Autor según el artículo: Borraz-Martinez, Sergio; Simo, Joan; Gras, Anna; Mestre, Mariangela; Boque, Ricard; Tarres, Francesc;
Departamento: Química Analítica i Química Orgànica
Autor/es de la URV: Boqué Martí, Ricard
Palabras clave: Varietal mixture Nursery plant Identification Deep learning Convolutional neural network Computer vision
Resumen: 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.
Áreas temáticas: 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
Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
Direcció de correo del autor: ricard.boque@urv.cat
Identificador del autor: 0000-0001-7311-4824
Fecha de alta del registro: 2023-02-23
Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
Enlace a la fuente original: https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/10.1002/cem.3388
URL Documento de licencia: http://repositori.urv.cat/ca/proteccio-de-dades/
Referencia al articulo segun fuente origial: Journal Of Chemometrics. 36 (2):
Referencia de l'ítem segons les normes 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
DOI del artículo: 10.1002/cem.3388
Entidad: Universitat Rovira i Virgili
Año de publicación de la revista: 2022
Tipo de publicación: Journal Publications
Repositori URV