| dc.description.abstract | Energy and food crisis are two major problems that our human society has to face in the 21st
century. With the world’s population reaching 7.62 billion as of May 2018, both electric power
and agricultural industries turn to technological innovations for solutions to keep up the increasing
demand. In the past and currently, utility companies rely on rule of thumb to estimate power
consumption. However, inaccurate predictions often result in over production, and much energy is
wasted. On the other hand, traditional periodic and threshold based irrigation practices have also
been proven outdated. This problem is further compounded by recent years’ frequent droughts
across the globe. New technologies are needed to manage irrigations more efficiently.
Fortunately, with the unprecedented development of Artificial Intelligence (AI), wireless communication,
and ubiquitous computing technologies, high degree of information integration and
automation are steadily becoming reality. More smart metering devices are installed today than
ever before, enabling fast and massive data collection. Patterns and trends can be more accurately
predicted using machine learning techniques. Based on the results, utility companies can schedule
production more efficiently, not only enhancing their profitabilities, but also making our world’s
energy supply more sustainable. In addition, predictions can serve as references to detect anomalous
activities like power theft and cyber attacks.
On the other hand, with wireless communication, real-time soil moisture sensor readings and
weather forecasts can be collected for precision irrigation. Smaller but more powerful controllers
provide perfect platforms for complicated control algorithms. We designed and built a fully automated
irrigation system at Bushland, Texas. It is designed to operate without any human intervention.
Workers can program, move, and monitor multiple irrigation systems remotely. The
algorithm that runs on the controls deserves more attention. AI and other state of art controlling
techniques are implemented, making it much more powerful than any existing systems. By integrating
professional crop yield simulation models like DSSAT, computers can run tens of thousand
simulations on all kinds of weather and soil conditions, and more importantly, learn from the experience. In reality, such process would take thousands of years to obtain. Yet, the computers can
find an optimum solution in minutes. The experience is then summarized as a policy and stored
inside the controller as a lookup table. Furthermore, after each crop season, users can calibrate and
update current policy with real harvest data.
Crop yield models like DSSAT and AquaCrop play very important roles in agricultural research.
They represent our best knowledge in plant biology and can be very accurate when well
calibrated. However, the calibration process itself is often time consuming, thus limiting the scale
and speed of using these models. We made efforts to combine different models to produce a single
accurate prediction using machine learning techniques. The process does not require manual calibration,
but only soil, historical weather, and harvest data. 20 models were built, and their results
were evaluated and compared. With high accuracy, machine learning techniques have shown a
promising direction to best utilize professional models, and demonstrated great potential for use in
future agricultural research. | en |
