SBIR Phase II: A Multimodal Sensor Platform for Automated Detection and Classification of Pest Insects

这个项目的更广泛的影响/商业潜力是巨大的。这项拟议的技术可以极大地提高各个农业行业病虫害管理项目的整体效果。拟议的系统不仅消除了人工检查捕虫器这一最费力和最可怕的活动之一，而且还提供了前所未有的访问准确的实时昆虫种群信息，以做出更有效的虫害管理决策。这导致减少了空间限制的农药使用，更好地了解了害虫的行为，并加强了生物防治。自动诱捕器？S可以监测不止一种昆虫，不仅实现了更高水平的虫害监测效率，而且为最终用户带来了倍增的技术？S价值。这项拟议技术的潜在市场相当广阔。事实上，这项技术可以使任何需要定期监测昆虫种群的行业受益。此外，该技术可用于各种州和联邦有害生物监测项目，如美国农业部管理的监测吉普赛飞蛾的慢传播(STS)项目。该小企业创新研究(SBIR)第二阶段项目建议开发一个多模式传感器平台，用于稳健地检测和分类多种害虫物种，用于生产农业中昆虫种群的自动监测。这个项目的主要目标有两个：第一，证明生物阻抗传感器本身就能为多种昆虫的分类提供有用的数据。基于生物阻抗数据提取的Mel倒谱系数的线性支持向量机分类器将在嵌入式平台上实现对两种害虫的检测和分类。这些基于生物阻抗的电子陷阱将进行大规模的现场试验，并为全面商业化做好准备。第二个目标是开发一种具有超声波、红外和生物阻抗传感器的多模式电子陷阱，可以同时监测四种或更多害虫。传感器产生的测量信号将被分析，以确定可以在嵌入式平台上计算用于实时处理的一组不同的特征。这些特征将被用于多模式传感器融合算法，以进行稳健的检测和分类。将研究不同的传感器融合策略，并评估每种融合算法在受控和现场条件下的性能。

The broader impact/commercial potential of this project is significant. The proposed technology could drastically improve the overall effectiveness of pest management programs in various agricultural industries. The proposed system not only eliminates one of the most laborious and dreaded activities of manually inspecting insect traps, but also provides unprecedented access to accurate, real-time insect population information to make more effective pest management decisions. This leads to reduced, spatially-restricted pesticide applications, better understanding of insect pest behaviors, and enhanced biological control. The automated trap?s ability to monitor more than one species of insect not only achieves a higher level of efficiency in pest monitoring but also multiplies the technology?s value to the end users. The potential market for the proposed technology is quite broad. In fact, the technology can benefit any industry that requires regular monitoring of insect populations. Furthermore, the proposed technology could be used for various state and federal pest monitoring programs, such as the Slow the Spread (STS) Project administered by USDA to monitor gypsy moths.This Small Business Innovation Research (SBIR) Phase 2 project proposes to develop a multi-modal sensor platform for robust detection and classification of multiple insect pest species for automated monitoring of insect populations in production agriculture. The main goal of this project is two-fold: First is to demonstrate that bio-impedance sensor alone provides useful data to classify multiple insect species. A linear support vector machine classifier using mel-frequency cepstral coefficients extracted from bio-impedance data will be implemented on an embedded platform for detection and classification of two insect pest species. These bio-impedance based electronic traps will undergo large-scale field trials and be prepared for full commercialization. The second objective is to develop a multi-modal electronic trap with ultrasound, infrared and bio-impedance sensors that can simultaneously monitor four or more insect pest species. Measurement signals generated by the sensors will be analyzed to determine a set distinct features that can be computed on an embedded platform for real-time processing. These features will then be used in multi-modal sensor fusion algorithms for robust detection and classification. Different sensor fusion strategies will be investigated and the performance of each fusion algorithm will be evaluated both in controlled and field conditions.