NSF Convergence Accelerator Track M: Targeted Insect Sensing and Control

在美国，害虫每年破坏或毁坏价值数十亿美元的食物。控制这些害虫的努力（通常是通过地毯式喷洒刺激性杀虫剂）既昂贵又对环境有害。几十年来，种植者一直在尝试自然生物防治，包括释放目标害虫的天敌/寄生虫，释放信息素来迷惑雄性，从而大大降低它们寻找配偶的成功率。然而，这两种想法都需要精确的昆虫监测，知道哪些昆虫在野外的什么地方。获取这类信息的成本和难度意味着生物防治不具有成本效益，因此很少使用。该项目将开发和测试昆虫监测的新方法。这种方法是基于自然界最好的昆虫传感器之一蝙蝠。蝙蝠可以在相对较远的距离上发现并区分各种昆虫。这种传感器可以监测昆虫的性别、种类和生命阶段，并提供昆虫最活跃的关键信息。这一信息对于拟定干预措施的精确时机非常重要。该项目更广泛的影响将包括减少种植者的劳动力和材料成本，使美国农业更具竞争力。此外，通过减少对刺激性杀虫剂的需求，该项目旨在减少对有益传粉媒介和环境的损害。该项目是高度跨学科的，涵盖无线传感、云计算、机器学习、机器人、昆虫学、统计学、农业经济学和符号化学。最初将使用直接的计算指标来衡量进展，例如传感器的信噪比和分类算法的错误率。中间评价将衡量虫害压力和作物损失的减少。衡量成功的最终标准是经济和行为：该系统是否为种植者降低了成本，他们是否愿意采用新技术？该项目将与加州中央山谷的坚果种植者一起测量这些终极指标。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。

Insect pests damage or destroy billions of dollars’ worth of food in the USA each year. Efforts to control such pests (often by blanket spraying of harsh pesticides), are expensive and harmful to the environment. For decades, growers have experimented with natural biocontrols, including the release of natural predators/parasites of the targeted pests, and the release of pheromones to confuse the males and drastically reduce their success in finding a mate. However, both ideas require accurate insect surveillance, knowing which insects are where in the field. The cost and difficulty of obtaining such information has meant that biocontrols are not cost effective and are thus rarely used. This project will develop and test novel methods for insect surveillance. The method is based loosely on how one of nature’s best insect sensors, bats. Bats can detect and discriminate between various types of insects from a relatively large distance. The proposed sensors will allow the surveillance of insects down to the level of sex, species, and life stage, and will provide critical information about when the insects are most active. This information is important for the precise timing of the proposed interventions. The project’s broader impacts will include reduced labor and material costs for growers, making US agriculture more competitive. Moreover, by reducing the need for harsh pesticides, the project seeks to reduce damage to beneficial pollinators and the environment.The project is highly interdisciplinary, spanning wireless sensing, cloud computing, machine learning, robotics, entomology, statistics, agricultural economics and semiochemistry. Progress will initially be measured with direct computational metrics, such as the signal-to-noise ratio of the sensor, and the error-rate of the classification algorithms. Intermediate evaluation will measure the insect pest pressure and the reduction in crop loss. The ultimate metrics of success are economic and behavioral: Does the system reduce costs for the grower, and are they willing to adopt the new technology? The project will measure these ultimate metrics with tree nut growers in California central valley.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.