Evidence map›Paper›PMID 36679645›Full record

ArticleSensors (Basel, Switzerland)2023

Designing a Proximal Sensing Camera Acquisition System for Vineyard Applications: Results and Feedback on 8 Years of Experiments.

Florian Rançon, Barna Keresztes, Aymeric Deshayes, Malo Tardif, Florent Abdelghafour, Gael Fontaine, Jean-Pierre Da Costa, Christian Germain

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Florian RançonIMS Laboratory, CNRS UMR 5218, University of Bordeaux, Talence Campus, F-33400 Talence, France.ORCID 0000-0001-7647-9722
Barna KeresztesIMS Laboratory, CNRS UMR 5218, University of Bordeaux, Talence Campus, F-33400 Talence, France.
Aymeric DeshayesIMS Laboratory, CNRS UMR 5218, University of Bordeaux, Talence Campus, F-33400 Talence, France.
Malo TardifIMS Laboratory, CNRS UMR 5218, University of Bordeaux, Talence Campus, F-33400 Talence, France.ORCID 0000-0001-6232-4391
Florent AbdelghafourINRAE, Institut Agro, ITAP, University of Montpellier, F-34196 Montpellier, France.ORCID 0000-0003-0890-3346
Gael FontaineIMS Laboratory, CNRS UMR 5218, University of Bordeaux, Talence Campus, F-33400 Talence, France.
Jean-Pierre Da CostaIMS Laboratory, CNRS UMR 5218, University of Bordeaux, Talence Campus, F-33400 Talence, France.ORCID 0000-0003-2390-0047
Christian GermainIMS Laboratory, CNRS UMR 5218, University of Bordeaux, Talence Campus, F-33400 Talence, France.ORCID 0000-0002-3097-8283

Funding

European Union's Horizon 2020 research and innovation program 31884
6 · The paper itself

Abstract

The potential of image proximal sensing for agricultural applications has been a prolific scientific subject in the recent literature. Its main appeal lies in the sensing of precise information about plant status, which is either harder or impossible to extract from lower-resolution downward-looking image sensors such as satellite or drone imagery. Yet, many theoretical and practical problems arise when dealing with proximal sensing, especially on perennial crops such as vineyards. Indeed, vineyards exhibit challenging physical obstacles and many degrees of variability in their layout. In this paper, we present the design of a mobile camera suited to vineyards and harsh experimental conditions, as well as the results and assessments of 8 years' worth of studies using that camera. These projects ranged from in-field yield estimation (berry counting) to disease detection, providing new insights on typical viticulture problems that could also be generalized to orchard crops. Different recommendations are then provided using small case studies, such as the difficulties related to framing plots with different structures or the mounting of the sensor on a moving vehicle. While results stress the obvious importance and strong benefits of a thorough experimental design, they also indicate some inescapable pitfalls, illustrating the need for more robust image analysis algorithms and better databases. We believe sharing that experience with the scientific community can only benefit the future development of these innovative approaches.

Indexed as

deep learningdisease detectionimage analysismobile cameraprecision viticultureproximal sensingsmart farmingyield estimation

Identifiers

PMID36679645
PMCPMC9865571

What OpenQuestion holds

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Registered trials

None linked

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.