Articles producció científica> Química Analítica i Química Orgànica

Varietal quality control in the nursery plant industry using computer vision and deep learning techniques

  • Identification data

    Identifier: imarina:9138981
    Authors:
    Borraz-Martínez STarrés FBoqué RMestre MSimó JGras A
    Abstract:
    © 2020 John Wiley & Sons, Ltd. Computer vision coupled to deep learning is a promising technique with multiple applications in the industry. In this work, the potential of this technique has been assessed in the classification of two varieties of almond trees (Prunus dulcis), Soleta and Pentacebas. For that, a convolutional neural network named VGG16 was used. The most appropriate configuration for model training was studied, which included the comparison between two different filling modes (reflect and nearest) in the data augmentation step, the evaluation of the batch size and the analysis of the image sizes. The robustness of the model was also checked, and information was obtained about how the model extracts the information from the images. The results showed that the reflect fill mode was more effective than the nearest one. The best results were obtained using batches with 30 and 40 images, with an image size of (224 × 224) pixels. The verification of the robustness proved the capability of the technique as a promising tool for plant varietal identification.
  • Others:

    Author, as appears in the article.: Borraz-Martínez S; Tarrés F; Boqué R; Mestre M; Simó J; Gras A
    Department: Química Analítica i Química Orgànica
    URV's Author/s: Boqué Martí, Ricard
    Keywords: Varietal mixture Nursery plant Deep learning Convolutional neural network Computer vision
    Abstract: © 2020 John Wiley & Sons, Ltd. Computer vision coupled to deep learning is a promising technique with multiple applications in the industry. In this work, the potential of this technique has been assessed in the classification of two varieties of almond trees (Prunus dulcis), Soleta and Pentacebas. For that, a convolutional neural network named VGG16 was used. The most appropriate configuration for model training was studied, which included the comparison between two different filling modes (reflect and nearest) in the data augmentation step, the evaluation of the batch size and the analysis of the image sizes. The robustness of the model was also checked, and information was obtained about how the model extracts the information from the images. The results showed that the reflect fill mode was more effective than the nearest one. The best results were obtained using batches with 30 and 40 images, with an image size of (224 × 224) pixels. The verification of the robustness proved the capability of the technique as a promising tool for plant varietal identification.
    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/full/10.1002/cem.3320
    Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
    Papper original source: Journal Of Chemometrics. (e3320):
    APA: Borraz-Martínez S; Tarrés F; Boqué R; Mestre M; Simó J; Gras A (2022). Varietal quality control in the nursery plant industry using computer vision and deep learning techniques. Journal Of Chemometrics, (e3320), -. DOI: 10.1002/cem.3320
    Article's DOI: 10.1002/cem.3320
    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
    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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