Receiver Operating Characteristic curve analysis determines association of individual potato foliage volatiles with onion thrips preference, cultivar and plant age

Abstract

Tomato spotted wilt virus (TSWV) causes sporadic but serious disease in Australian potato crops. TSWV is naturally spread to potato by thrips of which Thrips tabaci is the most important. Prior studies indicated possible non-preference of potato cultivars to T. tabaci. Select potato cultivars were assessed for non-preference to T. tabaci in paired and group choice trials. Cultivars ‘Bismark’, ‘Tasman’ and ‘King Edward’ were less preferred than ‘Atlantic’, ‘Russet Burbank’ and ‘Shepody’. Green leaf volatiles were sampled using solid-phase microextraction from the headspace of potato cultivars of two ages that differed in T. tabaci preference. Analysis of headspace volatile data using Receiver Operating Characteristic curves identified individual volatiles associated with T. tabaci preference and non-preference, young and old plants and individual cultivars. These data could be used to inform breeding programs for selection of T. tabaci resistance to assist with TSWV management, and biological testing of novel thrips management compounds.

Thrips as the Transmission Bottleneck for Mixed Infection of Two Orthotospoviruses

“…Our study suggested that adult female thrips could use olfactory cues to select plants for their offspring. Solid-phase microextraction (SPME) is considered to be a useful tool to get a realistic picture of the volatiles emitted from fresh tomato [49], and it was used to study volatiles from TSWV-infected plants [49][50][51]. We used SPME fibers to collect volatile compounds from mixed-infected plants and compared the collected compounds with those emitted by healthy or single virus-infected plants.…”

Automatic image annotation system using deep learning method to analyse ambiguous images

“…By dividing all observations that are part of the actual class by the percentage of observations that were correctly predicted to be positive, recall-also known as sensitivity-is calculated. [34].…”

Assessment of Status of Climate Change and Determinants of People’s Awareness to Climate-Smart Agriculture: A Case of Sarlahi District, Nepal

“…. ROC analysis is currently widely used in nearly all fields of science, including artificial intelligence [37], and in agriculture [39,40]. e ROC provides a measure of how well the fitted model distinguishes true cases from true noncases.…”

“…e ROC provides a measure of how well the fitted model distinguishes true cases from true noncases. e AUROC can also be interpreted as the average probability of correctly visualizing a positive case across all possible cutoff points of the predictor [40]. e value of the area under the ROC curve ranges between 0 and 1, which can be expressed as a percentage.…”