First steps in translating human cognitive processes of cane pruning grapevines into AI rules for automated robotic pruning

Abstract

Cane pruning of grapevines is a skilled task for which, internationally, there is a dire shortage of human pruners. As part of a larger project developing an automated robotic pruner, we have used artificial intelligence (AI) algorithms to create an expert system for selecting new canes and cutting off unwanted canes. A domain and ontology has been created for AI, which reflects the expertise of expert human pruners. The first step in the creation of an expert system was to generate virtual vines, which were then ‘pruned’ by human pruners and also by the expert system in its infancy. Here we examined the decisions of 12 human pruners, for consistency of decision, on 60 virtual vines. 96.7% of the 12 pruners agreed on at least one cane choice after which there was diminishing agreement on which further canes to select for laying. Our results indicate that techniques developed in computational intelligence can be used to co-ordinate and synthesise the expertise of human pruners into a best practice format. This paper describes first steps in this knowledge elicitation process, and discusses the fit between cane pruning expertise and the expertise that can be elicited using AI based expert system techniques.