Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloIn-field high throughput grapevine phenotyping with a consumer-grade depth camera
Anno di pubblicazione2019
Formato
  • Elettronico
  • Cartaceo
Autore/iMilella, Annalisa; Marani, Roberto; Petitti, Antonio; Reina, Giulio
Affiliazioni autoriCNR-STIIMA, Bari, Italy; CNR-STIIMA, Bari, Italy; CNR-STIIMA, Bari, Italy; Università del Salento, Lecce, Italy
Autori CNR e affiliazioni
  • ANNALISA MILELLA
  • ROBERTO MARANI
  • ANTONIO PETITTI
Lingua/e
  • inglese
AbstractPlant phenotyping, that is, the quantitative assessment of plant traits including growth, morphology, physiology, and yield, is a critical aspect towards efficient and effective crop management. Currently, plant phenotyping is a manually intensive and time consuming process, which involves human operators making measurements in the field, based on visual estimates or using hand-held devices. In this work, methods for automated grapevine phenotyping are developed, aiming to canopy volume estimation and bunch detection and counting. It is demonstrated that both measurements can be effectively performed in the field using a consumer-grade depth camera mounted on-board an agricultural vehicle. First, a dense 3D map of the grapevine row, augmented with its color appearance, is generated, based on infrared stereo reconstruction. Then, different computational geometry methods are applied and evaluated for plant per plant volume estimation. The proposed methods are validated through field tests performed in a commercial vineyard in Switzerland. It is shown that different automatic methods lead to different canopy volume estimates meaning that new standard methods and procedures need to be defined and established. Four deep learning frameworks, namely the AlexNet, the VGG16, the VGG19 and the GoogLeNet, are also implemented and compared to segment visual images acquired by the RGBD sensor into multiple classes and recognize grape bunches. Field tests are presented showing that, despite the poor quality of the input images, the proposed methods are able to correctly detect fruits, with a maximum accuracy of 91.52%, obtained by the VGG19 deep neural network.
Lingua abstractinglese
Altro abstract-
Lingua altro abstract-
Pagine da293
Pagine a306
Pagine totali-
RivistaComputers and electronics in agriculture
Attiva dal 1985
Editore: Elsevier Science Publishers - Amsterdam
Paese di pubblicazione: Paesi Bassi
Lingua: inglese
ISSN: 0168-1699
Titolo chiave: Computers and electronics in agriculture
Titolo proprio: Computers and electronics in agriculture.
Titolo abbreviato: Comput. electron. agric.
Numero volume della rivista156
Fascicolo della rivista-
DOI10.1016/j.compag.2018.11.026
Verificato da refereeSì: Internazionale
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)
  • ISI Web of Science (WOS) (Codice:000456754100029)
  • Scopus (Codice:2-s2.0-85057328249)
Parole chiaveAgricultural robotics, In-field phenotyping, RGB-D sensing, Grapevine canopy volume estimation, Deep learning-based grape bunch detection
Link (URL, URI)https://www.sciencedirect.com/science/article/abs/pii/S0168169918307580
Titolo parallelo-
Licenza-
Scadenza embargo-
Data di accettazione21/11/2018
Note/Altre informazioniReceived 28 May 2018, Revised 13 November 2018, Accepted 21 November 2018, Available online 30 November 2018
Strutture CNR
  • STIIMA — Istituto di Sistemi e Tecnologie Industriali Intelligenti per il Manifatturiero Avanzato
Moduli/Attività/Sottoprogetti CNR-
Progetti Europei-
Allegati
In-field high throughput grapevine phenotyping with a consumer-grade depth camera (documento privato )
Descrizione: Versione pubblicata
Tipo documento: application/pdf