上海澤泉科技股份有限公司多年來秉承推進中國生態(tài)環(huán)境改善、農業(yè)興國的理念，服務涉及植物表型育種，植物生理生態(tài)，水文水利，農業(yè)工程等領域的科研和。為更好地服務全國科研用戶，促進植物表型育種、表型技術推廣，同時促進相關研究設施和平臺的建設，上海澤泉科技股份有限公司將于2018年3月16日下午在上海孫橋現代農業(yè)園區(qū)AgriPheno高通量植物表型平臺舉辦“2018澤泉植物表型技術Workshop”。Workshop內容包括植物表型研究技術研究進展交流、AgriPheno高通量植物表型平臺及科研項目介紹以及平臺參觀考察。

現向各單位植物研究、農業(yè)建設領域科研人員發(fā)出誠摯邀請，歡迎您出席本次workshop與參會者交流領域內的科研進展，期待您的光臨。

一、主辦單位：上海澤泉科技股份有限公司

二、會議時間與地點

時間：2018年3月16日下午

地點：上海乾菲諾農業(yè)科技有限公司（AgriPheno高通量植物表型平臺），上海市浦東新區(qū)沔北路185號孫橋現代農業(yè)園C9-1

三、會議日程

四、參會須知

1、參會回執(zhí)：請參會人員于3月14日前將參會回執(zhí)通過電子郵件發(fā)送至：vivi.xu@zealquest.com，或傳真。我們將根據參會回執(zhí)協助推薦住宿和安排參會事宜。掃描/點擊二維碼，填寫信息亦可參會。

2、Workshop費用：參會免費。交通、食宿自理。

五、會務組

：徐靜萍，：vivi.xu@zealquest.com，：  分機：8043

地址：上海市普陀區(qū)金沙江路1038號華大科技園2號樓8層  ：200062

六、附件

附件1：2018澤泉植物表型技術Workshop 參會回執(zhí)

附件2：會場交通

附件3：報告摘要

上海澤泉科技股份有限公司

2018年3月12日

附件1：2018澤泉植物表型技術Workshop 回執(zhí)

請于3月14日前將參會回執(zhí)通過電子郵件發(fā)送至：vivi.xu@zealquest.com，或傳真發(fā)送至。

附件2：會場交通

上海乾菲諾農業(yè)科技有限公司

地址：上海市浦東新區(qū)沔北路185號孫橋現代農業(yè)園C9-1

交通：地鐵16號線羅山路站，2號線廣蘭路站下車，我司安排車輛接送。具體信息可在百度地圖中搜索“上海乾菲諾農業(yè)科技有限公司”。

附件3：報告摘要

● Plant Phenomics and Image Analysis (植物表型組學與圖像分析)

主講Ji Zhou, 周濟，英國BBSRC Earlham Institute，University of East Anglia, & 南京農業(yè)大學表型交叉研究中心

With the maturation of high-throughput and low-cost genotyping platforms, the current bottleneck in breeding, c*tion and crop research lies in phenotyping and phenotypic analyses. Recent phenotyping technologies invented by industry and academia are capable of producing large image- and sensor-based data. However, how to effectively transform big data into biological knowledge is an immense challenge that urgently requires a cross-disciplinary effort. In the talk, I will introduce our research-based phenotyping platforms at Norwich Research Park, ranging from the sky to cells, including AirSurf (automated aerial analytic software), Phenospex (an in-field 3D laser scanning platform), CropQuant (a low-cost distributed crop monitoring system), SeedGerm (a machine-learning based seed germination device), Leaf-GP (an open-source software for quantifying growth phenotypes), and high content screening systems for cellular phenotype measurements. Through these examples, I will introduce our multi-scale phenomics solutions developed for different biological questions on bread wheat, brassica, and other plant species, including linking phenotypic analyses to the assessment of genes controlling performance-related traits, QTL analysis of yield potential, gene discovery using near isogenic lines (NILs), quantifying genotype-by-environment interactions (GxE) to assess environmental adaptation, etc. I will also talk about how to utilise open scientific and numeric libraries for data calibration, annotation, image analysis and phenotypic analyses.

● Remote Sensing and IoT for Phenomics（遙感和物聯網技術在表型研究中的應用）

主講Daniel Reynolds（周濟實驗室, 英國BBSRC Earlham Institute）

A high-level overview of remote sensing, Internet of Things (IoT) and how they are applied to Plant Phenomics. Latest remote sensing and IoT provide high-resolution and high-frequency environmental measurements when compared to traditional manual methods. Distributed sensor networks such as the CropQuant platform allow researchers to record the environment of in-field or indoor experiments without manual intervention, which allow the capture of dynamic environmental changes throughout key growing stages. The lecture will introduce the techniques and applications of IoT and remote sensing in plant phenomics, covering (1) what is IoT with respect to sensing networks, (2) the hardware available and suitable for IoT including digital and analogue sensors, (3) single-board computers and microcontrollers, (4) control software and interfacing with IoT devices, (5) data transmission and retrieval, and finally (6) the management of multiple devices and collation of remote data. The lecture will not cover technical details and mainly focus on the introduction of how remote sensing and IoT could be used for phenomics.

● Machine Learning for Plant Phenomics (機器學習在植物表型中的應用)

主講 Aaron Bostrom （周濟實驗室, 英國BBSRC Earlham Institute）

An introduction to machine learning and how to apply it in plant phenomics. Machine learning is a tool that has been gaining attention due to many advances in the last decade. This talk aims to provide a summary of machine learning techniques, simple and intuitive explanations and demonstrations about how machine learning has been applied to different real-world problems. In particular, generalisation and how to design training datasets and experimentation with machine learning in mind will be explained. The lecture will finish with some of Aaron’s current and previous work, and where machine learning have been applied to real world problems such as our AirSurf on lettuces yield prediction as well as SeedGerm software on seed germination measurements together with industrial leaders such as G’s Growers and Syngenta.

● Engineering Cost-effective Inligent Phenotyping Complete Set Instrumentation/facilities for precise crop breeding (大宗作物表型篩選育種成套裝備、儀器與系統)

主講Liang Gong，貢亮，上海交通大學

It plays an important role for high-throughput phenotyping in cutting-edge crop breeding field, and this automation generates heterogeneous measuring data for subsequent meta-analyses, modeling, and ground-truth dataset building. Traditional researches focus on an individual instrument or data processing algorithms. We advocate that the crop breeeding issue has to be addressed with a systematic paradigm, ranging from building cost-effective infrastructure to leveraging crowd-sourcing applications, and to process standardization.The roadmap for conducting phenotyping-based breeding is depicted as, first, plant organ-specific phenotyping parameter index sets for crop breeding are optimally determined, and corresponding phenotyping instrumentation are introduced. Second, an entity-relationship data aggregation model is built to organize and present the phenotyping big data; Third, a paradigm of creating a phenotyping database is proposed to facilitate crop breeding. Finally, a formal GPEM database for constructing a crop breeding phenotyping database is established, which highlights the plant morphometric data retrieval and data mining. This data aggregation scheme provides an effective tool and exemplary template for dealing with big plant phenotyping data acquired by different devices and equipment under user-defined resolution. The case study for creating a GPEM phenotyping database is step-by-step investigated to show the feasibility and effectiveness of plant phenotyping big-data aggregation.