TECH CORNER


Implementing an irrigation data standard in the real world


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Data exchange in agricultural irrigation has been particularly messy with growers and manufacturers having to deal with multiple data formats.


tandards are tools that enable us to make order in a messy world. Data exchange in agricultural irrigation has been particularly messy with growers


and manufacturers having to deal with multi- ple data formats. The American Society of Ag- ricultural and Biological Engineers/American National Standards Institute National Standard S632 for the exchange of irrigation data helps to solve this issue by providing a common model and format for data that drives irriga- tion decisions.


The existence of such a standard is only a first step, though; its value is only realized through implementation.


Reference data Logger model


References (integer)


Manufacturer


References (integer)


Sensor model Figure 1 (top):


Components and relationships within


the S632 data model that were used in the integration


Figure 2 (bottom): Preserving the


meaning of the logger data


Logger in the field


Obs code


References (code) References (code)


Proprietary but small data package


Data


producer cloud


Standardized but larger data package


24 Irrigation TODAY | Winter 2021 irrigationtoday.org


txStatus (code)


Sensor


References (integer)


Setup data Logger


UoM (code)


Has Obs setup


References (code)


Value Obs Has Phen time


Two ag industry companies recently tested the implementation of S632 Part 2, which deals with observations and measurements data. It is an agricultural implementation of the Interna- tional Organization for Standardization 19156 standard, a conceptual model for observations and measurements. One company was the data producer and the other served as the data consumer.


The basic data model is shown in fig. 1. It in- cludes reference data describing the manufac- turer’s equipment, setup and configuration data at the farm site, and the actual measurements (“Obs” in fig. 1). The first task was to translate the data coming in the data producer’s proprietary


Data Has


Obs coll Has


Proprietary but small data package


Data


consumer cloud


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