期刊论文

Xu, SY

英文

FRONTIERS IN PLANT SCIENCE

1664-462X

2023

14

1188286

10.3389/fpls.2023.1188286

This research was funded by the National Key Research and Development Program of China (Program No. 2018YFD1000904) and the Fundamental Research Funds for the Central Universities (2662022GXYJ001).

本校为其他机构

In this study, we propose a high-throughput and low-cost automatic detection method based on deep learning to replace the inefficient manual counting of rapeseed siliques. First, a video is captured with a smartphone around the rapeseed plants in the silique stage. Feature point detection and matching based on SIFT operators are applied to the extracted video frames, and sparse point clouds are recovered using epipolar geometry and triangulation principles. The depth map is obtained by calculating the disparity of the matched images, and the dense point cloud is fused. The plant model of the w...