Li Jie, Li Yi, Qiao Jiangwei, Li Li, Wang Xinfa, Yao Jian, Liao Guisheng
Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan, China.
Key Laboratory of Biology and Genetic Improvement of Oil Crops, Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences, Wuhan, China.
Front Plant Sci. 2023 Jan 31;14:1101143. doi: 10.3389/fpls.2023.1101143. eCollection 2023.
Flowering is a crucial developing stage for rapeseed ( L.) plants. Flowers develop on the main and branch inflorescences of rapeseed plants and then grow into siliques. The seed yield of rapeseed heavily depends on the total flower numbers per area throughout the whole flowering period. The number of rapeseed inflorescences can reflect the richness of rapeseed flowers and provide useful information for yield prediction. To count rapeseed inflorescences automatically, we transferred the counting problem to a detection task. Then, we developed a low-cost approach for counting rapeseed inflorescences using YOLOv5 with the Convolutional Block Attention Module (CBAM) based on unmanned aerial vehicle (UAV) Red-Green-Blue (RGB) imagery. Moreover, we constructed a Rapeseed Inflorescence Benchmark (RIB) to verify the effectiveness of our model. The RIB dataset captured by DJI Phantom 4 Pro V2.0, including 165 plot images and 60,000 manual labels, is to be released. Experimental results showed that indicators for counting and the mean Average Precision (mAP) for location were over 0.96 and 92%, respectively. Compared with Faster R-CNN, YOLOv4, CenterNet, and TasselNetV2+, the proposed method achieved state-of-the-art counting performance on RIB and had advantages in location accuracy. The counting results revealed a quantitative dynamic change in the number of rapeseed inflorescences in the time dimension. Furthermore, a significant positive correlation between the actual crop yield and the automatically obtained rapeseed inflorescence total number on a field plot level was identified. Thus, a set of UAV- assisted methods for better determination of the flower richness was developed, which can greatly support the breeding of high-yield rapeseed varieties.
开花是油菜（L.）植株的一个关键发育阶段。油菜花在油菜植株的主花序和侧花序上发育，然后长成角果。油菜籽的种子产量在很大程度上取决于整个花期每单位面积的总花数。油菜花序数量能够反映油菜花的丰富程度，并为产量预测提供有用信息。为了自动计数油菜花序，我们将计数问题转化为一个检测任务。然后，我们基于无人机红-绿-蓝（RGB）图像，开发了一种使用带有卷积块注意力模块（CBAM）的YOLOv5来计数油菜花序的低成本方法。此外，我们构建了一个油菜花序基准数据集（RIB）来验证我们模型的有效性。由大疆御Mavic 4 Pro V2.0拍摄的RIB数据集，包括165张地块图像和60000个手动标注，即将发布。实验结果表明，计数指标和定位的平均精度均值（mAP）分别超过0.96和92%。与Faster R-CNN、YOLOv4、CenterNet和TasselNetV2+相比，该方法在RIB数据集上实现了先进的计数性能，并且在定位精度方面具有优势。计数结果揭示了油菜花序数量在时间维度上的定量动态变化。此外，在田间地块水平上，确定了实际作物产量与自动获取的油菜花序总数之间存在显著正相关。因此，开发了一套用于更好地确定花丰富度的无人机辅助方法，这可以极大地支持高产油菜品种的育种。
