RI: Medium: Machine Learning for Agricultural and Medical Entomology

RI：媒介：农业和医学昆虫学的机器学习

• 批准号: 1510741
• 负责人: Eamonn Keogh
• 金额: $ 110万
• 依托单位: University of California-Riverside
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-10-01 至 2019-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1510741&HistoricalAwards=false
• 关键词: RI; Medium; Machine; Learning; Agricultural

This award will enable a team of computer scientists and entomologists at the University of California-Riverside to develop sensors and software that will allow the classification of flying insects. The ability to automatically and accurately classify flying insects has the potential to have significant impact human affairs, because insects spread disease, feed on crops and livestock, and ruin food stores, at a combined annual cost of billions of dollars and incalculable human suffering. The intellectual merit of the project is in producing algorithms, devices, and procedures that will radically expand the ability to conduct insect surveillance. Recent advances in sensor technology and machine learning and the ongoing revolution in Big Data are just beginning to enable development of advanced algorithms that will help usher in a new era of computational entomology. The investigators will build inexpensive devices that can detect and classify flying insects. For at least some genera of insects the resulting classification labels will go beyond species-level to predict sex and physiological states (such as virgin vs. gravid and newly emerged vs. mature) of individual insects. The investigators will create computational devices that can determine the origin of selected insects, and selectively capture targeted insects for downstream molecular diagnostic analysis. Producing such information will both accelerate basic research in entomology and will allow more effective vector control. The broader impacts of the project are inherent in the potential to significantly improve the quality and volume of insect surveillance, thus allowing more effective Integrated Vector Management. In the case of mosquitoes, more effective interventions are known to directly save lives. Resulting algorithms will allow the creation of systems to provide actionable information on multiple scales, from informing a policy committee to instructing an agricultural robot to open a valve. The projects comprehensive educational and outreach activities have already been piloted on a small scale and include detailed plans to reach out to underserved communities at the K-12 and college levels.

该奖项将使加州大学河滨分校的计算机科学家和昆虫学家团队能够开发能够对飞行昆虫进行分类的传感器和软件，自动、准确地对飞行昆虫进行分类的能力有可能对人类事务产生重大影响。 ，因为昆虫传播疾病，以农作物和牲畜为食，并破坏食品储存，每年总共造成数十亿美元的损失和无法估量的人类痛苦。该项目的智力价值在于生产算法、设备和程序，这些算法、设备和程序将从根本上解决这些问题。扩大执行能力传感器技术和机器学习的最新进展以及大数据的持续革命刚刚开始推动先进算法的开发，这将有助于开创计算昆虫学的新时代。研究人员将构建能够检测和分类的廉价设备。至少对于某些昆虫属来说，由此产​​生的分类标签将超越物种级别来预测个体昆虫的性别和生理状态（例如处女与怀孕以及新出现与成熟）。研究人员将创建计算设备。可以确定所选的来源昆虫，并有选择地捕获目标昆虫进行下游分子诊断分析，将加速昆虫学的基础研究，并将实现更有效的媒介控制，从而显着提高昆虫的质量和数量。昆虫监测，从而实现更有效的综合病媒管理。就蚊子而言，已知更有效的干预措施可以直接拯救生命。由此产生的算法将允许创建从通知政策委员会到指示等多种层面上可操作的信息。农业机器人该项目的综合教育和外展活动已经进行了小规模试点，其中包括覆盖 K-12 和大学级别服务不足的社区的详细计划。