摘要
玉米叶片性状对生长发育、遗传育种及功能基因解析研究具有重要意义,而传统的测量方式效率低、主观性强、测量性状少,已无法满足现代玉米研究的需求,为此提出一种基于时间序列的玉米叶片性状动态追踪技术。研究基于高通量作物表型平台,针对100份玉米品种资源,每间隔3 d获取8个玉米生长点图像;利用图像分割、叶片骨架提取等算法得到单片叶长、叶角度、叶弯曲度参数;基于叶片相对位置信息实现玉米叶片的动态追踪及标记。试验结果和人工测量值相比,叶长和叶夹角测量误差分别为0.92%和3.32%。叶片追踪可以得到叶片的动态变化过程,计算获取叶片长度的平均生长率及叶片弯曲度的变化分布。
Maize leaf traits have great significance to the study of growing development, breeding and functional gene research. However, the traditional method is inefficient, subjective, and also with less measurement, which is far from the requirement of maize-related research. Therefore, an automatic and dynamic technology for maize leaf traits extraction was proposed. Totally 100 maize varieties were adopted, and eight growth points were analyzed every three days based on the high-throughput crop phenotyping platform. For each measurement, the 18 side-view images were acquired every 10°, and the maximum side-view image was identified based on the width information. Then an improved segmentation method was applied to extract the complete plant binary image. After that a parallel thinning was used to extract the plant skeleton l and Hough transform was adopted to distinguish leaf skeleton from the stem. Finally, each leaf skeleton was labelled and the specific algorithm was developed to calculate the leaf length, angle and curvature. The experimental results showed that the measurement error for leaf length and leaf angle was 0. 92% and 3.32% , respectively, and the results demonstrated that this method had a higher consistency than manual method. Since the new leaf would always grow from above in maize, the leaf matching based on time series was designed and carried out by using the leaf relative-position information. With leaf registration, the leaf growth rate and leaf curvature variation were obtained. In general, this study provided a novel method for maize-related research.
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2017年第5期174-178,198,共6页
Transactions of the Chinese Society for Agricultural Machinery
基金
国家自然科学基金项目(31600287)
