A fine recognition method of strawberry ripeness combining Mask R-CNN and region segmentation - Tang, Can; Chen, Du; Wang, Xin; Ni, Xindong; Liu, Yehong; Liu, Yihao; Mao, Xu; Wang, Shumao (Frontiers in Plant Science, 2023)
As a fruit with high economic value, strawberry has a short ripeness period, and harvesting at an incorrect time will seriously affect the quality of strawberries, thereby reducing economic benefits. Therefore, the timing of its harvesting is very demanding. A fine ripeness recognition can provide more accurate crop information, and guide strawberry harvest management more timely and effectively. This study proposes a fine recognition method for field strawberry ripeness that combines deep learning and image processing. The method is divided into three stages: In the first stage, self-calibrated convolutions are added to the Mask R-CNN backbone network to improve the model performance, and then the model is used to extract the strawberry target in the image. In the second stage, the strawberry target is divided into four sub-regions by region segmentation method, and the color feature values of B, G, L, a and S channels are extracted for each sub-region. In the third stage, the strawberry ripeness is classified according to the color feature values and the results are visualized. Experimental results show that with the incorporation of self-calibrated convolutions into the Mask R-CNN, the model's performance has been substantially enhanced, leading to increased robustness against diverse occlusion interferences. As a result, the final average precision (AP) has improved to 0.937, representing a significant increase of 0.039 compared to the previous version. The strawberry ripeness classification effect is the best on the SVM classifier, and the accuracy under the combined channel BGLaS reaches 0.866. The classification results are better than common manual feature extraction methods and AlexNet, ResNet18 models. In order to clarify the role of the region segmentation method, the contribution of different sub-regions to each ripeness is also explored. The comprehensive results demonstrate that the proposed method enables the evaluation of six distinct ripeness levels of strawberries in the complex field environment. This method can provide accurate decision support for strawberry refined planting management.