Authors:
Renzo Pacheco
1
;
Paula González
1
;
Luis Chuquimarca
1
;
2
;
Boris Vintimilla
1
and
Sergio Velastin
3
;
4
Affiliations:
1
ESPOL Polytechnic University, ESPOL, CIDIS, Guayaquil, Ecuador
;
2
UPSE Santa Elena Peninsula State University, UPSE, FACSISTEL, La Libertad, Ecuador
;
3
Queen Mary University of London, London, U.K.
;
4
University Carlos III, Madrid, Spain
Keyword(s):
Fruit Defects, Convolutional Neural Networks, Real, Virtual Data.
Abstract:
The present study seeks to evaluate different CNN models in order to compare their performance in recognizing a range of defects in apples and mangoes to ensure the quality of the production of these foods. Using the CNN models, InceptionV3, MobileNetV2, VGG16 and DenseNet121, which were trained with a dataset of real and synthetic images of apples and mangoes covering fruit in acceptable quality condition and with defects: rot, bruises, scabs and black spots. Training was performed with variations on the hyper-parameters and the metric is accuracy. The MobileNetV2 model achieved the highest accuracy in training and testing, obtaining 97.50% for apples and 92.50% for mangoes, making it the most suitable model for defect detection in these fruits. The InceptionV3 and DenseNet121 models presented accuracy values above 90%, while the VGG16 model obtained the poorest performance by not exceeding 80% accuracy for any of the fruits. The trained models, especially MobileNetV2, are capable o
f recognizing a range of defects in the fruits under study with a high degree of accuracy and are suitable for use in the development of automation applications for the quality assessment process of apples and mangoes.
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