基于全景图像的玉米果穗流水线考种方法及系统

为提高玉米果穗考种效率和精度,该文提出一种基于全景图像的玉米果穗流水线考种方法和系统。利用托辊传送装置实现果穗自动连续推送,基于工业相机自动检测果穗运动状态并实时采集图像,获取覆盖果穗全表面的图像序列;建立果穗运动、摄像机成像、表面拼接关系,从图像序列中抽取果穗中心畸变最小区域拼接出果穗表面全景图像;最后,结合果穗边界检测、籽粒分割和有效性鉴定等技术提取出果穗表面上有效籽粒。试验结果表明,该文方法和系统较好地平衡了玉米果穗考种的效率和精度,图像采集和计算平均效率达15穗/min和4穗/min,穗长和穗行数指标计算精度可达99%和98.89%,可为研发全自动、高通量玉米果穗表型检测装置提供有益借鉴。

Assembly line variety test method and system for corn ears based on panoramic surface image

The phenotypic traits of corn ear are important quantitative data in maize breeding and variety identification. In tradition, breeding workers are employed to deal with lots of corn ears by means of manual measurement and visual count, however this process is seriously labor-consuming and time-costing, and the measured traits are prone to be subjective and incomplete. In recent years, some semi-automatic systems based on machine vision and image analysis have been developed and applied to the maize variety test, however fully automated test system is still a challenge task owing to the strict high-throughput and high-precision requirements in large-scale maize breeding. To balance efficiency and accuracy of variety test for corn ears, in this paper, a high-throughput phenotypic measurement method and system based on panoramic surface image is proposed. Firstly, a novel mechanic system is proposed, which automatically conveys corn ears above a chain-roller structure, while the rolling corn ears are continuously imaged by a fixed industrial camera that is perpendicular to the moving plane of corn ear. In only several seconds, dozens of side images in which corn ears are in different positions can be collected to generate the image dataset of single corn ear. By analyzing the movement state of corn ear, a transformation model which describes the relationship among ear roll, camera imaging and surface position is then built to bridge the image sequence and the panoramic surface image of corn ear. Corn ears in the image sequence are respectively segmented and the center axes are dynamically determined by figuring out the shape and bounding box. This model always extracts the most appropriate sub regions of corn ear from image sequence, and then stitches them to the calculated positions on the panoramic surface image. As a result, the panoramic image of corn ear demonstrates the three-dimensional surface information in a two-dimensional image, and thus provides more intuitive and complete way for phenotyping calculation of corn ear. The valid surface region of corn ear in the panoramic image is further determined by the boundary detection technique that is performed by evaluating the perimeters of corn ear in the image sequence. Robust kernel segmentation based on hierarchical threshold method is also utilized to extract all candidate kernels which satisfy area and shape constraint, and some more restrictive filters based on machine learning methods, such as SVM (support vector machine), can also be taken to evaluate the validation of kernels. The segmented kernels in the panoramic image are used to calculate the total kernels, number of ear rows and kernels per row. The experimental results show that the proposed method and system can achieve optimized efficiency and accuracy balance. High-throughput convey mechanism improves the efficiency of image acquisition to 15 ears per minute. Compared with the methods based on single and multiple images, the variety test method based on panoramic surface image can make full use of the entire surface information of corn ear and reveal its individual phenotypic traits. The computation accuracies of ear length, ear diameter, number of ear rows, kernels per row and total kernels are up to 99%, 91.84%, 97.15%, 98.89% and 95.37% respectively.