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

小企业创新研究（SBIR）第一阶段项目的更广泛影响是提高作物产量，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，我们将能够更有效地管理害虫，使昆虫干预措施的应用能够在空间和时间上有针对性。例如，我们的系统可以建议在一天中的最佳时间在几个“热点”喷洒更温和（更便宜）的杀虫剂，而不是在整个田地中全面喷洒强效杀虫剂。减少杀虫剂的用量对整个社会有进一步的积极好处，它将减少污染，杀虫剂的使用被认为是气候变化和蜂群衰竭失调的一个因素。在这个项目中创建的硬件/算法/表示/数据模型有一个明显的应用蚊子监测，这对控制人类和牲畜的昆虫病媒疾病的影响。该SBIR第一期项目的商业潜力是显而易见的。昆虫每年破坏或摧毁价值约1500亿美元的农作物。如果我们将这一数字减少1%，我们就有一个十亿美元的市场。拟议中的项目将研究提高飞行昆虫分类技术的最新水平的技术，目标是生产一个平台，使昆虫监测能够用于精准农业。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），并使它们在该领域遇到的各种条件（温度，压力，湿度）下保持不变。该公司的研究表明，如果不创造这种不变性，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中的不确定性，并降低随机猜测的准确性。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飞行，这是众所周知的。然而，目前的模型将昆虫视为理想化的对象，使用航空航天方程的密度与升力，完全忽略了环境对昆虫生理的影响。该公司计划通过创建一个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。

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.