Non-Destructive Judgment of Watermelon Ripeness Based on Multi-Feature Fusion of Image

• Published in: 2024 20th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) pp. 1 - 8
• Main Authors: Shi, Hongbao, Liu, Jun, Huang, Yimei, Li, Jinping
• Format: Conference Proceeding
• Language: English
• Published: IEEE 27.07.2024
• Subjects: Accuracy; Deep learning; feature fusion; Guidelines; Knowledge discovery; Laser theory; Machine vision; Mobile handsets; Morphology; Reliability; ResNet; ripeness; Swin-Transformer; Vibrometers; watermelon
• DOI: 10.1109/ICNC-FSKD64080.2024.10702304

The nondestructive judgment of watermelon ripeness is a key issue in everyday life. Whether watermelons are ripe or not affects the interests of watermelon farmers and consumers. So far, acoustic, electrical, machine vision and other methods have been applied to the nondestructive judgment of agricultural product ripeness, but the equipment used in the lab, such as spectrometers and laser Doppler vibrometers, is expensive, difficult to carry, and has limited practical value in daily life. Based on practical experience, the ripeness of watermelons can be judged by observing specific physical characteristics, such as the rind's glossiness, texture, fuzz on the fruit stalk and navel. In this study, images of the watermelon's texture, fuzz and navel were captured using a smartphone. We utilized ResN et with an embedded Coordinate Attention mechanism and Swin-Transformer networks to independently analyze the ripeness of each feature. Then we combined the output prediction scores using post-fusion technology, with the highest weighted prediction score judging the final ripeness assessment. Our method achieved a 90.2% accuracy rate on the test set. The experiments demonstrate that a deep learning model utilizing multi-feature fusion of image data can reliably and accurately identify watermelon ripeness, even under varying image conditions.