A cellular automaton framework for within-field vineyard variance and grape production simulation

Shanmuganathan, Subana; Narayanan, Ajit; Robison, Nick

doi:10.1109/icnc.2011.6022364

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…Whatever the indirect method used, they all allow a fast and non-invasive alternative to manual sampling. They allow identifying single berries in images, even taken from a simple device such as a smartphone [56][57][58] and then using different methods such as convolutional neural networks [1,59], cellular automata [60], or even sensors capable of collecting phenotypic traits of grape bunches, that are known to be related with grapevine yield [14,61], to estimate yields.…”

Section: Resultsmentioning

confidence: 99%

Vineyard Yield Estimation, Prediction, and Forecasting: A Systematic Literature Review

2021

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Purpose—knowing in advance vineyard yield is a critical success factor so growers and winemakers can achieve the best balance between vegetative and reproductive growth. It is also essential for planning and regulatory purposes at the regional level. Estimation errors are mainly due to the high inter-annual and spatial variability and inadequate or poor performance sampling methods; therefore, improved applied methodologies are needed at different spatial scales. This paper aims to identify the alternatives to traditional estimation methods. Design/methodology/approach—this study consists of a systematic literature review of academic articles indexed on four databases collected based on multiple query strings conducted on title, abstract, and keywords. The articles were reviewed based on the research topic, methodology, data requirements, practical application, and scale using PRISMA as a guideline. Findings—the methodological approaches for yield estimation based on indirect methods are primarily applicable at a small scale and can provide better estimates than the traditional manual sampling. Nevertheless, most of these approaches are still in the research domain and lack practical applicability in real vineyards by the actual farmers. They mainly depend on computer vision and image processing algorithms, data-driven models based on vegetation indices and pollen data, and on relating climate, soil, vegetation, and crop management variables that can support dynamic crop simulation models. Research limitations—this work is based on academic articles published before June 2021. Therefore, scientific outputs published after this date are not included. Originality/value—this study contributes to perceiving the approaches for estimating vineyard yield and identifying research gaps for future developments, and supporting a future research agenda on this topic. To the best of the authors’ knowledge, it is the first systematic literature review fully dedicated to vineyard yield estimation, prediction, and forecasting methods.

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Section: Resultsmentioning

confidence: 99%

Vineyard Yield Estimation, Prediction, and Forecasting: A Systematic Literature Review

2021

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“…It is also well-known that repeating applications of simple rules lead to extremely complex behavior that can emulate physical, social and biological systems [13]. CA can also explain and predict changes in dynamics of environment systems and agricultural systems, for instance, the simulations of an individual plant growth [14,15], population dynamics [16], and community interaction levels [17].…”

Section: Introductionmentioning

confidence: 99%

A stochastic cellular automata model for rice tillering in the system of rice intensification

Precharattana

Kajonphol

2018

J. Phys.: Conf. Ser.

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Climate effects on grape production and quality at Kumeu, New Zealand

Shanmuganathan

Whalley²

2013

Piantadosi, J., Anderssen, R.S. And Boland J. (Eds) MODSIM2013, 20th International Congress on Modelling and Simulation

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Grape quality and anticipated berry composition (Brix, flavour and aroma) significantly vary due to the variability in seasonal climatic and environmental conditions, such as soil and topography within vineyards. Such "Meso" climatic and "terroir" variations affect grapevine phenology (from bud formation to berry ripening stages). This in turn affects productivity, some instances extensively, in terms of total vine yield, grape bunch count, berry weight and composition even within a vineyard. Quantifying these effects on grape production and quality has been a challenging task as no detailed study has been performed at precision agricultural level for this vineyard located in Kumeu, New Zealand. This study investigates the influences of climate and soil components on the production of this vineyard using data gathered from 2011 to 2013.

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A cellular automaton framework for within-field vineyard variance and grape production simulation

Abstract: Abstract-Winegrowers

Cited by 3 publications

References 15 publications

Vineyard Yield Estimation, Prediction, and Forecasting: A Systematic Literature Review

Vineyard Yield Estimation, Prediction, and Forecasting: A Systematic Literature Review

A stochastic cellular automata model for rice tillering in the system of rice intensification

Climate effects on grape production and quality at Kumeu, New Zealand

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