NSF convergence accelerator track m: targeted insect sensing and control

This $649,999 Project Grant award from the National Science Foundation (NSF) under the Technology, Innovation, and Partnerships (CFDA 47.084) program supports Farmsense, Inc., a self-certified small disadvantaged business, in developing novel sensor technologies for precise insect pest monitoring and surveillance in agricultural settings. The project aims to create a system inspired by how bats detect and discriminate between different insects, allowing for tracking of insect pests down to the level of sex, species, and life stage. This critical information will enable more targeted and effective pest control interventions, reducing the need for harsh pesticides and associated environmental damage. The project involves an interdisciplinary effort spanning wireless sensing, machine learning, robotics, and agricultural science, with the goal of lowering labor and material costs for growers and improving the competitiveness of US agriculture. Initial metrics will assess the technological performance of the sensors and classification algorithms, followed by measuring reductions in crop loss and economic benefits to growers. The project is expected to be completed by December 31, 2024.

Abstract

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.- subawards are planned for this award.