Wheat Spike Detection and Counting in the Field Based on SpikeRetinaNet - Wen, Changji; Wu, Jianshuang; Chen, Hongrui; Su, Hengqiang; Chen, Xiao; Li, Zhuoshi; Yang, Ce (Frontiers in Plant Science, 2022)
The number of wheat spikes per unit area is one of the most important agronomic traits associated with wheat yield. However, quick and accurate detection for the counting of wheat spikes faces persistent challenges due to the complexity of wheat field conditions. This work has trained a RetinaNet (SpikeRetinaNet) based on several optimizations to detect and count wheat spikes efficiently. This RetinaNet consists of several improvements. Firstly, a weighted bi-directional feature pyramid network (FPN) was introduced into the FPN of RetinaNet, which could fuse multi-scale features to recognize wheat spikes in different varieties and complicated environments. Then, to detect objects more efficiently, focal loss and attention modules were added. Finally, soft non-maximum suppression (Soft-NMS) was used to solve the occlusion problem. Based on these improvements, the new network detector was created and tested on the Global Wheat Head Detection (GWHD) dataset supplemented with Wheat-wheatgrass Spike Detection (WSD) images. The WSD images were supplemented with new varieties of wheat, making the mixed dataset richer in species. The method of this study achieved 0.9262 for mAP50, which improved by 5.59%, 49.06%, 2.79%, 1.35% and 7.26% compared to the state-of-the-art RetinaNet, single shot multiBox detector (SSD), you only look once version3 (Yolov3), you only look once version4 (Yolov4) and faster region-based convolutional neural network (Faster-RCNN), respectively. In addition, the counting accuracy reached 0.9288, which was improved from other methods as well. Our implementation code and partial validation data are available at https://github.com/wujians122/The-Wheat-Spikes-Detecting-and-Counting.