This paper describes a robot system for the automatic pruning of grape vines. A mobile platform straddles the row of vines, and it images them with trinocular stereo cameras as it moves. A computer vision system builds a three-dimensional (3D) model of the vines, an artificial intelligence (AI) system decides which canes to prune, and a six degree-of-freedom robot arm makes the required cuts. The system is demonstrated cutting vines in the vineyard. The main contributions of this paper are the computer vision system that builds 3D vine models, and the test of the complete-integrated system. The vine models capture the structure of the plants so that the AI system can decide where to prune, and they are accurate enough that the robot arm can reach the required cuts. Vine models are reconstructed by matching features between images, triangulating feature matches to give a 3D model, then optimizing the model and the robot's trajectory jointly (incremental bundle adjustment). Trajectories are estimated online at 0.25 m/s, and they have errors below 1% when modeling a 96 m row of 59 vines. Pruning each vine requires the robot arm to cut an average of 8.4 canes. A collision-free trajectory for the arm is planned in intervals of 1.5 s/vine with a rapidly exploring random tree motion planner. The total time to prune one vine is 2 min in field trials, which is similar to human pruners, and it could be greatly reduced with a faster arm. Trials also show that the long chain of interdependent components limits reliability. A commercially feasible pruning robot should stop and prune each vine in turn. C 2016 Wiley Periodicals, Inc.

Vine pruning is an important part of vineyard management, and pruning is the most expensive task in the vineyard which has not yet been automated. Every year, most new canes must be removed from the vine, and the choice of canes to retain impacts vine yield. To automate the process of vine pruning, a vine pruning robot must make decisions on what canes to remove or to keep, based on a 3D topological model of the structure of the vine. In this paper we present an Artificial Intelligence (AI) system for making these decisions, developed and evaluated using simulated vines. A viticulture expert evaluated our approach by comparing it to pruning decisions made by a pruner with a skill level typical of human pruners. Our system successfully pruned 30 % of vines better than the human and 89 % at least as well. These results demonstrate that the vine pruning problem is solvable using current computing technologies, and that automating the pruning process has the potential to improve vine quality and yield.

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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.

Diminishing acid concentrations have long been thought to be one of the effects of ripening grapes that leads to increased bird pressure approaching harvest. Blackbirds (Turdus merula) and silvereyes (Zosterops lateralis) were offered in a field context varying concentrations of tartaric and malic acids in artificial grapes, where sugar and all other ripening grape parameters were controlled. No linear response of consumption to varying acid concentration was found for either species. A response to rising sugar was confirmed, but diminishing acid concentrations in ripening grapes appear not to be a contributing factor to increasing bird pressure approaching harvest.