Surveillance of panicle positions by unmanned aerial vehicle to reveal morphological features of rice

Autoři: Daisuke Ogawa aff001;  Toshihiro Sakamoto aff002;  Hiroshi Tsunematsu aff001;  Toshio Yamamoto aff001;  Noriko Kanno aff001;  Yasunori Nonoue aff001;  Jun-ichi Yonemaru aff001
Působiště autorů: Institute of Crop Science, National Agricultural and Food Research Organization, Tsukuba, Japan aff001;  Institute for Agro-Environmental Sciences, National Agriculture and Food Research Organization, Tsukuba, Japan aff002
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article


Rice plant architecture affects biomass and grain yield. Thus, it is important to select rice genotypes with ideal plant architecture. High-throughput phenotyping by use of an unmanned aerial vehicle (UAV) allows all lines in a field to be observed in less time than with traditional procedures. However, discrimination of plants in dense plantings is difficult, especially during the reproductive stage, because leaves and panicles overlap. Here, we developed an original method that relies on using UAV to identify panicle positions for dissecting plant architecture and to distinguish rice lines by detecting red flags attached to panicle bases. The plant architecture of recombinant inbred lines derived from Japanese cultivars ‘Hokuriku 193’ and ‘Mizuhochikara’, which differ in plant architecture, was assessed using a commercial camera-UAV system. Orthomosaics were made from UAV digital images. The center of plants was plotted on the image during the vegetative stage. The horizontal distance from the center to the red flag during the reproductive stage was used as the panicle position (PP). The red flags enabled us to recognize the positions of the panicles at a rate of 92%. The PP phenotype was related to but was not identical with the phenotypes of the panicle base angle, leaf sheath angle, and score of spreading habit. These results indicate that PP on orthomosaics could be used as an index of plant architecture under field conditions.

Klíčová slova:

Crops – DNA sequence analysis – Habits – Leaves – Panicles – principal component analysis – Quantitative trait loci – Rice


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2019 Číslo 10
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