SBIR Phase I: Tool to estimate object visibility to fish in complex aquatic environments.

SBIR 第一阶段：估计复杂水生环境中鱼类的物体可见度的工具。

• 批准号: 1913419
• 负责人: Michael Mrozik
• 金额: $ 22.5万
• 依托单位: BassInSight, 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: 2019-08-01 至 2020-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1913419&HistoricalAwards=false
• 关键词: SBIR; Phase; Tool; estimate; object

The broader impact of this Small Business Innovation Research (SBIR) Phase I project is to develop tools that will create vast amounts of water quality data for the nation's inland, fresh waters. The data allow for direct environmental monitoring and for validation of other remote sensing GIS techniques to measure inland water quality. Algorithms will also be developed to use the data to allow anglers to customize their selection of lures based on the time and location where they are fishing. The difficulty for the angler is anticipating which lures are most visible to the fish, because fish see differently than humans. This project develops software tools and water quality sensors that allow anglers to customize their fishing strategies for any type of water. The product will ultimately recommend lures that work well for a given location based on the properties of the water, the properties of the lures, and the visual capabilities of fish in that environment.The proposed project will measure the optical properties of (a) lakes and rivers. First-generation photometers would be constructed to estimate the sub-surface transmission and light-levels at three wavelengths and capture relevant water quality data. The project also proposes new methods to accommodate the selection of fluorescent pigments on lures. Algorithms will use the data to estimate the visual contrasts as perceived by certain species of fish and determine which lures have the highest probability of success for that species - bass in the first instance. The novelty of this approach is that it relies on estimating critical lighting parameters at three wavelength bands. Traditional methods rely on spectrometers that estimate spectra with thousands of data points and are not feasible for anglers. This project directly tests whether 3 well-spaced data points can accurately estimate lighting environments, visual signals, and their transmission in diverse water bodies.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.

这个小企业创新研究（SBIR）第一阶段项目的更广泛的影响是开发工具，为国家的内陆，淡水沃茨创建大量的水质数据。这些数据可用于直接环境监测和验证其他遥感地理信息系统技术，以测量内陆水质。还将开发算法，利用这些数据，让垂钓者根据他们钓鱼的时间和地点定制他们对鱼饵的选择。垂钓者的困难是预测哪些诱饵对鱼来说是最明显的，因为鱼的视觉与人类不同。该项目开发软件工具和水质传感器，使垂钓者能够为任何类型的水定制他们的捕鱼策略。该产品最终将根据水的性质、诱饵的性质以及该环境中鱼类的视觉能力推荐适合特定位置的诱饵。拟议项目将测量（a）湖泊和河流的光学性质。将建造第一代光度计，以估计三个波长的次表层透射率和光照水平，并采集相关的水质数据。该项目还提出了新的方法，以适应诱惑的荧光颜料的选择。算法将使用这些数据来估计某些鱼类所感知的视觉对比，并确定哪些诱饵对该物种的成功概率最高-首先是鲈鱼。这种方法的新奇之处在于，它依赖于估计三个波长带的关键照明参数。传统的方法依赖于光谱仪，估计光谱与数千个数据点，是不可行的垂钓者。该项目直接测试了3个间隔良好的数据点是否可以准确地估计照明环境、视觉信号及其在不同水体中的传输。该奖项反映了NSF的法定使命，并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，被认为值得支持。