ReviewHeliyon2023
How can proximal sensors help decision-making in grape production?
Review in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- The Evolution of Data-Driven Management Zone Delineation: A Systematic Review.Sensors (Basel, Switzerland) · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Precision viticulture (PV) aims at achieving greater profit in a more sustainable way through improved resource use efficiency and greater production. PV is based on reliable data provided by different sensors. This study aims to identify the role of proximal sensors in the decision support of PV. During the selection process, 53 of 366 articles identified were relevant for the study. These articles are classified into four groups: management zone delineation (27 articles), disease/pest prevention (11 articles), water management (11 articles), and better grape quality (5 articles). Differentiation between heterogeneous management zones is the basis for site-specific actions. The most important data that sensors provide for this are climatic and soil information. This makes it possible to predict harvesting time or identify areas for plantations. The recognition and prevention of diseases/pests are of crucial importance. Combined platforms/systems provide a good option without any compatibility problems, while variable rate spraying makes pesticide use much lower. Vine water status is the key to water management. Soil moisture and weather data can provide good insight; however, leaf water potential and canopy temperature are also used for better measurement. Although vine irrigation systems are expensive, the price premium of high-quality berries compensates for this because grape quality is closely related to its price.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.