STTR Phase I: Using Audio Analytics and Sensing to Enhance Broiler Chicken Welfare and Performance by Continuously Monitoring Bird Vocalizations

STTR 第一阶段：使用音频分析和传感，通过持续监测鸡的发声来提高肉鸡的福利和性能

• 批准号: 2335590
• 负责人: Tom Darbonne
• 金额: $ 27.5万
• 依托单位: AUDIOT, INC.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-03-15 至 2025-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2335590&HistoricalAwards=false
• 关键词: STTR; Phase; Using; Audio; Analytics

The broader impact of this Small Business Technology Transfer (STTR) Phase I project will be in enhancing the well-being of chickens on poultry farms and in equipping growers with effective tools to monitor bird conditions. As chicken is a widely consumed source of live-animal protein globally, there is a growing consumer preference for ethically raised animals. The project addresses this demand by fostering improved welfare practices in poultry farming. There are collaborative movements with major producers and institutional consumers to establish evidence-based welfare standards impacting entire supply chain. With a declining agricultural workforce in the United States, it is essential to have automated mechanisms to extend a farmer’s capabilities. This project will develop a smart monitoring system for the birds meeting these needs, resulting in improved bird welfare and amplification of the farmer’s capacity. This Small Business Technology Transfer (STTR) Phase I project uses audio monitoring and machine listening to measure animal behavior. Since poultry operations differ significantly from farm to farm and over the life of the chicken as it grows from chick to a mature bird, the machine learning algorithms must adapt. The monitoring systems must be appliance-like in that they do not require expertise or any more than minimal involvement on the part of the farmer. This research will result in the advancement and productization of acoustic machine learning algorithms which search out unusual behaviors in the animals in their environment and provide early indications of distress, sickness, discomfort, and feed and water issues to the grower based on intelligent listening and inference. Acoustic approaches do not disturb the animals, are more robust than video for long-term deployment in dusty environments, and operate around the clock and in the dark. By providing early actionable insights to the grower, this technology can correct problems early, thereby improving not only the animal’s welfare, but their productivity as well. By deploying inexpensive microphones at multiple locations in a grow-out house, activities and problems can be localized, bringing precision livestock technology to flock-based animal management.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.

该小企业技术转让 (STTR) 第一阶段项目的更广泛影响将是提高家禽养殖场鸡的福祉，并为种植者提供监测鸡只状况的有效工具，因为鸡是广泛消费的活禽来源。在全球范围内，消费者对道德饲养的动物的偏好日益增加，该项目通过促进改善家禽养殖的福利实践来满足这一需求，并与主要生产商和机构消费者开展合作，建立影响整个供应链的基于证据的福利标准。随着农业的衰落。对于美国的劳动力来说，有必要建立自动化机制来扩展农民的能力，该项目将为鸟类开发一个智能监控系统，以满足这些需求，从而改善鸟类福利并扩大农民的能力。 Transfer (STTR) 第一阶段项目使用音频监控和机器监听来测量动物行为，因为不同农场的家禽操作以及鸡从雏鸡到成年鸡的整个生命周期都有很大差异，因此机器学习算法必须适应。监控系统必须这项研究将导致声学机器学习算法的进步和产品化，这些算法可以找出环境中动物的异常行为并提供帮助。基于智能聆听和推理，向种植者发出痛苦、疾病、不适以及饲料和水问题的早期迹象，声学方法不会干扰动物，比视频更可靠，适合在灰尘环境中长期部署，并且可以在周围运行。时钟和在黑暗中提供。该技术可以为种植者提供切实可行的见解，可以及早纠正问题，从而不仅提高动物的福利，还提高动物的生产力。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。