Image-based classiﬁcation and segmentation of healthy and defective mangoes

Maria Jeseca C. Baculo; Conrado Ruiz

doi:10.1117/12.2522840

15 March 2019 Image-based classiﬁcation and segmentation of healthy and defective mangoes

Maria Jeseca C. Baculo, Conrado Ruiz

Proceedings Volume 11041, Eleventh International Conference on Machine Vision (ICMV 2018); 1104117 (2019) https://doi.org/10.1117/12.2522840
Event: Eleventh International Conference on Machine Vision (ICMV 2018), 2018, Munich, Germany

Abstract

The use of image processing and classification for agricultural applications has been widely studied and has led to work such as the automatic grading of fruit and vegetables, yield approximation and defect detection. Image segmentation is one of the first steps to identify the region of interest within an image. This paper presents an approach to automatic segmentation and classification of healthy and defective Carabao mangoes. K-means, range filtering and color-channel segmentation were utilized so that the varying texture and color of mangoes due to the surface defects can be considered. Results show that the proposed technique performs better than the classical K-means segmentation. The performance of segmentation step has a considerable influence on the precision of the classification model. Segmented and not segmented images were trained using KNN, SVM, MLP and CNN. The experiments showed that the models performed better when trained with segmented images.

Citation Download Citation

Maria Jeseca C. Baculo and Conrado Ruiz "Image-based classiﬁcation and segmentation of healthy and defective mangoes", Proc. SPIE 11041, Eleventh International Conference on Machine Vision (ICMV 2018), 1104117 (15 March 2019); https://doi.org/10.1117/12.2522840

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