SBIR Phase I: Inexpensive Automatic Classification And Counting Of Insects To Enable Precision Agriculture

SBIR 第一阶段：廉价的昆虫自动分类和计数以实现精准农业

• 批准号: 1843998
• 负责人: Leslie Hickle
• 金额: $ 22.5万
• 依托单位: FARMSENSE 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: 2019-02-01 至 2019-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1843998&HistoricalAwards=false
• 关键词: SBIR; Phase; Inexpensive; Automatic; Classification

The broader impact of this Small Business Innovation Research (SBIR) Phase I project is in improving crop yields, while reducing the amount of pesticides used. By significantly improving the accuracy and timeliness of insect surveillance, we will allow more effective pest management, allowing the applications of insect interventions to be targeted in space and time. For example, rather than a blanket spraying of harsh pesticides across the entire field, our system could suggest spraying of a milder (and cheaper) pesticide in just a few "hot spots", at the optimal time of day. Reducing the volume of pesticides has further positive benefits to society at large, it will reduce pollution, and the use of pesticides has been implicated as a contributor to climate change and to colony collapse disorder. The hardware/algorithms/representations/data-models created in this project have an obvious application to mosquito surveillance, which has implications for control of insect vectored diseases of both humans and livestock. The commercial potential of this SBIR Phase I project is obvious. Insects damage or destroy about 150 billion dollars' worth of crops each year. If we prevent reduce this by just one percent, we have a billion-dollar market.The proposed project will investigate techniques to improve the state-of-the-art in flying insect classification, with the goal of producing a platform that allows insect surveillance for precision agriculture. In particular, we will take the current algorithms and representations (many of which were invented by the current PIs) and make them invariant to the wide range of conditions (temperature, pressure, humidity) encountered in the field. The company's research has shown that without creating such invariances, the variability induced by changing environment conditions will swamp the regularities in the features that are currently exploited by classification algorithms, and reduce the accuracy to random guessing. It is well understood how temperature, pressure, humidity effect air density, and how air density effects insect flight. However, the current models treat the insects as idealized objects using aerospace equations for density vs. lift and completely ignore the effects of the environment on insect physiology. The company plans to achieve this by creating a model that compensates for environmental conditions. To achieve these ambitious goals, they plan to use machine learning to learn the appropriate invariances and model corrections.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.

这项小企业创新研究（SBIR）第一阶段项目的广泛影响在于提高作物产量，同时减少农药的使用量。通过显著提高昆虫监测的准确性和及时性，我们将实现更有效的害虫管理，使昆虫干预措施的应用在空间和时间上都有针对性。例如，我们的系统可以建议在一天中的最佳时间，只在几个“热点”喷洒一种较温和（也较便宜）的农药，而不是在整个田地上大面积喷洒刺激性农药。减少农药用量对整个社会有进一步的积极效益，它将减少污染，农药的使用已被认为是气候变化和蜂群衰竭失调的一个因素。本项目中创建的硬件/算法/表示/数据模型明显适用于蚊子监测，这对控制人类和牲畜的昆虫病媒疾病具有重要意义。SBIR第一期工程的商业潜力是显而易见的。昆虫每年破坏或毁坏价值约1500亿美元的农作物。如果我们能减少百分之一，我们就有十亿美元的市场。拟议中的项目将研究提高飞行昆虫分类技术的最新技术，目标是建立一个平台，为精准农业提供昆虫监测。特别是，我们将采用当前的算法和表示（其中许多是由当前的pi发明的），并使它们对该领域遇到的各种条件（温度，压力，湿度）保持不变。该公司的研究表明，如果不创造这种不变性，由环境条件变化引起的可变性将淹没目前分类算法所利用的特征的规律性，并将准确性降低到随机猜测。温度、压力、湿度如何影响空气密度，以及空气密度如何影响昆虫的飞行，我们都很清楚。然而，目前的模型使用密度与升力的航空方程将昆虫视为理想化的对象，完全忽略了环境对昆虫生理的影响。该公司计划通过创造一种补偿环境条件的模型来实现这一目标。为了实现这些雄心勃勃的目标，他们计划使用机器学习来学习适当的不变性和模型校正。该奖项反映了美国国家科学基金会的法定使命，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。