or difficult to simulate with more traditional models.
For example, canopy wetness can be very difficult to diagnose and predict based on scientific principles alone. However, an AI-based model can learn the associations between observed canopy wetness (e.g., from sensor data) and the factors that influence it. Once these associations have been learned, they can be applied to diagnose and predict canopy wetness in other locations and times.
Taking to the sky
Automating the analysis of aerial imagery of a field is yet another example of how AI could be used. This might include diagnosing areas of crop stress due to moisture, disease, etc.
AI can further identify the relationships between observed crop stress and sensor- or model-based soil moisture data. This allows such data to be used to more proactively manage irrigation activities to
prevent crop stress, rather than waiting for stress to become apparent. Similarly, AI can be applied to images from a digital camera, enabling a simple cell phone or tablet to become a pocket agronomist of sorts. Smartphone-based apps that apply AI to identify insects, weeds and diseases by simply pointing the camera at the item of interest are already available.
In many ways, the sky’s the limit in terms of how AI could be applied for the betterment of irrigation. However, it will take time — and loads of data — to realize this potential. Do you have a problem that AI might be able to solve? If so, have you started collecting the data that might be needed? The sooner you get started, the sooner you might be able to see the benefits.
This type of understanding of crops can foster the development of more efficient or productive management practices, including irrigation.
John Mewes, PhD, serves as chief scientist at Iteris Inc. and manages many of the research and development activities associated with Iteris’ ClearAg offerings.
Rob Hale, PhD, serves as senior director research meteorologist at Iteris and leads research and development activities related to soil modeling and irrigation scheduling.
Caleb Midgley, MS, serves as scientific software project manager at Iteris and is the product manager in charge of ClearAg’s IMFocus irrigation scheduling and custom field modeling APIs.
Soil moisture impact on growth response 1 100 0.08 80 0.06 60 0.04 40 20 0 0.02
30
40
50 60 70 80 90 Average daily temperature (F)
0
Figure 1. This is a visualization of the impacts of air temperature and soil moisture on corn growth as determined using AI. Values near 0 (purple and blue dots) indicate dry conditions, while values nearer to 1 (green and yellow dots) indicate more adequate soil moisture. Of particular note is the revealed impact of dry soils on slowing corn growth on days with warm temperatures.
8 Irrigation TODAY | October 2018
Growth response (scaled from 0 to 100)
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