蔬菜穴盘苗自动补苗试验台穴孔定位与缺苗检测系统
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摘要
为了精确获得蔬菜穴盘育苗空穴信息并为自动补苗提供依据,研制了蔬菜穴盘苗自动补苗试验台。利用该试验台获取了苗龄25、35 d的拟南介穴盘苗彩色图像,对彩色图像依次进行灰度化处理、Otsu阈值分割得到幼苗和穴盘二值图;对幼苗二值图进行开运算去除噪声,提取出幼苗特征图像;将穴盘二值图去除幼苗图像并去除噪声获得穴盘特征图像,依据穴盘特征图像分别在行、列上的像素统计峰值、峰宽及穴盘规格化结构,精确确定了穴孔边界;对穴孔内幼苗图像像素统计以判定是否空穴,结果表明:25、35 d拟南芥穴盘苗有苗穴孔与无苗穴孔内像素统计值差异极显著,空穴、有苗穴判断正确率均为100%,为穴盘苗空穴自动补苗提供了精确的幼苗信息与穴孔位置。
The seeds cannot fully sprout owing to the seed quality, sowing precision and environmental differences. In order to get the accurate information of the null plug holes and the boundary of plug holes and provide the basis for automatic seedling supplement device, a automatic seedling supplementing test-bed was developed with seedling shortage detecting system and seedling supplementing system as core components. The seedling shortage detection system was composed of a hardware system for image processing, which include a CMOS industrial camera, a controller and a computer, and a software system programmed in MATLAB. Color images(RGB images) of Arabidopsis plug seedlings with the age of 25 and 35 d was acquired with the automatic seedling supplementing test-bed. The grayscale images of seedlings and plug holes are obtained by graying the color images applying different linear transformations to three color components of R, G and B. Applying Otsu algorithm, binary images of the grayscale images of seedlings were obtained by threshold segmentation. Then the morphological corrosion operation and expansion operation of the binary images were carried out by the disk 2 × 2 type structure operator. By marking single connected domain, analyzing the characteristics of connected domain and removing the isolated area, the noise in the binary images were removed and the characteristic images of the seedlings were extracted from the background effectively. The feature images of plug tray were acquired by removing feature images of plug seedlings from that of the plug tray according to the extracted seedling information and de-noising. And then statistics on the peak value, peak width and standardizing structure of plug tray were made according to the row and column pixel of binary images of plug trays, so that the edges of plug holes were determined accurately. The statistics of the pixel of the seedling image in each plug hole were made to determine whether the hole was short of seedlings according to the feature images of the seedlings, the position, and the edge information of the plug holes. The results showed that the statistics value of the seedlings image pixel of the plug holes with the Arabidopsis plug seedlings of 25 and 35 d were 1 895 to 4 572, and 3 386 to 8 710, respectively, while the statistics value of the seedlings image pixel of the plug holes without seedlings was 0. There were significant differences in the statistics value of the seedling image pixels between the plug holes with seedlings and the null plug holes. The testing results of the missing plug tray hole were the same as the actual situation, and the statistical value of seedling pixels corresponds to the projection area of stem and leaf. According to the threshold value of seedlings at different growth stages, the undeveloped seedlings can be marked for removing, so the seedling early stage was the super time for the detection of the null plug hole and the determination of the undeveloped seedlings. The accuracy rate of judging the null plug holes and the seedling holes with the Arabidopsis plug seedlings of 25 and 35 d were all 100% applying the detection device. The accurate determination of position of the null plug hole provides basis for automatic supplementing system taking out the substrate without seedlings, removing dysplasia seedlings and supplementing the healthy seedlings with same age.
The seeds cannot fully sprout owing to the seed quality, sowing precision and environmental differences. In order to get the accurate information of the null plug holes and the boundary of plug holes and provide the basis for automatic seedling supplement device, a automatic seedling supplementing test-bed was developed with seedling shortage detecting system and seedling supplementing system as core components. The seedling shortage detection system was composed of a hardware system for image processing, which include a CMOS industrial camera, a controller and a computer, and a software system programmed in MATLAB. Color images(RGB images) of Arabidopsis plug seedlings with the age of 25 and 35 d was acquired with the automatic seedling supplementing test-bed. The grayscale images of seedlings and plug holes are obtained by graying the color images applying different linear transformations to three color components of R, G and B. Applying Otsu algorithm, binary images of the grayscale images of seedlings were obtained by threshold segmentation. Then the morphological corrosion operation and expansion operation of the binary images were carried out by the disk 2 × 2 type structure operator. By marking single connected domain, analyzing the characteristics of connected domain and removing the isolated area, the noise in the binary images were removed and the characteristic images of the seedlings were extracted from the background effectively. The feature images of plug tray were acquired by removing feature images of plug seedlings from that of the plug tray according to the extracted seedling information and de-noising. And then statistics on the peak value, peak width and standardizing structure of plug tray were made according to the row and column pixel of binary images of plug trays, so that the edges of plug holes were determined accurately. The statistics of the pixel of the seedling image in each plug hole were made to determine whether the hole was short of seedlings according to the feature images of the seedlings, the position, and the edge information of the plug holes. The results showed that the statistics value of the seedlings image pixel of the plug holes with the Arabidopsis plug seedlings of 25 and 35 d were 1 895 to 4 572, and 3 386 to 8 710, respectively, while the statistics value of the seedlings image pixel of the plug holes without seedlings was 0. There were significant differences in the statistics value of the seedling image pixels between the plug holes with seedlings and the null plug holes. The testing results of the missing plug tray hole were the same as the actual situation, and the statistical value of seedling pixels corresponds to the projection area of stem and leaf. According to the threshold value of seedlings at different growth stages, the undeveloped seedlings can be marked for removing, so the seedling early stage was the super time for the detection of the null plug hole and the determination of the undeveloped seedlings. The accuracy rate of judging the null plug holes and the seedling holes with the Arabidopsis plug seedlings of 25 and 35 d were all 100% applying the detection device. The accurate determination of position of the null plug hole provides basis for automatic supplementing system taking out the substrate without seedlings, removing dysplasia seedlings and supplementing the healthy seedlings with same age.
引文
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[22]贺磊盈,杨太玮,武传宇,等.基于贪心遗传算法的穴盘苗补栽路径优化[J].农业机械学报,2017,48(5):36-43.He Leiying,Yang Taiwei,Wu Chuanyu,et al.Optimization of replugging tour planning based on greedy genetic algorithm[J].Transactions of the Chinese Society for Agricultural Machinery,2017,48(5):36-43.(in Chinese with English abstract)
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