Wang Nan, Cao Haijuan, Huang Xia, Ding Mingquan
The Key Laboratory for Quality Improvement of Agricultural Products of Zhejiang Province, College of Advanced Agricultural Sciences, Zhejiang A&F University, Hangzhou 311300, China.
Plants (Basel). 2024 Aug 27;13(17):2388. doi: 10.3390/plants13172388.
Accurately quantifying flora and their respective anatomical structures within natural ecosystems is paramount for both botanical breeders and agricultural cultivators. For breeders, precise plant enumeration during the flowering phase is instrumental in discriminating genotypes exhibiting heightened flowering frequencies, while for growers, such data inform potential crop rotation strategies. Moreover, the quantification of specific plant components, such as flowers, can offer prognostic insights into the potential yield variances among different genotypes, thereby facilitating informed decisions pertaining to production levels. The overarching aim of the present investigation is to explore the capabilities of a neural network termed GhP2-YOLO, predicated on advanced deep learning techniques and multi-target tracking algorithms, specifically tailored for the enumeration of rapeseed flower buds and blossoms from recorded video frames. Building upon the foundation of the renowned object detection model YOLO v8, this network integrates a specialized P2 detection head and the Ghost module to augment the model's capacity for detecting diminutive targets with lower resolutions. This modification not only renders the model more adept at target identification but also renders it more lightweight and less computationally intensive. The optimal iteration of GhP2-YOLOm demonstrated exceptional accuracy in quantifying rapeseed flower samples, showcasing an impressive mean average precision at 50% intersection over union metric surpassing 95%. Leveraging the virtues of StrongSORT, the subsequent tracking of rapeseed flower buds and blossom patterns within the video dataset was adeptly realized. By selecting 20 video segments for comparative analysis between manual and automated counts of rapeseed flowers, buds, and the overall target count, a robust correlation was evidenced, with R-squared coefficients measuring 0.9719, 0.986, and 0.9753, respectively. Conclusively, a user-friendly "Rapeseed flower detection" system was developed utilizing a GUI and PyQt5 interface, facilitating the visualization of rapeseed flowers and buds. This system holds promising utility in field surveillance apparatus, enabling agriculturalists to monitor the developmental progress of rapeseed flowers in real time. This innovative study introduces automated tracking and tallying methodologies within video footage, positioning deep convolutional neural networks and multi-target tracking protocols as invaluable assets in the realms of botanical research and agricultural administration.
准确量化自然生态系统中的植物群落及其各自的解剖结构，对植物育种者和农业种植者来说都至关重要。对于育种者而言，在开花阶段精确统计植物数量有助于区分开花频率较高的基因型，而对于种植者来说，此类数据可为潜在的作物轮作策略提供参考。此外，对特定植物组成部分（如花朵）的量化，可以对不同基因型之间的潜在产量差异提供预测性见解，从而有助于就生产水平做出明智决策。本研究的总体目标是探索一种名为GhP2 - YOLO的神经网络的能力，该网络基于先进的深度学习技术和多目标跟踪算法，专门用于从录制的视频帧中统计油菜花芽和花朵数量。在著名的目标检测模型YOLO v8的基础上，该网络集成了一个专门的P2检测头和Ghost模块，以增强模型检测低分辨率小目标的能力。这种改进不仅使模型更擅长目标识别，还使其更轻量级且计算量更小。GhP2 - YOLOm的最佳迭代在量化油菜花样本方面表现出卓越的准确性，在50%交并比指标下展示了超过95%的令人印象深刻的平均精度。利用StrongSORT的优势，随后在视频数据集中对油菜花芽和花朵模式进行了有效跟踪。通过选择20个视频片段进行油菜花、花蕾以及总体目标数量的人工计数与自动计数之间的比较分析，可以证明存在很强的相关性，决定系数R平方分别为0.9719、0.986和0.9753。最后，利用图形用户界面（GUI）和PyQt5接口开发了一个用户友好的“油菜花检测”系统，便于可视化油菜花和花蕾。该系统在田间监测设备中具有广阔的应用前景，使农业工作者能够实时监测油菜花的发育进程。这项创新性研究在视频片段中引入了自动跟踪和计数方法，将深度卷积神经网络和多目标跟踪协议定位为植物研究和农业管理领域的宝贵资产。
