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Quality evaluation of Mono & bi-Colored Apples with computer vision and multispectral imaging

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Abstract

In the field of agriculture science, the presence of disease in fruits affects the quantity and quality of production. To sort the fruits based on quality is a challenging task. Human grades the fruit but this process is inconsistent, stagnant, expensive and get influenced by the surrounding. Thus an effective system for grading fruit is desired. In this paper, an automated fruit grading system has been developed for mono and bi-colored apples. An automated fruit grading system involves three steps, namely, segmentation, feature extraction, and classification. In this work, segmentation of defected area has been carried out using fuzzy c-means and for feature extraction, the various combination of Statistical/ Textural, Geometrical, Gabor Wavelet and, Discrete Cosine Transform feature have been considered. For classification three different classifiers i.e. k-NN (k- Nearest Neighbor)), SRC (Sparse Representation Classifier), SVM (Support Vector Machine) have been applied. The proposed system has been validated for four different databases of apples one having 1120 samples of which 984 were defective, the second having 333 samples of which 247 were defective, the third having 100 samples with 26 defective and the fourth with 56 defectives. The maximum accuracy of 95.21%, 93.41%, 92.64% and 87.91% for four datasets respectively, achieved by the system is encouraging and is comparable with the state of art techniques. The system performance has been validated using the k-fold cross-validation technique by considering k = 10. Results showed that a combination of features provides improved performance and the SVM classifier has the highest performance among k-NN, SRC. As compared to the state of art, our proposed solution yields better accuracy. Hence, the proposed algorithm showed great potential for the classification of apple and the possibility of its uses for further different fruit.

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  1. Department of Electronics and Communication, GLA, University, Mathura, 281406, India

    Anuja Bhargava & Atul Bansal

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  1. Anuja Bhargava
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Bhargava, A., Bansal, A. Quality evaluation of Mono & bi-Colored Apples with computer vision and multispectral imaging. Multimed Tools Appl 79, 7857–7874 (2020). https://doi.org/10.1007/s11042-019-08564-3

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