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

Abstract

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.

Computer Vision and Deep Learning for Precision Viticulture

“…This work completes the survey proposed by Seng et al by adding the most recent research works based on computer vision and Deep Learning [32]. In addition, exhaustive reviews of existing works for grape yield prediction, not limited to computer vision-based methods, can be found in recent publications by Laurent et al and Barriguinha et al [33,34]. We first present the generic framework used by most Computer Vision methods and common Deep Learning models.…”

Hyperspectral Sensing of Plant Diseases: Principle and Methods

“…More than 40 VIS have been defined so far and are widely used in global and regional land cover [62,63], vegetation classification and environmental change [64,65], crop and pasture yield estimation [66,67], drought monitoring [63,68], and host-pathogen studies. Francesco et al [69] investigated the relationships among tiger-stripe foliar symptom expression, microelements, and vegetation indices, including the NDVI, GNDVI, and WI of grapevine.…”

Semi-supervised deep learning and low-cost cameras for the semantic segmentation of natural images in viticulture

“…Automatic procedures for object segmentation are even more attractive in those areas of horticulture of high added value, such as viticulture (Barriguinha et al, 2021). Here, monitoring at the plant scale allows vine-growers to understand possible spatial variabilities and find fine-tuned solutions.…”