Autor segons l'article: Borraz-Martinez, Sergio; Simo, Joan; Gras, Anna; Mestre, Mariangela; Boque, Ricard; Tarres, Francesc;
Departament: Química Analítica i Química Orgànica
Autor/s de la URV: Boqué Martí, Ricard
Paraules clau: Varietal mixture Nursery plant Identification Deep learning Convolutional neural network Computer vision
Resum: 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.
Àrees temàtiques: 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
Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
Adreça de correu electrònic de l'autor: ricard.boque@urv.cat
Identificador de l'autor: 0000-0001-7311-4824
Data d'alta del registre: 2024-09-07
Versió de l'article dipositat: info:eu-repo/semantics/publishedVersion
Enllaç font original: https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/10.1002/cem.3388
URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
Referència a l'article segons font original: Journal Of Chemometrics. 36 (2):
Referència 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 de l'article: 10.1002/cem.3388
Entitat: Universitat Rovira i Virgili
Any de publicació de la revista: 2022
Tipus de publicació: Journal Publications