SBIR Phase I: Automated detection, classification, and motion tracking of precipitation and insects for agricultural, health, and transport safety applications.

SBIR 第一阶段：针对农业、健康和运输安全应用的降水和昆虫的自动检测、分类和运动跟踪。

• 批准号: 1940212
• 负责人: Allan Reaburn
• 金额: $ 22.4万
• 依托单位: Particle Flux Analytics, Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-12-15 至 2021-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1940212&HistoricalAwards=false
• 关键词: SBIR; Phase; Automated; detection; classification

The broader impact of this Small Business Innovation Research (SBIR) Phase I project is to address consumer weather, and agricultural information needs as an advanced precipitation sensor and as a fully automated detector and classifier of insect species. Information on hydrometeor rate and type is critical for transportation safety and weather prediction. Wet snow and freezing rain events count among the worst natural disasters in North American history; the proposed device will be the first with the unambiguous capability for providing reliable remote detection of particles in a semi-frozen state for transportation management. Further, insect number, species, and motion vectors are critical for targeted pesticide applications that prevent the spread of disease and crop damage while preventing off-target damages to vulnerable species, such as bees. The combined commercial damages from precipitation and agricultural pests in the United States exceed $100 billion annually. The proposed project will exploit recent advances in lighting, digital imaging, motion detection, and image processing to develop a new Small Airborne Particle Imager for obtaining high-resolution forward-illuminated photography of precipitation particles, as well as insects and their trajectories. The goal is to create a device capable of detecting and photographing objects moving up to 10 m/s at resolutions as fine as 0.01 mm. Primary components of the Phase I effort involve the integration of multi-spectral, multi-polarization photography and design of a novel motion detection system capable of providing 3D motion vectors of objects 0.1 mm across.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这个小型企业创新研究（SBIR）第一阶段项目的更广泛影响是解决消费者的天气和农业信息需求，作为先进的降水传感器和昆虫物种的全自动检测器和分类器。关于水凝速率和类型的信息对于运输安全和天气预报至关重要。湿雪和冻雨事件是北美历史上最严重的自然灾害之一;拟议的设备将是第一个具有明确能力的设备，可以为运输管理提供可靠的半冻结状态颗粒远程检测。此外，昆虫数量、物种和运动矢量对于有针对性的农药应用至关重要，这些农药应用可以防止疾病传播和作物损害，同时防止对脆弱物种（如蜜蜂）的脱靶损害。在美国，降水和农业害虫造成的商业损失每年超过1000亿美元。拟议的项目将利用照明，数字成像，运动检测和图像处理方面的最新进展，开发一种新的小型空中粒子成像仪，用于获得降水粒子以及昆虫及其轨迹的高分辨率前向照明摄影。目标是创造一种能够以0.01毫米的分辨率检测和拍摄移动速度高达10米/秒的物体的设备。第一阶段工作的主要组成部分涉及多光谱，多的偏振摄影和设计一种新型的运动检测系统，能够提供物体的3D运动矢量0.1毫米宽。这个奖项反映了NSF的法定使命，并已通过使用基金会的知识价值和更广泛的影响审查标准进行评估，认为值得支持。