YOLO-v8 for verticillium disease phenotyping for cotton breeding under complex field background condition

Abstract

The soil-borne disease Verticillium wilt causes significant yield losses in cotton. Developing resistant cotton varieties is a long-term solution to manage the disease. The phytopathological parameter used to assess variety resistance is the presence or absence of infection, where transverse sectioning of infected stems is discoloured/brown. The traditional method of selecting resistant varieties involves manual scoring of visual stem discolouration after cutting. However, this process is associated with high labour costs, delayed processing time and cognitive biases. Therefore, automatic detection of resistant varieties with scalable, cost-effective, and rapid phenotyping tools is needed in cotton breeding.

Plant phenotyping relevance

YOLO-v8を用いて綿のVerticillium病抵抗性を自動評価する画像ベース表現型計測が主題であり、育種における従来の目視判定を代替する方法開発に該当する。

Code and data availability

The paper describes ~7,200 annotated cotton stem images and YOLO-v8 models, but no public deposit of the dataset, annotations, or authors' code is stated. The only URL (github.com/ultralytics/ultralytics) is a cited third-party library, not a paper-specific asset.

No evidence-backed public reproduction asset is currently recorded.