Evidence map›Paper›PMID 37836956›Full record

ArticleSensors (Basel, Switzerland)2023

A Deep Learning Approach for Precision Viticulture, Assessing Grape Maturity via YOLOv7.

Eftichia Badeka, Eleftherios Karapatzak, Aikaterini Karampatea, Elisavet Bouloumpasi, Ioannis Kalathas, Chris Lytridis, Emmanouil Tziolas, Viktoria Nikoleta Tsakalidou, Vassilis G Kaburlasos

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 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

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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

9 authors.

Eftichia BadekaHuman-Machines Interaction Laboratory (HUMAIN-Lab), Department of Computer Science, International Hellenic University (IHU), 65404 Kavala, Greece.
Eleftherios KarapatzakDepartment of Agricultural Biotechnology and Oenology, International Hellenic University, 66100 Drama, Greece.ORCID 0000-0003-4251-7908
Aikaterini KarampateaDepartment of Agricultural Biotechnology and Oenology, International Hellenic University, 66100 Drama, Greece.
Elisavet BouloumpasiDepartment of Agricultural Biotechnology and Oenology, International Hellenic University, 66100 Drama, Greece.ORCID 0009-0005-8726-0546
Ioannis KalathasHuman-Machines Interaction Laboratory (HUMAIN-Lab), Department of Computer Science, International Hellenic University (IHU), 65404 Kavala, Greece.ORCID 0000-0001-7386-3788
Chris LytridisHuman-Machines Interaction Laboratory (HUMAIN-Lab), Department of Computer Science, International Hellenic University (IHU), 65404 Kavala, Greece.ORCID 0000-0002-9045-3833
Emmanouil TziolasHuman-Machines Interaction Laboratory (HUMAIN-Lab), Department of Computer Science, International Hellenic University (IHU), 65404 Kavala, Greece.ORCID 0000-0002-7229-8577
Viktoria Nikoleta TsakalidouHuman-Machines Interaction Laboratory (HUMAIN-Lab), Department of Computer Science, International Hellenic University (IHU), 65404 Kavala, Greece.
Vassilis G KaburlasosHuman-Machines Interaction Laboratory (HUMAIN-Lab), Department of Computer Science, International Hellenic University (IHU), 65404 Kavala, Greece.ORCID 0000-0002-1639-0627

Funding

European Regional Development Fund MIS 5046047
6 · The paper itself

Abstract

In the viticulture sector, robots are being employed more frequently to increase productivity and accuracy in operations such as vineyard mapping, pruning, and harvesting, especially in locations where human labor is in short supply or expensive. This paper presents the development of an algorithm for grape maturity estimation in the framework of vineyard management. An object detection algorithm is proposed based on You Only Look Once (YOLO) v7 and its extensions in order to detect grape maturity in a white variety of grape (Assyrtiko grape variety). The proposed algorithm was trained using images received over a period of six weeks from grapevines in Drama, Greece. Tests on high-quality images have demonstrated that the detection of five grape maturity stages is possible. Furthermore, the proposed approach has been compared against alternative object detection algorithms. The results showed that YOLO v7 outperforms other architectures both in precision and accuracy. This work paves the way for the development of an autonomous robot for grapevine management.

Indexed as

Deep LearningVitisAlgorithmsFarmsHumansgrape maturity detectionmaturity estimationobject detectionYOLO

Identifiers

PMID37836956
PMCPMC10575379

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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.