| 雾滴沉积特性参数的图像检测算法改进 |
| |
| 引用本文： | 郭娜,刘思瑶,须晖,田素博,李天来.雾滴沉积特性参数的图像检测算法改进[J].农业工程学报,2018,34(17):176-182. |
| |
| 作者姓名： | 郭娜 刘思瑶 须晖 田素博 李天来 |
| |
| 作者单位： | 沈阳农业大学园艺学院;沈阳农业大学工程学院;设施园艺省部共建教育部重点试验室 |
| |
| 基金项目： | 国家重点研发计划（2016YFD0200708） |
| |
| 摘 要： | 快速获取施药后雾滴在靶标表面的沉积分布有助于了解农药的田间分布情况,水敏试纸雾滴图像处理算法是检测喷药沉积特性参数常用的方法,但常因光照不均、试纸上沉积的雾滴斑痕粘连而引起雾滴识别误差。为解决这一问题,针对手机拍摄的照片,该文提出了与位置相关的动态阈值法提取雾滴区域,并设计基于圆形度的循环分割算法对粘连雾滴分割。以水代替农药利用背负式喷雾器喷洒,选取8张不同稀疏程度和碰撞角度的试纸作为样本进行试验,以验证上述算法的检测效果。试验结果表明,该方法不受亮度不均影响,覆盖率比固定阈值与分块阈值法分别高12.57%和8.74%,提取到的雾滴区域更加完整,能够提取92.64%以上的雾滴,且粘连分割效果较好,雾滴识别的正确率为97.2%,覆盖密度检测误差为3.31%,能够满足实际生产要求,为下一步开发雾滴检测APP打下基础。
|
| 关 键 词： | 图像处理 图像分割 算法 水敏试纸 雾滴 沉积分布 动态阈值 粘连分割 |
| 收稿时间： | 2018/3/2 0:00:00 |
| 修稿时间： | 2018/5/23 0:00:00 |
Improvement on image detection algorithm of droplets deposition characteristics |
| |
| Guo N,Liu Siyao,Xu Hui,Tian Subo and Li Tianlai.Improvement on image detection algorithm of droplets deposition characteristics[J].Transactions of the Chinese Society of Agricultural Engineering,2018,34(17):176-182. |
| |
| Authors: | Guo N Liu Siyao Xu Hui Tian Subo and Li Tianlai |
| |
| Institution: | 1. College of Horticulture, Shenyang Agricultural University, Shenyang 110866, China; 2. College of Engineering, Shenyang Agricultural University, Shenyang 110866, China; 3. Key Laboratory of Protected Horticulture Ministry of Education, Shenyang 110866, China;,2. College of Engineering, Shenyang Agricultural University, Shenyang 110866, China;,1. College of Horticulture, Shenyang Agricultural University, Shenyang 110866, China; 3. Key Laboratory of Protected Horticulture Ministry of Education, Shenyang 110866, China;,2. College of Engineering, Shenyang Agricultural University, Shenyang 110866, China; 3. Key Laboratory of Protected Horticulture Ministry of Education, Shenyang 110866, China; and 1. College of Horticulture, Shenyang Agricultural University, Shenyang 110866, China; 3. Key Laboratory of Protected Horticulture Ministry of Education, Shenyang 110866, China; |
| |
| Abstract: | Droplets deposition characteristics estimation is helpful to know the pesticides deposition distribution on crops, which is related to the crop spray quality directly, and especially the fast detection method will provide the basis for improving the pesticides spraying technology. Droplets image processing based on the water sensitive paper is one of the most common methods to detect the droplets deposition characteristics. The droplets coverage percentage and coverage density were selected to evaluate deposition distribution in this paper. With the development of science and technology, a smart phone was selected as image acquiring tool to replace the special image acquisition system, and the improved image processing algorithm of the water sensitive paper was developed. There are 3 steps in the image processing algorithm to obtain each droplet stain, which are image preprocessing, droplets area segmentation, and overlapped droplets segmentation. Firstly, the image was enhanced and calibrated by a calibration board, and then the image of water sensitive paper was segmented from the R channel and B channel image and transferred to a gray scale image. Secondly, the blue droplets stain area was segmented from yellow paper background, and the dynamic threshold method based on the pixel position was used to solve the problem of the influence of uneven brightness in this step, in which the gray mean value of an area of 25×25 pixels was calculated as the segmentation threshold for the middle pixel, and the droplets coverage percentage was calculated by the stains pixel number divided by the pixel number of water sensitive paper area. Thirdly, the circulatory segmentation method based on region circularity was designed to segment the multiple-droplet overlapped regions. Based on the statistical analysis, 0.6 was selected as the circularity threshold, less than 0.6 was considered to be overlapped droplets, and first erosion and then dilation based on a coefficient of corrosion and expansion r was performed; next the segmented area circularity was recalculated, the area with the circularity of less than 0.6 was selected again and segmented by the coefficient (r+1), and the erosion-dilation operation would be repeated over and over until the circularity of all stains was greater than 0.6. Finally, the identified droplets were marked as circle, and the droplets coverage density was calculated by droplets number divided by the water sensitive paper area. Experiments were conducted to test and verify the detection advancement of the proposed image processing algorithm. Experimental results showed that the dynamic threshold segmentation method is not affected by the uneven brightness and can extract 92.64% of droplets, and the droplet coverage percentage detection result is 12.57% and 8.74% greater than constant threshold and partitioned threshold method respectively. Moreover, the proposed overlapped droplets segmentation algorithm can segment successfully more than 2 droplets and the long and thin droplets, the accuracy of droplets identification is 97.2%, and the coverage density detection results showed that the relative error between the algorithm in this paper and manual counting is only 3.31%. The results indicated that the proposed image detection algorithm of droplets deposition characteristics is efficient and convenient, and can completely fulfill the demand of droplets deposition characteristics detection in the field, and the corresponding smartphone applications are in development. |
| |
| Keywords: | image processing image segmentation algorithm water sensitive paper droplets spray deposition dynamic threshold overlapped region segmentation |
| 本文献已被 CNKI 等数据库收录！ |
| 点击此处可从《农业工程学报》浏览原始摘要信息 |
| 点击此处可从《农业工程学报》下载免费的PDF全文 |
|
