Automated image-processing for counting seedlings in a wheat field

被引:0
作者
Tao Liu
Wei Wu
Wen Chen
Chengming Sun
Xinkai Zhu
Wenshan Guo
机构
[1] Yangzhou University,Jiangsu Key Laboratory of Crop Genetics and Physiology/Co
来源
Precision Agriculture | 2016年 / 17卷
关键词
Wheat; Seedling counting; Image processing; Morphological characters; Digital camera;
D O I
暂无
中图分类号
学科分类号
摘要
Wheat field seedling density has a significant impact on the yield and quality of grains. Accurate and timely estimates of wheat field seedling density can guide cultivation to ensure high yield. The objective of this study was to develop an image-processing based, automatic counting method for wheat field seedlings, to investigate the principle of automatic counting of wheat emergence in the field, and to validate the newly developed method in various conditions. Digital images of the wheat fields at seedling stages with five cultivars and five seedling densities were acquired directly from above the fields. The wheat seedlings information was extracted from the background using excessive green and Otsu’s method. By analyzing the characteristic parameters of the overlapping regions (Overlapping region is a number of overlapping wheat seedlings in the image) of the fields, a chain code-based skeleton optimization method and corresponding equation were established for automatic counting of wheat seedlings in the overlapping regions. The results showed that the newly developed method can effectively count the number of wheat seedlings, with an average accuracy rate of 89.94 % and a highest accuracy rate of 99.21 %. The results also indicated that the accuracy of counting was not affected by different cultivars. However, the seedling density had significant impact on the counting accuracy (P < 0.05). When the seedling density was between 120 × 104 and 240 × 104 ha−1, high counting accuracy (>92 %) could be obtained. The study demonstrated that the newly developed method is reliable for automatic wheat seedlings counting, and also provides a theoretical perspective for automatic seedling counting in the wheat field.
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收藏
页码:392 / 406
页数:14
相关论文
共 64 条
[1]  
Bai X(2009)Splitting touching cells based on concave points and ellipse fitting Pattern Recognition 42 2434-2446
[2]  
Sun C(2000)An efficient watershed algorithm based on connected components Pattern Recognition 33 907-916
[3]  
Zhou F(2011)Effects of interaction between density and nitrogen on grain yield and nitrogen use efficiency of winter wheat Plant Nutrition and Fertilizer Science 17 815-822
[4]  
Bieniek A(1992)Location of the maize plant with machine vision Journal of Agricultural Engineering Research 52 169-181
[5]  
Moga A(2013)Estimation of rice growth and nitrogen nutrition status using color digital camera image analysis European Journal Agronomy 48 57-65
[6]  
Cao Q(2014)A novel matching algorithm for splitting touching rice kernels based on contour curvature analysis Computers and Electronics Agriculture 109 124-133
[7]  
He MR(2006)Effect of planting density on grain yield and quality of weak-gluten and medium-gluten wheat Journal of Triticeae Crops 26 117-121
[8]  
Dai XL(2010)Applied machine vision of plants: a review with implications for field deployment in automated farming operations Intelligent Service Robotics 3 209-217
[9]  
Men HW(2008)Verification of color vegetation indices for automated crop imaging applications Computers and Electronics Agriculture 63 282-293
[10]  
Wang CY(1975)A threshold selection method from gray-level histograms Automatica 11 23-27