BREAD PHENO: High-Throughput Phenotyping with Smart Phones. #phenoApps

BREAD PHENO：使用智能手机进行高通量表型分析。

• 批准号: 1543958
• 负责人: Jesse Poland
• 金额: $ 158.28万
• 依托单位: Kansas State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-01 至 2020-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1543958&HistoricalAwards=false
• 关键词: BREAD; PHENO; Throughput; Phenotyping; Smart

Food and nutritional security will be a grand challenge in the coming decades. The global population is expected to increase to over 9 billion and food demand will grow by more than 50%. Currently, there are 2 billion people worldwide living in poverty, mostly relying on subsistence agriculture in developing countries. While poverty and food insecurity is a complex issue, the development of improved climate-resilient, high yielding and nutritious plant varieties is a critical part of improving food security, increasing income and economic welfare. To address this challenge, innovative approaches are needed to speed up the development of improved plant varieties. In plant breeding and genetics, precise measurements of plant characteristics are needed to accurately determine the effect of important genes and to identify and select the most promising candidate plant varieties. There has been limited technology development in this area, particularly for traits measured in field trials where most measurements are still taken and recorded by hand. This project will develop mobile applications (apps) for measuring plant traits that can be deployed on inexpensive and readily available mobile devices. Initial testing and deployment through collaboration with cassava and wheat breeders will enable rapid dissemination and broad usability. Middle-school and high-school students will also be engaged to test and use the apps to explore plant growth and measure plant traits. Equipping thousands of plant breeders around the world with tools for rapid measurement and analysis of important plant traits will provide the foundation for accelerated development of improved plant varieties that will ultimately result in increased productivity, food security, nutrition and income of smallholder farmers and their families in developing countries. Over the past decade, the availability of genomic data has exploded while the methods to collect phenotypes have made minimal advancements. This has led to a dramatic imbalance in data sets connecting genotype to phenotype and highlights phenotyping as the remaining major bottleneck in plant breeding programs. This project will advance the field of 3D graphics and modeling, data mining and deep learning through integration of simultaneous ground truth phenotypic measurements and imaging with mobile technology. Building on the success of Field Book (www.wheatgenetics.org/field-book), user-friendly mobile apps for field-based high-throughput phenotyping (HTP) will be developed and deployed. This project will converge novel advances in image processing and machine vision to deliver mobile apps through established breeder networks. Novel image analysis algorithms will be developed to model and extract plant phenotypes. A robust development pipeline will be assisted by 1) real-time field testing through breeding collaborators around the world and 2) middle-school and high-school students using the apps to explore plant growth and quantitative differences under genetic control. To ensure both immediate, broad deployment and functionality on a diverse set of crops, breeder networks for cassava and wheat will be engaged, providing a diverse set of target plant phenotypes, environments, breeding programs and working cultures. By combining data from research programs with ground truth breeder knowledge, this project will lay the foundation for collecting training sets that can subsequently be used to extract and quantify complex phenotypes using deep learning. Open-source apps for smartphones and tablets will consist of both software and documentation so that users will be able to understand how to use the apps. Apps will be distributed through online app stores (Windows Store, iTunes App Store, Google Play), through project websites, and via collaborative plant breeding networks. The resulting source code will be hosted in a public GitHub repository with a GNU General Public License (GPL) open-source license.

粮食和营养安全将是今后几十年的重大挑战。 全球人口预计将增加到90亿以上，粮食需求将增长50%以上。 目前，全世界有20亿人生活在贫困之中，大多数人依赖发展中国家的自给农业。 虽然贫穷和粮食不安全是一个复杂的问题，但开发更好的适应气候变化、高产和营养的植物品种是改善粮食安全、增加收入和经济福利的一个关键部分。 为了应对这一挑战，需要采取创新办法，加快开发改良植物品种。 在植物育种和遗传学中，需要对植物性状进行精确测量，以准确确定重要基因的作用，并识别和选择最有前途的候选植物品种。 这一领域的技术发展有限，特别是在田间试验中测量的性状，大多数测量仍然是手工进行和记录的。 该项目将开发用于测量植物性状的移动的应用程序（app），这些应用程序可以部署在廉价且易于获得的移动的设备上。 通过与木薯和小麦育种者合作进行初步测试和部署，将能够迅速传播和广泛使用。 初中和高中学生也将参与测试和使用应用程序来探索植物生长和测量植物性状。 为世界各地数以千计的植物育种者提供快速测量和分析重要植物性状的工具，将为加速开发改良植物品种奠定基础，最终提高发展中国家小农及其家庭的生产力、粮食安全、营养和收入。在过去的十年中，基因组数据的可用性已经爆炸，而收集表型的方法却取得了最小的进步。 这导致了连接基因型与表型的数据集的严重不平衡，并突出了表型作为植物育种计划中的剩余主要瓶颈。 该项目将通过将地面实况表型测量和成像与移动的技术相结合，推进3D图形和建模、数据挖掘和深度学习领域。 在Field Book（www.wheatgenetics.org/field-book）成功的基础上，将开发和部署用户友好的移动的应用程序，用于基于现场的高通量表型分析（HTP）。 该项目将融合图像处理和机器视觉方面的新进展，通过已建立的育种者网络提供移动的应用程序。 将开发新的图像分析算法来建模和提取植物表型。 一个强大的开发管道将得到以下方面的帮助：1）通过世界各地的育种合作者进行实时田间测试，2）初中和高中学生使用应用程序探索遗传控制下的植物生长和数量差异。 为了确保在各种作物上的即时、广泛部署和功能，木薯和小麦的育种者网络将参与进来，提供各种目标植物表型、环境、育种计划和工作文化。通过将研究项目的数据与基础育种知识相结合，该项目将为收集训练集奠定基础，这些训练集随后可用于使用深度学习提取和量化复杂的表型。智能手机和平板电脑的开源应用程序将包括软件和文档，以便用户能够了解如何使用这些应用程序。应用程序将通过在线应用程序商店（Windows Store、iTunes App Store、Google Play）、项目网站和植物育种协作网络分发。生成的源代码将托管在具有GNU通用公共许可证（GPL）开源许可证的公共GitHub存储库中。

项目成果

PhenoApps: Integrating Free Mobile Apps into Specialty Crop Breeding and Horticultural Programs

PhenoApps：将免费移动应用程序集成到特种作物育种和园艺计划中

• 发表时间: 2018
• 期刊: American Society for Horticultural Sciences
• 作者: Rife, T. and

Augmented reality for high-throughput phenotyping

用于高通量表型分析的增强现实

• 发表时间: 2019
• 期刊: 17th International Conference on Scientific Computing
• 作者: Wu, Shanshan;Neilsen, Mitchell L.
• 通讯作者: Neilsen, Mitchell L.

A Seed Segmentation Contour Generator and Counter

种子分割轮廓生成器和计数器

• 发表时间: 2018
• 期刊: 31st International Conference on Computer Applications in Industry and Engineering
• 作者: Courtney, Chaney;Neilsen, Mitchell
• 通讯作者: Neilsen, Mitchell

PhenoApps: Open source apps for plant breeding

PhenoApps：植物育种开源应用程序

• 发表时间: 2018
• 期刊: National Association of Plant Breeders 2018 Annual Meeting
• 作者: Rife, T. and

Vetting Anti-patterns in Java to Kotlin Translation

审查 Java 中的反模式到 Kotlin 的翻译

• 发表时间: 2019
• 期刊: Proceedings of 34th International Conference on Computers and Their Applications
• 作者: Courtney, Chaney;Neilsen, Mitchell
• 通讯作者: Neilsen, Mitchell