Integrated Proximal Sensing of Soil and Crop

土壤和作物的近端传感是一个不断发展的科学和技术领域，它利用传感系统获得靠近目标或直接接触目标的信息。如果部署得当，近端传感系统可提供关于土壤和作物的物理、化学和生物特性的高密度数据。这些属性有助于我们更好地了解影响作物生长的土壤条件的变化。考虑到这种空间和时间上的可变性，减少了对环境的负面影响，提高了盈利能力。近年来，近端传感技术已被用于提高肥料、水和其他生产投入的使用效率以及评估环境。主要问题是，大多数传感器响应不止一种农艺特性，这往往使决策过程复杂化。传感器融合是一个新兴的概念，它依赖于结合来自不同来源的数据来增加所获得信息的确定性。虽然这个概念很吸引人，但人们对它的理解很差，需要在理论和潜在应用方面进行实质性的发展。在这个建议中，一个独特的方法来整合最有前途和成本效益的传感技术进行了讨论。概念上不同的，商业化的，近端土壤/作物传感器将同时使用，并与我们最近开发的传感器原型集成。此外，包括地面高程在内的近地遥感数据将与遥感数据（卫星或航空图像）集成。这种方法的跨学科和多用途性质保证了无穷无尽的潜在用途。在演示和分析某些应用程序的同时，该计划的最终目标是开发一个强大而可靠的数据流结构，可以在农业和自然资源管理的许多学科中轻松采用。

Proximal sensing of soils and crops is a growing area of science and technology that uses sensing systems to obtain information either close to, or in direct contact with, the target. When deployed properly, proximal sensing systems provide high-density data on the physical, chemical and biological attributes of soil and crops. These attributes help us to better understand the changes in soil conditions influencing crop growth. Accounting for this variability over space and time reduces the negative environmental impact and increases profitability. In recent years, proximal sensing technology has been used to increase efficiency in the use of fertilizers, water, and other production inputs as well as to assess the environment. The main problem is that most sensors respond to more than one agronomic property, which frequently complicates the decision-making process. Sensor fusion is an emerging concept that relies on combining data from alternative sources to increase the certainty of the information obtained. Although appealing, the concept is poorly understood and requires substantial development in both theory and potential applications. In this proposal, a unique methodology to integrate the most promising and cost-effective sensing technologies is discussed. Conceptually different, commercially available, proximal soil/crop sensors will be used simultaneously as well as integrated with our recently developed sensor prototypes. Also, proximal sensing data that includes ground elevation will be integrated with remotely sensed data (satellite or airborne imagery). The interdisciplinary and multipurpose nature of this approach promises an endless array of potential uses. While demonstrating and analyzing certain applications, the ultimate goal of this program is to develop a strong and reliable dataflow structure that can be adopted easily across many disciplines in agriculture and in the management of natural resources.