Papaya Fruit Maturity Estimation Using Wavelet and ConvNET.

Ratha, Ashoka Kumar; Barpanda, Nalini Kanta; Sethy, Prabira Kumar; Behera, Santi Kumari

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: midripen, 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.

PAPAYA; FEATURE extraction; FRUIT industry

Ingénierie des Systèmes d'Information, 2023, Vol 28, Issue 1, p175

1633-1311

Academic Journal

10.18280/isi.280119