Information about the spread of crop disease is vital in developing countries, and as a result the governments of these countries devote scarce resources to gathering such data. Unfortunately, current surveys tend to be slow and expensive, and hence also tend to gather insufficient quantities of data. I will describe three computational methods for improving the use of survey resources by performing data collection with mobile devices and by directing survey progress through the application of AI techniques. First, I will introduce a spatial disease density model based on Gaussian process ordinal regression, which offers a better representation of the disease level distribution, as compared to the statistical approaches typically applied. Second, this model can be used to dynamically route survey teams to obtain the most valuable survey possible given a fixed budget. Third, I'll show that the diagnosis of plant disease can be automated using images taken by a camera phone, enabling data collection by survey workers with only basic training. We have applied our methods to the specific challenge of viral cassava disease monitoring in Uganda, for which we have implemented a real-time mobile survey system that will soon see practical use.
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