期刊
COMPUTERS AND ELECTRONICS IN AGRICULTURE
卷 182, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.compag.2021.106054
关键词
Hydroponic lettuce seedlings; Deep learning; Object detection; Faster RCNN
资金
- Science and Technology Cooperation Program of He Bei, China-Development and demonstration of precise control system for facility hydroponic lettuce planting environment based on Internet of Things
This study proposes an automatic detection method for hydroponic lettuce seedlings based on an improved Faster RCNN framework to improve the efficiency and reduce the cost of seedlings sorting in the raising process. Using HRNet for image feature extraction and focal loss along with RoI Align to enhance the accuracy of seedlings detection, the method achieves a high mean average precision of 86.2% for hydroponic lettuce seedlings, outperforming other detectors like RetinaNet and SSD.
In order to improve the efficiency and reduce high cost for seedlings sorting in the raising process of hydroponic lettuce seedlings, we propose an automatic detection method for hydroponic lettuce seedlings based on improved Faster RCNN framework, taking the dead and double-planting status of seedlings growing in a single hole as our research objects. Since the characteristics of hydroponic lettuce seedlings are dense and small in the images, our model uses High Resolution Network (HRNet) as the backbone network for image feature extraction so as to obtain reliable and high-resolution feature expressions. Besides, we adopt focal loss as the classification loss in the Region Proposal Network (RPN) stage to address the imbalance between difficult and easy samples in seedlings classification. We also employ the Region of Interest (RoI) Align instead of the RoI Pooling layer to improve the detection accuracy of seedlings in the different status. The results show that the mean average precision of our method for the hydroponic lettuce seedlings is 86.2%, which is higher than RetinaNet, SSD, Cascade RCNN, FCOS and other detectors. Compared with different feature extraction networks, the detection accuracy of adopting HRNet performs nicely. Therefore, our method presented for the detection of hydroponic lettuce seedlings status can achieve high accuracy and identify seedlings in a problematic status well, which will provide technical support for automatic seedlings detection of hydroponic lettuce.
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