Image Filtering to Improve Maize Tassel Detection Accuracy Using Machine Learning Algorithms.

ABSTRACT: Unmanned aerial vehicle (UAV)-based imagery has become widely used to collect time-series agronomic data, which are then incorporated into plant breeding programs to enhance crop improvements. To make efficient analysis possible, in this study, by leveraging an aerial photography dataset for a field trial of 233 different inbred lines from the maize diversity panel, we developed machine learning methods for obtaining automated tassel counts at the plot level. We employed both an object-based counting-by-detection (CBD) approach and a density-based counting-by-regression (CBR) approach. Using an image segmentation method that removes most of the pixels not associated with the plant tassels, the results showed a dramatic improvement in the accuracy of object-based (CBD) detection, with the c

SUBMITTER: Rodene E 

PROVIDER: S-EPMC11013961 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature