Fruit sizing using AI: A review of methods and challenges
Ver/Abrir
Autor/a
Miranda, Juan C.
Zude-Sasse, Manuela
Tsoulias, Nikos
Escolà, Alexandre
Arnó, Jaume
Rosell-Polo, Joan R.
Sanz-Cortiella, Ricardo
Martínez-Casasnovas, José A.
Gregorio, Eduard
Fecha de publicación
2023-09-23ISSN
0925-5214
Resumen
Fruit size at harvest is an economically important variable for high-quality table fruit production in orchards and vineyards. In addition, knowing the number and size of the fruit on the tree is essential in the framework of precise production, harvest, and postharvest management. A prerequisite for analysis of fruit in a real-world environment is the detection and segmentation from background signal. In the last five years, deep learning convolutional neural network have become the standard method for automatic fruit detection, achieving F1-scores higher than 90 %, as well as real-time processing speeds. At the same time, different methods have been developed for, mainly, fruit size and, more rarely, fruit maturity estimation from 2D images and 3D point clouds. These sizing methods are focused on a few species like grape, apple, citrus, and mango, resulting in mean absolute error values of less than 4 mm in apple fruit. This review provides an overview of the most recent methodologies developed for in-field fruit detection/counting and sizing as well as few upcoming examples of maturity estimation. Challenges, such as sensor fusion, highly varying lighting conditions, occlusions in the canopy, shortage of public fruit datasets, and opportunities for research transfer, are discussed.
Tipo de documento
Artículo
Versión del documento
Versión publicada
Lengua
Inglés
Materias (CDU)
633 - Cultivos y producciones
Páginas
18
Publicado por
Elsevier
Publicado en
Postharvest Biology and Technology
Citación
Miranda, Juan Carlos, Jordi Gené-Mola, Manuela Zude-Sasse, Nikos Tsoulias, Alexandre Escolà, Jaume Arnó, Joan R. Rosell-Polo, Ricardo Sanz, José A. Martínez‐Casasnovas, and Eduard Gregorio. “Fruit Sizing Using AI: A Review of Methods and Challenges.” Postharvest Biology and Technology 206 (December 1, 2023): 112587. https://doi.org/10.1016/j.postharvbio.2023.112587.
Número del acuerdo de la subvención
MICIU/Programa Estatal de I+D+I orientada a los retos de la sociedad/RTI2018-094222-B-100/ES/Tecnologías de agricultura de precisión para optimizar el manejo de dosel foliar y la protección fitosanitaria sostenible en plantaciones de frutales/PAgFRUIT
MICINN/Programa Estatal para impulsar la investigación científico-técnica y su transferencia/PID2021-126648OB-100/ES/Protección de cultivos de precisión para conseguir objetivos del Pacto Verde Europeo en uso eficiente y reducción de fitosanitarios mediate Agricultura de Precisión/PAgPROTECT
FEDER/ / /EU/ /
Program
Ús Eficient de l'Aigua en Agricultura
Este ítem aparece en la(s) siguiente(s) colección(ones)
- ARTICLES CIENTÍFICS [2590]
El ítem tiene asociados los siguientes ficheros de licencia:
Excepto si se señala otra cosa, la licencia del ítem se describe como http://creativecommons.org/licenses/by/4.0/