I-Corps: Image processing platform to identify photosynthetic pigment density to measure nitrogen content and manage fertilizer (Smart Sustainable Fertilizer Manager)

I-Corps项目的更广泛影响/商业潜力是开发一种确定盆栽植物肥料需求的技术。大规模苗圃生产是农业的一个相当大的组成部分，涉及使用容器种植植物。在苗圃生产中，一英亩的土地容纳了多达30万个容器，其中许多容器被过度施肥。氮(N)是一种影响植物叶绿素含量的常量营养素，可以用来决定植物的生长状况和叶片氮含量。提出的技术可以使苗圃生产者确定盆栽植物的肥料需求，并有助于避免过度施肥和养分流失。此外，拟议的技术可以促进植物健康和环境可持续性，并能够从大大小小的施肥实践中收集大数据。I-Corps项目的基础是开发一个智能、可持续的肥料管理平台，该平台使用图像处理和机器学习来测量叶片氮含量。提出的技术允许使用任何相机传感器对叶子颜色进行成像和处理，以非破坏性的方式识别光合色素密度，并将此测量结果与现有的云数据进行比较以进行分析。目前的肥料管理通常是基于已公布的肥料建议，这些建议因植物种类而异。拟议的设计使用智能手机拍摄的单幅图像来建议盆栽植物（花卉和观赏植物）的肥料需求。图像被扫描和分析，用户收到一条消息，表明植物是否缺乏营养。核心机器学习被用来训练一个基于已完成和正在进行的研究项目提供的“绿色价值”的模型。该技术的应用程序编程接口（API）将嵌入到应用程序中。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this I-Corps project is the development of a technology to determine the fertilizer needs of potted plants. Large-scale nursery production, a sizable component of the agriculture industry, involves the use of containers to grow plants. An acre of land in nursery production houses up to 300,000 containers, many of which receive excessive fertilizer application. Nitrogen (N) is a macronutrient that affects plant chlorophyll content, which may be used to define the growth status and leaf N content in plants. The proposed technology may enable nursery producers to determine the fertilizer needs of potted plants and help avoid overfertilization and nutrient runoff. In addition, the proposed technology may promote plant health and environmental sustainability as well as enable big data to be collected from fertilizer practices in large and small settings.This I-Corps project is based on the development of a smart, sustainable fertilizer manager platform that uses image processing and machine learning to measure leaf nitrogen content. The proposed technology allows the use of any camera sensor to image and process leaf color to identify the photosynthetic pigment density in a non-destructive way and compare this measurement with the existing cloud data for analysis. Current fertilizer management is typically based on published fertilizer recommendations, which vary among plant species. The proposed design uses a single image taken by a smartphone to give a recommendation on fertilizer needs for potted plants (flowers and ornamentals). The image is scanned and analyzed, and the user receives a message indicating whether a plant is deficient in nutrients. Core machine learning is being used to train a model based on the “green value” provided by completed and ongoing research projects. The technology's Application Programming Interface (API) will be embedded in the application.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.