SING: Free-Space SensING of Grape Moisture Using RF Shadowing

Altherwy, Youssef N; McCann, Julie A.

doi:10.1109/tim.2020.3027928

Cited by 3 publications

(3 citation statements)

References 29 publications

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“…It should be noted that proper cultivation practices help minimize these impacts. Altherwy and McCann [ 71 ] collected data on the moisture content with radio frequency signals. Based on these data, their regression model achieved 90% accuracy; therefore, it can provide information on grape yields and health in a low-cost and contactless way.…”

Section: Resultsmentioning

confidence: 99%

How can proximal sensors help decision-making in grape production?

Mizik

2023

Heliyon

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Section: Resultsmentioning

confidence: 99%

How can proximal sensors help decision-making in grape production?

Mizik

2023

Heliyon

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“…The standard or traditional methods retrieve limited data and produce a static prediction in a multi-step process of determining average number of clusters per vine, number of berries per cluster, and weight per cluster or berry with the growth overall 10% error greatly dependent on adequate staffing and extensive historical databases of cluster weights and yields [37] Computer vision and image processing are leading the alternative methods and are one of the most utilized techniques for attempting an early yield estimation. Still, different approaches such as Synthetic Aperture Radar (SAR), low frequency ultrasound [38], RF Signals [39], counting number of flowers [40][41][42][43][44][45][46][47], Boolean model application [48], shoot count [49], shoot biomass [50,51], frequency-modulated continuous-wave (FMCW) radar [52,53], detection of specular spherical reflection peaks [54], the combination of RGB and multispectral imagery [55] along with derived occlusion ratios, are alternative methods.…”

Section: Resultsmentioning

confidence: 99%

“…It relies on a new exploratory approach using a scheme that senses grape moisture content by utilizing Radio Frequency (RF) signals to estimate yield without physical contact in a laboratory environment. According to the authors, it can be used for early yield estimation [39]. This study represents an exploratory approach in a laboratory environment that does not provide an actual yield estimative.…”

Section: H-data-driven Models Based On Radio Frequency Data Processing (N = 1)mentioning

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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