Panicle-Cloud:An Open and Al-Powered Cloud Computing Platform for Quantifying Rice Panicles from Drone-Collected Imagery to Enable the Classification of Yield Production in Rice
作者机构：Digital Fujian Research Institute of Big Data for Agriculture and ForestryCollege of Computer and Information SciencesFujian Agriculture and Forestry UniversityFuzhou 350002China State Key Laboratory of Crop Genetics&Germplasm Enhancementacademy for Advanced Interdisciplinary StudiesNanjing Agricultural UniversityNanjing 210095China Ningxia Academy of Agriculture and Forestry SciencesYinchuan 750002China College of Mechanical and Electrical EngineeringFujian Agriculture and Forestry UniversityFuzhou 350002China Cambridge Crop ResearchNational Institute of Agricultural Botany(NIAB)Cambridge CB3 OLEUK Key Laboratory of Smart Agriculture and Forestry(Fujian Agriculture and Forestry University)Fujian Province UniversityFuzhou 350002China Center for Agroforestry Mega Data ScienceSchool of Future TechnologyFujian Agriculture and Forestry UniversityFuzhou 350002China
出 版 物：《Plant Phenomics》 (植物表型组学（英文）)
年 卷 期：2023年第5卷第4期
页 面：905-916页
核心收录：
基 金：supported by the National Natural Science Foundation of China(under grant nos.32070400,62171130,61972093,and 61802064) in part by the Fujian University Industry University Research Joint Innovation Project under grant 2022H6006 in part by the Fujian Science and Technology Planning Project under grant 2021S0007 Drone-based phenotypic analysis and yield prediction were supported by the National Natural Science Foundation of China(32070400 to J.Z.) Both J,Z.and R.J.were partially supported by the United Kingdom Research and Innovation's(UKRI)Biotechnology and Eiological Sciences Research Council's(BBSRC)International Partnership Grant(BB/X511882/1)
主 题：breeding cultivar hundreds
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 *** evaluate different rice varieties yield performance,key yield-related traits such as panicle number per unit area(PNpM^(2))are key indicators,which have attracted much attention by many plant research ***,it is still challenging to conduct large-scale screening of rice panicles to quantify the PNpM^(2)trait due to complex field conditions,a large variation of rice cultivars,and their panicle morphological ***,we present Panicle-Cloud,an open and artificial intelligence(AI)-powered cloud computing platform that is capable of quantifying rice panicles from drone-collected *** facilitate the development of Al-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 *** trialed the Al 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 ***,we applied the platform in a 2-season rice breeding trial to valid its biological relevance and classified yield production using the platform-derived PNpM^(2)trait from hundreds of rice *** correlation analysis between computational analysis and manual scoring,we found that the platform could quantify the PNpM^(2)trait reliably,based on which yield production was classified with high ***,we trust that our work demonstrates a valuable advance in phenotyping the PNpM^(2)trait in rice,which provides a useful toolkit to enable rice br
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