Fruit maturity grading framework for small dataset using single image multi-object sampling and Mask R-CNN

Abstract

Fruit maturity grading is the key factor for fruit export where maturity consistence and standard are required. This paper proposes a non-destructive approach for pineapple maturity grading and pineapple localization based on object segmentation framework which has enhanced for training a robust model with small dataset. We introduced a multi-object sampling technique in augmentation process to generate images from a small dataset taken under controlled conditions to generalize the model for a more practical dataset. We identify the robustness of object segmentation models, Mask R-CNN over other models, e.g., Faster R-CNN, RetinaNet and CenterMask through mean average precision (mAP), detection ratio to explore the false positives detected, precision-recall curve and computational time. The optimal threshold selection which is crucial especially for sensitive small dataset to achieve high detection performance is proposed in this work using mAP and detection ratio. Our proposed framework enhances the model generalization and achieves mAP of 86.7%, AP50 and AP75 at 97.98% and APunripe, APpartially_ripe and APfully_ripe of 99.20%, 96.58% and 98.63%, respectively. Additional insights of the models developed with our small dataset are explained along with the experimental results which are suggested for future.

Keywords

• Multi-object sampling
• Instance segmentation models
• Small dataset
• Maturity grading
• Object localization
• Pineapple