Abstract

Advances in remote-sensing, sensor and robotic technology, machine learning, and artificial intelligence (AI) – smart algorithms that learn from patterns in complex data or big data - are rapidly transforming agriculture. This presents huge opportunities for sustainable viticulture, but also many challenges. This chapter provides a state-of-the-art review of the benefits and challenges of AI and big data, highlighting work in this domain being conducted around the world. A way forward, that incorporates the expert knowledge of wine-growers (i.e. human-in-the-loop) to augment the decision-making guidance of big data and automated algorithms, is outlined. Future work needs to explore the coupling of expert systems to AI models and algorithms to increase both the usefulness of AI, its benefits, and its ease of implementation across the vitiviniculture value-chain.

Highlights

  • Viticulture is at the front line of climate change as grape production is highly sensitive to changing environmental conditions

  • Vineyards that are certified organic and biodynamic, are not necessarily the same ones that are early- or significant-adopters of latest Big data (BD) and artificial intelligence (AI) technology that can accelerate and support the wider transformation from conventional to sustainable vitiviniculture practices. This is because of a disconnect that exists between the path to adoption of sustainable practices and the path to adoption of BD and AI technology

  • The majority of research challenges identified in this review, which span a wide range of aspects of viniviticulture, point to the need for including expert knowledge to provide context and rules to design AI algorithms and their automated learning, while helping to structure data, obtain high-quality data for training AI models, and validate the use and adoption of new BD types and sources

Read more

Summary

Introduction

Viticulture is at the front line of climate change as grape production is highly sensitive to changing environmental conditions. Seasonal climate changes of hotter and longer summers and warmer winters are shifting areas suitable for growing grapes further north in the Northern Hemisphere (NH), and south in the Southern Hemisphere (SH), from historical cultivation latitudes of 4° and 51° (NH) and 6° and 45° (SH) [1]. This is driving wine makers to move vineyards to higher elevations that provide colder nighttime temperatures and less frequent and intense peak daytime temperatures to ripen grapes, while preventing over-ripening [2, 3]. The goal is to design AI algorithms with a fast and efficient learning speed, fast convergence to a solution, good generalization ability and ease of implementation

Review objective and methodology
AI in Vitiviniculture
AI learning algorithms and model types
AI use-cases and knowledge gaps
Proposed BD and AI framework
Conclusions
Findings
Conflict of interest
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call