RT info:eu-repo/semantics/article
T1 From harvest to market: Non-destructive bruise detection in kiwifruit using convolutional neural networks and hyperspectral imaging
A1 Ebrahimi, Sajad
A1 Pourdarbani, Razieh
A1 Sabzi, Sajad
A1 Rohban, Mohammad H.
A1 Arribas Sánchez, Juan Ignacio
K1 Fruit - Quality
K1 Frutas - Conservación
K1 Frutas - Industria y comercio - Calidad - Control
K1 Hyperspectral imaging
K1 Image processing - Digital techniques
K1 Imágenes, Tratamiento de las
K1 Kiwifruit
K1 Kiwi
K1 Machine learning
K1 Aprendizaje automático
K1 Near infrared spectroscopy
K1 Espectroscopia de infrarrojos
K1 Fruit - Ripening
K1 3107 Horticultura
K1 2209.90 Tratamiento Digital. Imágenes
K1 1203.06 Sistemas Automatizados de Control de Calidad
AB Fruit is often bruised during picking, transportation, and packaging, which is an important post-harvest issue especially when dealing with fresh fruit. This paper is aimed at the early, automatic, and non-destructive ternary (three-class) detection and classification of bruises in kiwifruit based on local spatio-spectral near-infrared (NIR) hyperspectral (HSI) imaging. For this purpose, kiwifruit samples were hand-picked under two ripening stages, either one week (7 days) before optimal ripening (unripe) or at the optimal ripening time instant (ripe). A total of 408 kiwi fruit, i.e., 204 kiwifruits for the ripe stage and 204 kiwifruit for the unripe stage, were harvested. For each stage, three classes were considered (68 samples per class). First, 136 HSI images of all undamaged (healthy) fruit samples, under the two different ripening categories (either unripe or ripe) were acquired. Next, bruising was artificially induced on the 272 fruits under the impact of a metal ball to generate the corresponding bruised fruit HSI image samples. Then, the HSI images of all bruised fruit samples were captured either 8 (Bruised-1) or 16 h (Bruised-2) after the damage was produced, generating a grand total of 408 HSI kiwifruit imaging samples. Automatic 3D-convolutional neural network (3D-CNN) and 2D-CNN classifiers based on PreActResNet and GoogLeNet models were used to analyze the HSI input data. The results showed that the detection of bruising conditions in the case of the unripe fruit is a bit easier than that for its ripe counterpart. The correct classification rate (CCR) of 3D-CNN-PreActResNet and 3D-CNN-GoogLeNet for unripe fruit was 98% and 96%, respectively, over the test set. At the same time, the CCRs of 3D-CNN-PreActResNet and 3D-CNN-GoogLeNet for ripe fruit were both 86%, computed over the test set. On the other hand, the CCRs of 2D-CNN-PreActResNet and 2D-CNN-GoogLeNet for unripe fruit were 96 and 95%, while for ripe fruit, the CCRs were 91% and 98%, respectively, computed over the test set, implying that early detection of the bruising area on HSI imaging was consistently more accurate in the unripe fruit case as compared to its ripe counterpart, with an exception made for the 2D-CNN GoogLeNet classifier which showed opposite behavior.
PB MDPI
SN 2311-7524
YR 2023
FD 2023
LK https://uvadoc.uva.es/handle/10324/66602
UL https://uvadoc.uva.es/handle/10324/66602
LA eng
NO Horticulturae, 2023, Vol. 9, Nº. 8, 936
NO Producción Científica
DS UVaDOC
RD 24-nov-2024