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

I-Corps：图像处理平台，用于识别光合色素密度，测量氮含量并管理肥料（智能可持续肥料管理器）

• 批准号: 2227256
• 负责人: Amir Khoddamzadeh
• 金额: $ 5万
• 依托单位: Florida International University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-15 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2227256&HistoricalAwards=false
• 关键词: Corps; Image; processing; platform; identify

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.

这个i-Corps项目的更广泛的影响/商业潜力是开发一种技术来确定盆栽植物的肥料需求。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。在苗圃的一英亩土地上生产多达300,000个集装箱，其中许多得到了过量的化肥使用。氮(N)是一种影响植物叶绿素含量的常量营养素，它可以用来定义植物的生长状况和叶片的氮含量。这项拟议的技术可能使苗圃生产者能够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，拟议的技术可能会促进植物健康和环境可持续发展，并能够从大小环境中的肥料实践中收集大数据。这个i-Corps项目基于一个智能、可持续的肥料管理器平台的开发，该平台使用图像处理和机器学习来测量叶片氮含量。这项拟议的技术允许使用任何相机传感器来成像和处理叶片颜色，以非破坏性的方式识别光合色素密度，并将该测量结果与现有的云数据进行比较以进行分析。目前的化肥管理通常基于公布的化肥建议，不同的植物种类不同。拟议的设计使用智能手机拍摄的一张图像来推荐盆栽植物(花卉和观赏植物)的肥料需求。对图像进行扫描和分析，用户会收到指示植物是否缺乏营养的消息。核心机器学习正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。该技术的应用编程接口(API)将嵌入到应用程序中。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。