A Hybrid Deep Learning Framework for Strawberry Ripeness and Quality Identification Through Color Space Analysis
Strawberry (Fragaria x ananassa) is one of Indonesia’s most popular and economically valuable fruits, rich in nutrients beneficial for health. The ripeness level significantly determines fruit quality, affecting flavor, texture, and nutritional value. Therefore, selecting the appropriate ripeness level is essential to improve strawberry quality. However, conventional harvesting often leads to time wastage and inconsistencies in ripeness assessment. To address this, YOLOv7 is used for object detection of strawberry images, and EfficientNetV2S is employed for classification detection. The dataset consists of 2300 images with 5 classes: Fully Ripe Grade A, Fully Ripe Grade B, Half Ripe Grade A, Half Ripe Grade B, and Unripe. The dataset is transformed into different color spaces, RGB, HSV, Lab*, and YIQ, to classify strawberries' ripeness level and quality. Experimental results show that the RGB color space achieved an accuracy of 98%, while HSV and YIQ obtained 97% accuracy. On the other hand, Lab* showed the lowest performance with 95% accuracy. Specifically, within the RGB color space, the green layer achieved an accuracy of 100%. This indicates that the RGB with the green layer method effectively learns data patterns and provides excellent accuracy in measuring the ripeness and quality of strawberries, thereby improving crop productivity, efficiency, and quality.