Assessing automatic data processing algorithms for RGB-D cameras to predict fruit size and weight in apples
Visualitza/Obre
Data de publicació
2023-10-07ISBN
0168-1699
Resum
Data acquired using an RGB-D Azure Kinect DK camera were used to assess different automatic algorithms to estimate the size, and predict the weight of non-occluded and occluded apples. The programming of the algorithms included: (i) the extraction of images of regions of interest (ROI) using manual delimitation of bounding boxes or binary masks; (ii) estimating the lengths of the major and minor geometric axes for the purpose of apple sizing; and (iii) predicting the final weight by allometric modelling. In addition to the use of bounding boxes, the algorithms also allowed other post-mask settings (circles, ellipses and rotated rectangles) to be implemented, and different depth options (distance between the RGB-D camera and the fruits detected) for subsequent sizing through the application of the thin lens theory. Both linear and nonlinear allometric models demonstrated the ability to predict apple weight with a high degree of accuracy (R2 greater than 0.942 and RMSE < 16 g). With respect to non-occluded apples, the best weight predictions were achieved using a linear allometric model including both the major and minor axes of the apples as predictors. The mean absolute percentage error (MAPE) ranged from 5.1% to 5.7% with respective RMSE of 11.09 g and 13.02 g, depending to whether circles, ellipses, or bounding boxes were used to adjust fruit shape. The results were therefore promising and open up the possibility of implementing reliable in-field apple measurements in real time. Importantly, final weight prediction error and intermediate size estimation errors (from sizing algorithms) interact but in a way that is not easily quantifiable when weight allometric models with implicit prediction error are used. In addition, allometric models should be reviewed when applied to other apple cultivars, fruit development stages or even for different fruit growth conditions depending on canopy management.
Tipus de document
Article
Versió del document
Versió publicada
Llengua
Anglès
Matèries (CDU)
62 - Enginyeria. Tecnologia
633 - Cultius i produccions
Pàgines
15
Publicat per
Elsevier
Publicat a
Computers and Electronics in Agriculture
Citació
Miranda, Juan Carlos, Jaume Arnó, Jordi Gené-Mola, Jaume Lordan, L. Asín, and Eduard Gregorio. “Assessing Automatic Data Processing Algorithms for RGB-D Cameras to Predict Fruit Size and Weight in Apples.” Computers and Electronics in Agriculture 214 (November 1, 2023): 108302. doi:10.1016/j.compag.2023.108302.
Número de l'acord de la subvenció
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
MICINN/Programa Estatal para impulsar la investigación científico-técnica y su transferencia/TED2021-131871B-I00/ES/Sistemas de monitoreo de bajo coste en plantaciones frutales para agricultura de precisión basados en sensores fotónicos/DIGIFRUIT
Programa
Fructicultura
Ús Eficient de l'Aigua en Agricultura
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