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Integrating Irrigation Tech

A Texas A&M team seeks to automate and incorporate advanced center pivot controls into a user-friendly package.

There is a wealth of irrigation technology that has been introduced by pivot manufacturers in the past decade aimed at remote control of sprinklers and boosting water efficiency. Now, a Texas A&M team is working to integrate these advances along with new technology.  

“There are some great advanced irrigation technologies available, but they are complex, underutilized, difficult to use, and not well integrated in existing control systems. Therefore, their benefits are not being fully realized,” explains Dana Porter, who is part of a five-member team at Texas A&M working to unify irrigation technology.

The team is working to design a system utilizing off-the-shelf sensors and components to create a cost-effective and independent platform that will allow producers to realize benefits of irrigation technologies by integrating and automating information and decision support tools.

Their objectives include establishing:

  • A wireless sensor network with anomaly detection.
  • An irrigation system controller using real-time and forecast data, integration of data from multiple sensor inputs, and unmanned aerial systems and models.
  • A user-friendly interface.

Technology Suite

The team’s leader, Thomas Marek, says they have already shown significant improvements over commercially available systems by developing a technology suite that includes:

  • Improved center pivot irrigation positioning and speed control.
  • Improved variable-rate irrigation control with real-time updates using in-field near-real-time data plus predictive crop water-use capabilities.
  • A soil-moisture in-field sensor placement method to optimize wireless sensor nodes to balance cost with necessary data reliability.

“In our case, advanced automation includes automated communication of data from soil water sensors to the pivot controller,” Marek says. “We are using a processing model that looks at recent field data and the status today, plus a machine-learning process to integrate data and decisions with an automated controller. Together, this tells the user and the system what to do and when. The system is also unique in that it logs all of what it does, and all of this happens at the pivot.”

Issues Still Remain

Utilizing tools such as real-time soil moisture monitoring, near-real-time and short-term predictive crop evapotranspiration or crop water use, quantitative precipitation forecasting, and an artificial intelligence algorithm, Marek says the Texas A&M effort was able to automate the “when, where, and how much” decisions of crop irrigation.

Development was conducted so the platform-independent control system could be retrofitted into existing center pivot irrigation systems. The project team has several patents pending as a result of the work, Marek says.

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