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Combining computer vision and deep learning to classify varieties of Prunus dulcis for the nursery plant industry

  • Identification data

    Identifier: imarina:9242972
    Authors:
    Borraz-Martinez, SergioSimo, JoanGras, AnnaMestre, MariangelaBoque, RicardTarres, Francesc
    Abstract:
    Varietal control to avoid unwanted varietal mixtures is an important objective for the nursery plant industry. In this study, we have developed and analyzed the capabilities of a computer vision system based on deep learning for the control of plant varieties in the nursery plant industry and for evaluating its capabilities. For this purpose, three datasets of nursery plant images were compared. The datasets came from two varieties of almond trees (Prunus dulcis) named Soleta and Pentacebas. Each dataset contained images with three different scales: whole plant, leaf, and venation. The Gradient-weighted Class Activation Mapping (Grad-CAM) technique was used to unveil the most important features to discriminate between both varieties. The three datasets provided classification accuracies above 97% in the test set, being the leaf dataset, with a 98.8% accuracy, the one providing the best results. Concerning the most important features of the plants, the Grad-CAM showed that they are located in the center of the leaf, that is, the venation. In conclusion, we have shown that computer vision is a promising technique for the control of plant varietal mixtures.
  • Others:

    Author, as appears in the article.: Borraz-Martinez, Sergio; Simo, Joan; Gras, Anna; Mestre, Mariangela; Boque, Ricard; Tarres, Francesc;
    Department: Química Analítica i Química Orgànica
    URV's Author/s: Boqué Martí, Ricard
    Keywords: Varietal mixture Nursery plant Identification Deep learning Convolutional neural network Computer vision
    Abstract: Varietal control to avoid unwanted varietal mixtures is an important objective for the nursery plant industry. In this study, we have developed and analyzed the capabilities of a computer vision system based on deep learning for the control of plant varieties in the nursery plant industry and for evaluating its capabilities. For this purpose, three datasets of nursery plant images were compared. The datasets came from two varieties of almond trees (Prunus dulcis) named Soleta and Pentacebas. Each dataset contained images with three different scales: whole plant, leaf, and venation. The Gradient-weighted Class Activation Mapping (Grad-CAM) technique was used to unveil the most important features to discriminate between both varieties. The three datasets provided classification accuracies above 97% in the test set, being the leaf dataset, with a 98.8% accuracy, the one providing the best results. Concerning the most important features of the plants, the Grad-CAM showed that they are located in the center of the leaf, that is, the venation. In conclusion, we have shown that computer vision is a promising technique for the control of plant varietal mixtures.
    Thematic Areas: Statistics & probability Química Mathematics, interdisciplinary applications Matemática / probabilidade e estatística Interdisciplinar Instruments & instrumentation Engenharias iv Engenharias iii Engenharias ii Computer science, artificial intelligence Ciências agrárias i Ciência da computação Chemistry, analytical Biotecnología Biodiversidade Automation & control systems Astronomia / física Applied mathematics Analytical chemistry
    licence for use: https://creativecommons.org/licenses/by/3.0/es/
    Author's mail: ricard.boque@urv.cat
    Author identifier: 0000-0001-7311-4824
    Record's date: 2024-09-07
    Papper version: info:eu-repo/semantics/publishedVersion
    Link to the original source: https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/10.1002/cem.3388
    Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
    Papper original source: Journal Of Chemometrics. 36 (2):
    APA: Borraz-Martinez, Sergio; Simo, Joan; Gras, Anna; Mestre, Mariangela; Boque, Ricard; Tarres, Francesc; (2022). Combining computer vision and deep learning to classify varieties of Prunus dulcis for the nursery plant industry. Journal Of Chemometrics, 36(2), -. DOI: 10.1002/cem.3388
    Article's DOI: 10.1002/cem.3388
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2022
    Publication Type: Journal Publications
  • Keywords:

    Analytical Chemistry,Applied Mathematics,Automation & Control Systems,Chemistry, Analytical,Computer Science, Artificial Intelligence,Instruments & Instrumentation,Mathematics, Interdisciplinary Applications,Statistics & Probability
    Varietal mixture
    Nursery plant
    Identification
    Deep learning
    Convolutional neural network
    Computer vision
    Statistics & probability
    Química
    Mathematics, interdisciplinary applications
    Matemática / probabilidade e estatística
    Interdisciplinar
    Instruments & instrumentation
    Engenharias iv
    Engenharias iii
    Engenharias ii
    Computer science, artificial intelligence
    Ciências agrárias i
    Ciência da computação
    Chemistry, analytical
    Biotecnología
    Biodiversidade
    Automation & control systems
    Astronomia / física
    Applied mathematics
    Analytical chemistry
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