AUTHOR=Zhao Biquan , Zhang Jian , Yang Chenghai , Zhou Guangsheng , Ding Youchun , Shi Yeyin , Zhang Dongyan , Xie Jing , Liao Qingxi 

TITLE=Rapeseed Seedling Stand Counting and Seeding Performance Evaluation at Two Early Growth Stages Based on Unmanned Aerial Vehicle Imagery

JOURNAL=Frontiers in Plant Science

VOLUME=Volume 9 - 2018

YEAR=2018

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2018.01362

DOI=10.3389/fpls.2018.01362

ISSN=1664-462X

ABSTRACT=The development of unmanned aerial vehicles (UAVs) and image processing algorithms for field-based phenotyping offers a non-invasive and effective technology to obtain plant growth traits such as canopy cover and plant height in fields. Crop seedling stand count in early growth stages is important not only for determining plant emergence, but also for planning other related agronomic practices. The main objective of this research was to develop practical and rapid remote sensing methods for early growth stage stand counting to evaluate mechanically seeded rapeseed (Brassica napus L.) seedlings. Rapeseed was seeded in a field by three different seeding devices. A digital single-lens reflex camera was installed on an UAV platform to capture ultrahigh resolution RGB images at two growth stages when most rapeseed plants had at least two leaves. Rapeseed plant objects were segmented from images of vegetation indices using typical Otsu thresholding method. With various shape features extracted from the objects, regression models of seedling stand count was established. Three row characteristics were calculated for seeding performance evaluation after crop row detection. Results demonstrated that shape features had strong correlations with ground-measured seedling stand count. The regression models achieved R-squared values of 0.845 and 0.867, respectively, for the two growth stages. The mean absolute errors of total stand count were 9.79% and 5.11% for the two respective stages. Moreover, the calculated row characteristics were demonstrated to be useful in recognizing areas of failed germination possibly resulted from skipped or ineffective planting. In summary, this study developed practical UAV-based remote sensing methods and demonstrated the feasibility of using the methods for rapeseed seedling stand counting and mechanical seeding performance evaluation at early growth stages.