Tech corner
The sky’s the limit with AI Can using artificial intelligence in irrigation solve real problems? By John Mewes, PhD; Rob Hale, PhD; and Caleb Midgley, MS
I What is IoT?
IoT refers to the universe of sensors and devices that leverage our modern communications infrastructure to collect and exchange data.
t would be hard to have missed the recent hype surrounding the promise artificial intelligence holds for agriculture.
AI refers to the application of computers to perform tasks that normally require human intelligence. The irrigation space, like agriculture as a whole, is fertile ground for the application of AI to solve real-world problems.
The key to realizing this potential, however, is data – and lots of it. For this reason, you may also have heard the term “internet of things,” or IoT, in the context of AI.
Scheduling & efficiency
There is an abundance of ways in which IoT devices and AI could potentially be applied to benefit irrigation. One of the most frequently touted applications is for improved irrigation scheduling and efficiency.
While soil moisture data (from either sensors or models) has long been used as a scheduling aid, AI offers the potential for machine learning of how soil moisture responds to irrigation events in scenarios with different crops, soils, environmental conditions, etc. When tied to an irrigation control system, this information can automatically implement control strategies that help minimize water usage, manage nutrient losses, or achieve more desirable or uniform soil moisture throughout the field.
Similarly, AI could be applied to learn the associations between available weather, crop and soil condition data, and the corresponding irrigation recommendations of a trained agronomist, thereby automating the repetitive aspects of the scheduling process.
Environmental influence
AI can also be used to determine how environmental factors influence the crops being irrigated. Figure 1 on pg. 8 depicts the nature of the relationship between air temperatures, soil moisture and the daily growth of corn. This relationship was determined by applying AI to corn test plot data, along with the weather and soil conditions experienced in those test plots. The AI-based algorithm was not only able to quantify how air temperature drives corn growth, but it also revealed how corn growth slows under dry conditions during warm temperatures.
This type of understanding of crops can foster the development of more efficient or productive management practices, including irrigation.
Real-world problems
AI can be used to simulate other real-world problems, as well. It can be particularly useful for problems where the underlying physical processes are poorly understood
The irrigation space, like agriculture as a whole, is fertile ground for the application of AI to solve real-world problems.
6 Irrigation TODAY | October 2018
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44 |
Page 45 |
Page 46 |
Page 47 |
Page 48
