Agriculture may be a big-data domain, but many specific AI applications depend on relatively few observations. Making AI useful in these settings means combining limited data with existing agricultural knowledge and using uncertainty to guide what we collect next.
Agriculture is often characterized as a big-data domain in the world of artificial intelligence (AI). In some contexts, that is certainly true. Satellite imagery, genomics, sensors, and modern agricultural equipment can generate enormous amounts of data. But for many real agricultural AI problems, the data are not particularly big at all.
This disconnect has become increasingly apparent to me through our recent projects and conversations with computer scientists involved in the AI Institute for Transforming Workforce & Decision Support (AgAID). As AI applications proliferate, it is easy to take the availability of big data for granted, but that assumption does not fit many agricultural applications.
