Integrated Proximal Sensing of Soil and Crop

土壤和作物的集成近端传感

• 批准号: RGPIN-2017-04810
• 负责人: Adamchuk, Viacheslav
• 金额: $ 2.99万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=688278
• 关键词: Integrated; Proximal; Sensing; Soil; Crop

The ultimate goal of this research program is to advance proximal soil and plant sensing technologies to enable low-cost acquisition of accurate information about spatial and temporal changes in crop growing conditions across a landscape. Proximal sensing systems rely on gathering signals from transducers placed in contact, or less than 2 m away from the target. Knowing and understanding the heterogeneity of soil and plant properties help farmers and other land managers optimize their decision-making process to develop profitable, sustainable, and environmentally friendly operations. During the 2017-2022 period, the emphasis will be placed on integrating several soil sensing techniques in a single, on-the-spot soil analyzer and the development of an optimized methodology for deployment of this system in both piloted and autonomous modes of operation. The minimum integrated package will include: an integrated cartridge of several ion-selective membranes (pH, nitrate, potassium, sodium, etc), digital microscope, spectroscope, soil respiration sensor as well as mechanical resistance and water content sensors. The system will be tested in both mineral and organic soil conditions. The integrated plant sensing platform will be extended to combine multispectral, ultrasonic and thermal measurements with low-grade laser scanning and digital imagery components. This platform will serve as the basic crop phenotyping unit; it will be tested on commodity crops (corn and soybean) as well as forage and vegetable crops. Finally, a cloud-based geospatial data analytic tool will be developed to blend proximal sensing data obtained using systems being developed with conventional high-density data layers, such as field topography, apparent soil electrical conductivity, yield maps, satellite and low-altitude imagery, etc. The neighborhood search clustering process will be used to increase the value of information obtained by minimizing the cost and maximizing the accuracy of targeted thematic maps revealing spatially variable soil productivity and potential crop stress assessments. The program will engage at least six HQPs over the next five years. The results will be disseminated within the precision agriculture scientific community, relevant agribusinesses and service providers as well as crop producer associations.

该研究计划的最终目标是推进近端土壤和植物传感技术，以低成本获取有关整个景观中作物生长条件的空间和时间变化的准确信息。近端传感系统依赖于从接触的传感器或距离目标不到 2 m 的传感器收集信号。 了解和理解土壤和植物特性的异质性有助于农民和其他土地管理者优化决策过程，以发展有利可图、可持续和环境友好的运营。 2017-2022 年期间，重点将放在将多种土壤传感技术集成到单个现场土壤分析仪中，并开发在试点和自主操作模式下部署该系统的优化方法。最小集成包将包括：多个离子选择性膜（pH、硝酸盐、钾、钠等）的集成盒、数字显微镜、分光镜、土壤呼吸传感器以及机械阻力和水分传感器。该系统将在矿物和有机土壤条件下进行测试。集成植物传感平台将进行扩展，将多光谱、超声波和热测量与低级激光扫描和数字图像组件结合起来。该平台将作为作物表型分析的基本单元；它将在商品作物（玉米和大豆）以及饲料和蔬菜作物上进行测试。最后，将开发基于云的地理空间数据分析工具，以混合使用传统高密度数据层开发的系统获得的近端传感数据，例如田野地形、表观土壤电导率、产量图、卫星和低空图像等。邻域搜索聚类过程将用于通过最小化成本和最大化揭示空间变量的目标专题地图的准确性来增加所获得信息的价值 土壤生产力和潜在作物胁迫评估。该计划将在未来五年内参与至少六个总部项目。研究结果将在精准农业科学界、相关农业企业和服务提供商以及作物生产者协会内传播。