Fast anther dehiscence status recognition system established by deep learning to screen heat tolerant cotton.

ABSTRACT:

Background

From an economic perspective, cotton is one of the most important crops in the world. The fertility of male reproductive organs is a key determinant of cotton yield. Anther dehiscence or indehiscence directly determines the probability of fertilization in cotton. Thus, rapid and accurate identification of cotton anther dehiscence status is important for judging anther growth status and promoting genetic breeding research. The development of computer vision technology and the advent of big data have prompted the application of deep learning techniques to agricultural phenotype research. Therefore, two deep learning models (Faster R-CNN and YOLOv5) were proposed to detect the number and dehiscence status of anthers.

Result

The single-stage model based on YOLOv5 has

SUBMITTER: Tan Z 

PROVIDER: S-EPMC9026675 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature