Abstract—A multi-agent (MA) cellular automaton (CA)
model framework for simulating grapevine growth and crop in
Chardonnay cultivated in northern New Zealand is presented.
Estimating or projecting grape crop in quantity (grapes in tons
per hectare (ha)) and berry quality in Brix (sugar content) is an
extremely complex and challenging task. The crop depends on
many factors, such as local weather and environmental
conditions that interact with each other at varying degrees and
over different time intervals in a “chaotic” manner. The key
factors and their influences are simulated using CA rules, MA
behaviour and interactions. Two sets of CA lattices and rules
are used to simulate individual grapevine growth and vineyard
phenological dynamics respectively. The results achieved show
potential for simulating vine growth and yield in different grape
varieties (Pinot Noir, Pinot Gris, Merlot and other wine styles)
and scales, such as New Zealand’s major wine regions and that
of the world’s, in ways not been explored previously.
Index Terms—Component; climate effects; yield; vineyard.
Subana Shanmuganathan is with Geoinformatics Research Centre, Auckland University of Technology, Private Bag 92006, Auckland 1142, New Zealand. (e-mail: firstname.lastname@example.org ). Ajit Narayanan and Nick Robinson are with School Computing and Mathematical Sciences, Auckland University of Technology, Private Bag 92006, Auckland 1142, New Zealand (e-mail: email@example.com).
Cite:S. Shanmuganathan, A. Narayanan, and N. Robinson, "A Multi-Agent (MA) Cellular Automaton (CA) Framework for Grapevine Growth and Crop Simulation," International Journal of Machine Learning and Computing vol.2, no. 4, pp. 496-500, 2012.