because quantitative ordinal disease scales lack the resolution of the 0 to 100% scale, they are inherently less accurate. We contend that scale design and structure have ramifications for the resulting analysis of data from the ordinal scale data. To minimize inaccuracy and ensure that there is equivalent statistical power when using quantitative ordinal scale data, design of the scales can be optimized for use in the discipline of plant pathology. In this review, we focus on the nature of quantitative ordinal scales used in plant disease assessment. Subsequently, their application and effects will be discussed. Finally, we will review how to optimize quantitative ordinal scales design to allow sufficient accuracy of estimation while maximizing power for hypothesis testing.

Knowledge of the extent of the symptoms of a plant disease, generally referred to as severity, is a key to both fundamental and applied aspects of plant pathology. Most commonly, severity is obtained visually and the accuracy of each estimate (closeness to the actual value) by individual raters is paramount as it directly affects inferences and decisions based on average data. Thus, the more accurate the visual estimate of severity made by a rater for each specimen, the lower the magnitude of errors in the analysis, and thus the lower the risk of incorrect decisions. Errors in estimates of severity on diseased specimens are becoming better understood and ways to minimize them have been developed during the last 25 years. The development of various tools to aid raters prior to, or during the assessments provides a basis for greater accuracy. Severity is often based on the continuous percentage scale to represent the proportion of area diseased. However, some tools, such as those based on category scales have been shown to harm accuracy of some estimates under specific conditions, potentially leading to Type II errors. On the other hand, ensuring raters are familiar with symptom identity, assessment training and the use of standard area diagrams (a series of pictorial representations of examples of actual severity), can lead to improved accuracy of each (or at least most) estimate(s) by raters, particularly those who are innately less accurate. In this review, the need for accuracy of visual estimates of disease severity on plant specimens is emphasized in relation to the sample mean and variance, and the history of accuracy in disease estimation is described, the advantages of tools to aid accuracy are presented and future research needs are outlined.

Plant pathologists most often obtain quantitative information on disease severity using visual assessments. Category scales have been used for assessing plant disease severity in field experiments, epidemiological studies, and for screening germplasm. The most widely used category scale is the Horsfall-Barratt (H-B) scale, but reports show that estimates of disease severity using the H-B scale are less precise compared with nearest percent estimates (NPEs) using the 0 to 100% ratio scale. Few studies have compared different category scales. The objective of this study was to compare NPEs, the H-B midpoint converted data, and four different linear category scales (5 and 10% increments, with and without additional grades at low severity [0.1, 0.5, 1.0, 2.0, 5.0, 10.0, 15.0, 20.0…100%, and 0.1, 0.5, 1.0, 2.0, 5.0, 10.0, 20.0, 30.0…100%, respectively]). Results of simulations based on known distributions of disease estimation using the type II error rate (the risk of failing to reject H₀ when H₀ is false) showed that at disease severity ≤5%, a 10% category scale had a greater probability of failing to reject H₀ when H₀ is false compared with all other methods, while the H-B scale performed least well at 20 to 50% severity. The 5% category scale performed as well as NPEs except when disease severity was ≤1%. Both the 5 and 10% category scales with the additional grades included performed as well as NPEs. These results were confirmed with a mixed model analysis and bootstrap analysis of the original rater assessment data. A better knowledge of the advantages and disadvantages of category scale types will provide a basis for plant pathologists and plant breeders seeking to maximize accuracy and reliability of assessments to make an informed decision when choosing a disease assessment method.

The effect of rater bias and assessment method on hypothesis testing was studied for representative experimental designs for plant disease assessment using balanced and unbalanced data sets. Data sets with the same number of replicate estimates for each of two treatments are termed “balanced” and those with unequal numbers of replicate estimates are termed “unbalanced”. The three assessment methods considered were nearest percent estimates (NPEs), an amended 10% incremental scale, and the Horsfall-Barratt (H-B) scale. Estimates of severity of Septoria leaf blotch on leaves of winter wheat were used to develop distributions for a simulation model. The experimental designs are presented here in the context of simulation experiments which consider the optimal design for the number of specimens (individual units sampled) and the number of replicate estimates per specimen for a fixed total number of observations (total sample size for the treatments being compared). The criterion used to gauge each method was the power of the hypothesis test. As expected, at a given fixed number of observations, the balanced experimental designs invariably resulted in a higher power compared with the unbalanced designs at different disease severity means, mean differences, and variances. Based on these results, with unbiased estimates using NPE, the recommended number of replicate estimates taken per specimen is 2 (from a sample of specimens of at least 30), because this conserves resources. Furthermore, for biased estimates, an apparent difference in the power of the hypothesis test was observed between assessment methods and between experimental designs. Results indicated that, regardless of experimental design or rater bias, an amended 10% incremental scale has slightly less power compared with NPEs, and that the H-B scale is more likely than the others to cause a type II error. These results suggest that choice of assessment method, optimizing sample number and number of replicate estimates, and using a balanced experimental design are important criteria to consider to maximize the power of hypothesis tests for comparing treatments using disease severity estimates.