IntegrALIMENTA Banana fruit laboratory images [Dataset]

For the generation of the dataset, pieces of banana (two varieties: Cavendish, Gros Michel) of different weight ranges (70-250 g). For each pieze of banana, 72 photographs were taken. To train the model, each photograph was identified with the exact weight of the banana piece and the different condi...

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Bibliographic Details
Authors: Motilva, María-José, Bartolomé, Begoña, Moreno-Arribas, M. Victoria, Izquierdo González, Pablo, Aragón Espinosa, Patricia, Relaño de la Guía, Edgard, Cobo Cano, Miriam, Heredia, Ignacio, García Díaz, Daniel, Aguilar, Fernando, Lloret Iglesias, Lara, Yuste, Silvia, Íñiguez, María, Pérez-Matute, Patricia
Format: conjunto de datos
Publication Date:2025
Country:España
Institution:Consejo Superior de Investigaciones Científicas (CSIC)
Repository:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/397207
Online Access:http://hdl.handle.net/10261/397207
https://doi.org/10.20350/digitalCSIC/17508
Access Level:Open access
Keyword:Artificial intelligence
Banana weight estimation
Deep learning
Convolutional neural network
Image recognition
Description
Summary:For the generation of the dataset, pieces of banana (two varieties: Cavendish, Gros Michel) of different weight ranges (70-250 g). For each pieze of banana, 72 photographs were taken. To train the model, each photograph was identified with the exact weight of the banana piece and the different conditions (type of support, distance and camera angle). To train the model, each photograph was identified with the exact weight of the banana piece and the different conditions (variety, type of support, distance and camera angle).

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