The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to enable holistic farm management by assisting ranchers/farmers in making more informed decisions about when and where to graze their livestock while minimizing negative environmental impacts through regenerative farming. The innovation enables monitoring of animal health in real time to assist in disease control and prevention. Poorly managed livestock systems have resulted in a variety of environmental and health problems. The project, which includes innovation in the Internet of Things, sensors, machine learning, and wireless communications, when applied to farming practices, seeks to alter how ranchers and agricultural businesses manage farms in terms of animal health, the environment, productivity, and farm economics. The innovation may have an impact on agricultural efficiency and cost reduction. Farmers will be able to track and regulate animal movement, classify animal activity and behavior, and predict and monitor animal health in real time using data collected by the platform's sensors. Early detection of health impacts enables preventive or early-stage treatments, as well as disease control and prevention on a broader scale.This SBIR Phase I project seeks to develop an Internet of Things (IoT) platform capable of tracking a variety of data in real time, including animal movement (e.g., location and direction), animal activity (walking, grazing, etc.), animal behavior, and animal health. These sensor data are ideal for monitoring and evaluating a range of variables critical to animal health protection. The proposed animal management system is transformational because it is capable of gathering precise multi-sensor data while using minimal power, communicating data wirelessly, and running for an extended period of time. These capabilities will gather real-time data on the animal's movement, activity, and health, enabling accurate analysis, characterization, and monitoring of the animals. The proposed research and development activities will focus on optimizing intertial measurement unit (IMU) sensors and other components for efficient animal control, characterization of animal activity and behavior, and development of machine learning models for real time monitoring of animal health.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.
