【摘要】 针对穴盘苗叶片之间相互覆盖难以利用俯视图像判断种苗品质的问题,该研究以白掌苗为研究对象,提出一种叶片下观测苗茎局部区域的方法,通过提取穴盘苗叶片下苗茎参数,结合种苗级别判断标准,实现叶片相互覆盖穴盘苗的自动化品质检测。该方法首先确定白掌苗苗茎品质分级临界值,并构建由微型相机和导光纤维组成的苗茎图像采集单元,在检测室暗室环境中捕获白掌苗叶片下光纤光斑区域苗茎图像,利用视觉算法提取苗茎图像和苗茎投影面积,通过提取的待测白掌苗苗茎投影面积与白掌苗苗茎品质分级临界值对比分析,确定不合格苗,并返回不合格苗穴孔位置信息。试验结果表明,穴盘苗品质检测准确度主要受种苗在穴中位置和输送速度影响,当苗偏离穴中心10 mm以上时,种苗品质检测准确度最低降至85%以下。当种苗品质接近分级临界值时,种苗品质检测准确度略微下降,但不显著(P>0.05)。针对72孔待售白掌穴盘苗进行品质检测试验,试验结果表明,当输送带速度为0.045 m/s,苗茎偏离距离在10 mm内,系统的识别准确率可达97.92%,对应生产率为150盘/h(10 800株/h)。该研究可为存在相邻叶片覆盖时穴盘苗分级、品质检测的自动化评估提供理论指导和参考。更多还原

【Abstract】 Plug seedlings have been widely used in the production of vegetable and flower planting. The consistent quality of plug seedlings depends mainly on economic benefits. It is usually necessary to identify and remove unqualified seedlings from the plugs, and then replace them with qualified seedlings. The manual operation of substandard seedlings is mainly used from the plugs to the supplement seedling at present, indicating low efficiency, high labor costs, and unstable classification. The seedling sorting machine using machine vision can automatically identify the lack of seedling holes and unqualified seedlings,and then remove the unqualified seedlings from the plug trays. The accurate classification can be achieved with higher operation efficiency. The top view images are selected to judge the quality of plug seedlings with no crossed leaves and no mutual obscuration. However, the leaves of adjacent seedlings cross each other or are blocked and covered, when most plug seedlings of flower and vegetable are sold. It cannot be evaluated on the growth status and quality of individual seedlings using the top view image. Taking the Spathiphyllum seedlings as the research object, this study aims to observe the local area of seedling stem under the leaves using perspective images under the leaves. An automatic quality inspection of plug seedlings was realized to combine with the judging standard of seedling level, particularly on the stem image covering each other with leaves. Firstly, the critical value of the projection area of the stem of Spathiphyllum seedlings was proposed, according to the production standards. Secondly, an image acquisition unit of the seedling stem was constructed, consisting of a leaf guide piece,a miniature camera, and two light guide fibers. Subsequently, the stem images were captured under the leaf of Spathiphyllum seedlings in the darkroom. Then, the PC vision was utilized to analyze the images and projection area of the seedling stem. The seedlings were determined to be qualified or not, according to the quality evaluation on the projection area and the critical value of the Spathiphyllum seedling stem. The hole positions of unqualified seedlings were returned to PLC at last. A three-factor three-level test was carried out to select the conveyor speed, where the deviation of the center distance between seedling stem and hole in the shooting direction, the deviation rate-How closed the projection area of the stem to the Critical Value of the Projection Area of Stem(CVA) as the test factors. The quality test results show that the accuracy of quality detection of plug seedlings depended mainly on the deviation distance and conveyor speed. Specifically, the accuracy of quality detection dropped bellow 85%, when the seedling deviated from the hole center greater than 10 mm and the conveyor speed increased to 0.06 m/s. But there was no significant impact when the projection area of the stem was close to CVA. In addition, the quality inspection test was carried out on 72 holes of Spathiphyllum plug seedlings. It was found that the recognition accuracy of the system reached 97.92%, and the productivity was 150 tray/h, and 10 800 plant/h, when the conveyor speed was 0.045 m/s and the deviation distance of seedling stem was within 10 mm. This finding can provide a strong theoretical reference for the automatic evaluation of plug seedlings grading and quality inspection, particularly when adjacent leaves were covered.更多还原