Papaya Fruit Maturity Estimation Using Wavelet and ConvNET

The papaya (Carica papaya L.) is a tropical fruit with high commercial value due to its superior nutritional and therapeutic properties. Papayas must be packaged in the fruit industry according to their degree of ripeness. Physically grading papaya fruit using human vision is time-consuming and dest...

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Bibliographic Details
Published inIngénierie des systèmes d'Information Vol. 28; no. 1; pp. 175 - 181
Main Authors Ratha, Ashoka Kumar, Barpanda, Nalini Kanta, Sethy, Prabira Kumar, Behera, Santi Kumari
Format Journal Article
LanguageEnglish
Published Edmonton International Information and Engineering Technology Association (IIETA) 01.02.2023
Subjects
Accuracy
Algorithms
Classification
Decomposition
Diabetes
Fruits
Machine learning
Papayas
Support vector machines
Wavelet transforms
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Summary:The papaya (Carica papaya L.) is a tropical fruit with high commercial value due to its superior nutritional and therapeutic properties. Papayas must be packaged in the fruit industry according to their degree of ripeness. Physically grading papaya fruit using human vision is time-consuming and destructive. A brand-new, non-destructive classification system for papaya fruit development stages is being offered as a result of this study. The project proposes to investigate three classification models: one deep learning method, the DWT approach, and a hybrid approach. A total of 300 papaya fruit sample photos were used in the experiment, 100 of which corresponded to each fruit's three ripeness stages: mid-ripen, ripen, and un-ripen. The maturity level of papaya is estimated using a hybrid network, i.e., the combination of high-level features and an SVM classifier. The high-level features are the integration of deep Features of VGG16 and coefficients of DWT. The accuracy and AUC of the proposed hybrid model are 98% and 100%, respectively.
ISSN:1633-1311
2116-7125
DOI:10.18280/isi.280119