Xu Rui, Li Changying, Paterson Andrew H, Jiang Yu, Sun Shangpeng, Robertson Jon S
Bio-Sensing and Instrumentation Lab, College of Engineering, University of Georgia, Athens, GA, United States.
Plant Genome Mapping Laboratory, Department of Genetics, University of Georgia, Athens, GA, United States.
Front Plant Sci. 2018 Feb 16;8:2235. doi: 10.3389/fpls.2017.02235. eCollection 2017.
Monitoring flower development can provide useful information for production management, estimating yield and selecting specific genotypes of crops. The main goal of this study was to develop a methodology to detect and count cotton flowers, or blooms, using color images acquired by an unmanned aerial system. The aerial images were collected from two test fields in 4 days. A convolutional neural network (CNN) was designed and trained to detect cotton blooms in raw images, and their 3D locations were calculated using the dense point cloud constructed from the aerial images with the structure from motion method. The quality of the dense point cloud was analyzed and plots with poor quality were excluded from data analysis. A constrained clustering algorithm was developed to register the same bloom detected from different images based on the 3D location of the bloom. The accuracy and incompleteness of the dense point cloud were analyzed because they affected the accuracy of the 3D location of the blooms and thus the accuracy of the bloom registration result. The constrained clustering algorithm was validated using simulated data, showing good efficiency and accuracy. The bloom count from the proposed method was comparable with the number counted manually with an error of -4 to 3 blooms for the field with a single plant per plot. However, more plots were underestimated in the field with multiple plants per plot due to hidden blooms that were not captured by the aerial images. The proposed methodology provides a high-throughput method to continuously monitor the flowering progress of cotton.
监测花朵发育可为生产管理、产量估算和作物特定基因型选择提供有用信息。本研究的主要目标是开发一种方法，利用无人机系统获取的彩色图像来检测和计数棉花花朵（即花）。在4天内从两个试验田收集了航拍图像。设计并训练了一个卷积神经网络（CNN）来检测原始图像中的棉花花朵，并使用通过运动结构方法从航拍图像构建的密集点云计算其三维位置。分析了密集点云的质量，并将质量较差的地块排除在数据分析之外。开发了一种约束聚类算法，根据花朵的三维位置对从不同图像中检测到的同一花朵进行配准。分析了密集点云的准确性和不完整性，因为它们影响花朵三维位置的准确性，进而影响花朵配准结果的准确性。使用模拟数据对约束聚类算法进行了验证，结果表明该算法具有良好的效率和准确性。对于每块地种植单株棉花的地块，所提方法的花朵计数与人工计数结果相当，误差为-4至3朵花。然而，对于每块地种植多株棉花的地块，由于航拍图像未捕捉到隐藏的花朵，更多的地块被低估了。所提方法提供了一种高通量方法，可用于持续监测棉花的开花进程。
