SBIR Phase I: Use of Machine Learning Techniques for Robust Crop and Weed Detection in Agricultural Fields

SBIR 第一阶段：利用机器学习技术实现农田中农作物和杂草的稳健检测

• 批准号: 1143463
• 负责人: Lee Redden
• 金额: $ 15万
• 依托单位: Blue River Technology Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-01-01 至 2012-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1143463&HistoricalAwards=false
• 关键词: SBIR; Phase; Use; Machine; Learning

This Small Business Innovation Research (SBIR) Phase I project seeks to understand the fundamental visual cues and characteristics of plants found in agricultural facilities for the purpose of rapid automated identification of plant species. The human eye, coupled with the brain?s processing power , can readily distinguish between different plant species. This capability was one of the basic needs for humans to become an agrarian society (farming requires weeding), which helped start enormous social advancement. Similarly, to bring automated systems to the next generation of capability, computer vision must interact with the natural world with greater fidelity. Today?s computer vision has ability to detect a ?splotch? of vegetation versus no vegetation. This project will advance computer vision by developing the equipment and software algorithms necessary to automatically distinguish plant types. The project team will build a computer vision algorithm based on a field customized support vector machine (SVM) that can automatically and reliably identify a known crop versus a foreign plant (i.e. weed) for use in a larger system for automated weeding. By creating the ability for computers to distinguish between plant types, we will enable food to be grown with reduced amounts of chemical herbicides.The broader impact/ commercial potential of this project is to increase the competitiveness of vegetable farms, particularly organic ones, while improving human health and the environment. Today, organic farms represent 5% of the U.S. agricultural economy and are growing at a pace to double organic acreage every 4 years. A key feature of organic farming is the lack of herbicides. Consequently, organic farms are normally weeded by hand. Weed control represents approximately 50% of operating costs for organic farms, compared to less than 10% for conventional ones. With an estimated $700M spent annually on weeding organic farms, there is a substantial commercial opportunity to create a system that can weed farms automatically. This project will develop a system that uses a computer system towed behind a tractor to automatically detect and eliminate weeds at early plant stages. The system can be developed and deployed at less than 1/5 the life-cycle costs of hand weeding. The technology is also applicable to conventional crop thinning where it can significantly reduce the amount of herbicides used. Additionally this technology has a profound health and sustainability benefits by eliminating human exposure to chemical herbicides through food and avoids herbicides leaching into the soil.

该小型企业创新研究 (SBIR) 第一阶段项目旨在了解农业设施中植物的基本视觉线索和特征，以便快速自动识别植物物种。人眼加上大脑的处理能力，可以轻松区分不同的植物种类。这种能力是人类进入农业社会的基本需求之一（耕种需要除草），这有助于开启巨大的社会进步。同样，为了将自动化系统引入下一代功能，计算机视觉必须以更高的保真度与自然世界交互。今天的计算机视觉有能力检测“斑点”有植被与无植被的比较。该项目将通过开发自动区分植物类型所需的设备和软件算法来推进计算机视觉。项目团队将构建基于现场定制支持向量机 (SVM) 的计算机视觉算法，该算法可以自动可靠地识别已知作物与外来植物（即杂草），以便在更大的自动除草系统中使用。通过让计算机具备区分植物类型的能力，我们将能够在种植食物时减少化学除草剂的用量。该项目更广泛的影响/商业潜力是提高蔬菜农场（尤其是有机农场）的竞争力，同时改善人类健康和环境。如今，有机农场占美国农业经济的 5%，并且以每 4 年有机种植面积翻一番的速度增长。有机农业的一个主要特点是不使用除草剂。因此，有机农场通常采用手工除草。杂草控制约占有机农场运营成本的 50%，而传统农场的运营成本不到 10%。据估计，每年在有机农场除草上的花费为 7 亿美元，因此创建一个可以自动为农场除草的系统存在巨大的商业机会。该项目将开发一种系统，使用拖拉机牵引的计算机系统在植物早期阶段自动检测和消除杂草。该系统的开发和部署成本不到手工除草的生命周期成本的 1/5。该技术还适用于常规作物间伐，可显着减少除草剂的使用量。此外，该技术通过消除人类通过食物接触化学除草剂并避免除草剂渗入土壤，具有深远的健康和可持续发展效益。