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

这个小型企业创新研究(SBIR)第一阶段项目旨在了解农业设施中发现的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，再加上大脑？S的处理能力，可以很容易地区分不同的植物物种。这种能力是人类成为农业社会(耕作需要除草)的基本需求之一，这有助于启动巨大的社会进步。同样，为了将自动化系统引入下一代能力，计算机视觉必须与自然世界进行更高保真度的交互。今天？S计算机视觉有能力检测一个斑点？植被和没有植被的对比。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。项目组将建立一个基于现场定制支持向量机(支持向量机)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)，以用于更大的自动除草系统。通过创造计算机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数量。这个项目的更广泛的影响/商业潜力是增加蔬菜农场的竞争力，特别是有机蔬菜农场，同时改善人类健康和环境。今天，有机农场占美国农业经济的5%，并以每4年翻一番有机面积的速度增长。有机农业的一个关键特点是缺乏除草剂。因此，有机农场通常是手工除草的。杂草控制约占有机农场运营成本的50%，而传统农场的这一比例不到10%。据估计，每年在除草有机农场上花费7亿美元，因此创造一种可以自动除草的系统是一个巨大的商业机会。该项目将开发一种系统，该系统使用拖拉机后面的计算机系统来自动检测和消除早期种植阶段的杂草。该系统的开发和部署成本不到手工除草的五分之一。这项技术也适用于传统的作物间伐，因为它可以显著减少除草剂的使用量。此外，这项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，对健康和可持续发展具有深远的好处。

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.