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I-Corps: 3-Dimensional Data Output from Remote Sensing Algorithms

I-Corps: 3-Dimensional Data Output from Remote Sensing Algorithms
I-Corps:遥感算法的 3 维数据输出
批准号:
1741462
负责人:
Kevin Czajkowski
金额:
$5.0万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2017
资助国家:
美国
项目状态:
已结题
起止时间:
2017-06-15 至 2018-11-30

项目摘要

项目成果

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中文摘要
翻译
这个I-Corps项目更广泛的影响/商业潜力是利用遥感图像的算法绘制农田三维视图,帮助农民提高土壤肥力和作物产量,同时保护流域。从经济角度来看,农民希望尽量减少营养素的支出。农民需要通过安装瓷砖和穿孔塑料管来增加地下排水,从而提高产量。瓷砖的放置和完整性,加上关于土壤类型和特性的现有数据,对农田的水文和肥力有着至关重要的影响,影响到植物的生长、对肥料/养分、杀虫剂和其他类型应用的需求。对土地三维视图的精确了解将使农民能够就肥料、微量营养素、杀虫剂等的应用做出适当的决定,以最大限度地降低成本,最大限度地增加收入,并尽可能减少对农场所在生态系统的伤害。将农田的数据层沿着与瓦片排水沟的信息结合在一起,将使农业能够通过瓦片的适当放置和维修来优化生产,同时控制养分的使用和损失。农民们表示,了解瓷砖的位置可以在瓷砖损坏和需要修理时降低成本,并且在向田地添加额外的瓷砖以改善排水时也有帮助。在以前的工作中,人们发现,施用到农田的肥料将穿过土壤,并通过瓷砖排水沟离开农田进入地表水系统。这个I-Corps项目使用遥感算法来探测土壤下的瓷砖排水沟。该技术利用土壤的反射率以及土壤表面上表示的线性形状,指示田地内的瓦片。通过该项目产生的信息是独一无二的,是农业界急需的,以便在作物生产“精确耕作”中更好地利用土地。
英文摘要
The broader impact/commercial potential of this I-Corps project is to develop a 3-dimensional view of agricultural fields using algorithms applied to remotely sensed imagery to help farmers enhance soil fertility and crop production while also protecting watersheds. From an economic perspective, farmers want to minimize expenditures on nutrients. There is a need by farmers to increase below ground drainage through installation of tiles, perforated plastic pipes, and thus increase production. Tile placement and integrity, coupled with existing data on soil type and characteristics, have a critical impact on the hydrology and fertility of fields, affecting plant growth, the need for fertilizer/nutrients, pesticide and other types of applications. Precise knowledge of a three dimensional view of the land will enable farmers to make appropriate decisions about the application of fertilizers, micro-nutrients, pesticides, etc. to minimize cost, maximize revenue and do as little harm as possible to the ecosystem in which the farm exists. Bringing together data layers of agricultural fields along with information on tile drains will allow the agricultural industry to optimize production through proper placement and repair of tiles while controlling nutrient use and loss.This I-Corps project uses remote sensing techniques with aerial imagery to provide information precisely identifying the location of drain tiles buried beneath farm fields. Farmers have expressed that knowledge of the location of tile reduces their costs when tile fail and need repair and also helps when additional tile are added to a field improving drainage. In previous work, it was found that fertilizers applied to agricultural fields will pass through the soil and exit a field through tile drains to the surface water system. This I-Corps project uses remote sensing algorithms to detect tile drains below the soil. The technique utilizes the reflectance of the soil as well as the linear shape expressed on the soil surface that indicates tiles within fields. The information generated through this project is unique and much needed by the agricultural industry to better utilize land in crop production "precision farming".
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REU Site: Undergraduate Research and Mentoring - Using the Lake Erie Sensor Network to Study Land-Lake Ecological Linkages
  • 批准号:
    1461124
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $32.48万
  • 财政年份:
    2015
  • 负责人:
    Kevin Czajkowski
  • 依托单位:
LEADERS: Leadership for Educators: Academy for Driving Economic Revitalization in Science
  • 批准号:
    0927996
  • 项目类别:
    Standard Grant
  • 资助金额:
    $500.0万
  • 财政年份:
    2009
  • 负责人:
    Kevin Czajkowski
  • 依托单位:
REU Site: An Integrated Assessment of Physical, Ecological, and Socio-economic Aspects of a Watershed System
  • 批准号:
    0243872
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $18.67万
  • 财政年份:
    2003
  • 负责人:
    Kevin Czajkowski
  • 依托单位:
Energy and Earth Systems: GLOBE Protocol Research and Outreach
  • 批准号:
    0222905
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $38.11万
  • 财政年份:
    2002
  • 负责人:
    Kevin Czajkowski
  • 依托单位:
国内基金
海外基金
Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis