文献类型： 外文期刊

作者： Teng, Zixuan ¹ ; Chen, Jiawei ² ; Wang, Jian ³ ; Wu, Shuixiu ¹ ; Chen, Riqing ¹ ; Lin, Yaohai ¹ ; Shen, Liyan ² ; Jackson, Robert ⁵ ; Zhou, Ji ² ; Yang, Changcai ¹ ;

作者机构： 1.Fujian Agr & Forestry Univ, Digital Fujian Res Inst Big Data Agr & Forestry, Coll Comp & Informat Sci, Fuzhou 350002, Peoples R China

2.Nanjing Agr Univ, Acad Adv Interdisciplinary Studies, State Key Lab Crop Genet & Germplasm Enhancement, Nanjing 210095, Peoples R China

3.Ningxia Acad Agr & Forestry Sci, Yinchuan 750002, Peoples R China

4.Fujian Agr & Forestry Univ, Coll Mech & Elect Engn, Fuzhou 350002, Peoples R China

5.Natl Inst Agr Bot NIAB, Cambridge Crop Res, Cambridge CB3 0LE, England

6.Fujian Prov Univ, Fujian Agr & Forestry Univ, Key Lab Smart Agr & Forestry, Fuzhou 350002, Peoples R China

7.Fujian Agr & Forestry Univ, Ctr Agroforestry Mega Data Sci, Sch Future Technol, Fuzhou 350002, Peoples R China

期刊名称：PLANT PHENOMICS （ 影响因子：6.5； 五年影响因子：7.5 ）

ISSN： 2643-6515

年卷期： 2023 年 5 卷

页码：

收录情况： SCI

摘要： Rice (Oryza sativa) is an essential stable food for many rice consumption nations in the world and, thus, the importance to improve its yield production under global climate changes. To evaluate different rice varieties' yield performance, key yield-related traits such as panicle number per unit area (PNpM2) are key indicators, which have attracted much attention by many plant research groups. Nevertheless, it is still challenging to conduct large-scale screening of rice panicles to quantify the PNpM2 trait due to complex field conditions, a large variation of rice cultivars, and their panicle morphological features. Here, we present Panicle-Cloud, an open and artificial intelligence (AI)-powered cloud computing platform that is capable of quantifying rice panicles from drone-collected imagery. To facilitate the development of AI-powered detection models, we first established an open diverse rice panicle detection dataset that was annotated by a group of rice specialists; then, we integrated several state-of-the-art deep learning models (including a preferred model called Panicle-AI) into the Panicle-Cloud platform, so that nonexpert users could select a pretrained model to detect rice panicles from their own aerial images. We trialed the AI models with images collected at different attitudes and growth stages, through which the right timing and preferred image resolutions for phenotyping rice panicles in the field were identified. Then, we applied the platform in a 2-season rice breeding trial to valid its biological relevance and classified yield analysis between computational analysis and manual scoring, we found that the platform could quantify a useful toolkit to enable rice breeders to screen and select desired rice varieties under field conditions.