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: 2023-02-23

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: http://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