Automated image-processing for counting seedlings

Precision Agric DOI 10.1007/s11119-015-9425-6

Automated image-processing for counting seedlings in a wheat field Tao Liu1 • Wei Wu1 • Wen Chen1 • Chengming Sun1 Xinkai Zhu1 • Wenshan Guo1

•

Ó Springer Science+Business Media New York 2015

Abstract 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 9 104 and 240 9 104 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.

& Chengming Sun [email protected] & Wenshan Guo [email protected] 1

Jiangsu Key Laboratory of Crop Genetics and Physiology/Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou 225009, China

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Keywords Wheat  Seedling counting  Image processing  Morphological characters  Digital camera

Introduction The yield and quality of wheat are influenced not only by genetic factors, but also environmental factors and cultivation methods. Among the various methods of cultivation, plant seedling density and nitrogen status have the most influence (Cao et al. 2011; Xue et al. 2014). Research by Spink et al. (2000) indicated that wheat seedling density significantly affects grain yield. Research by Liu et al. (2006) showed that seedling density also affects grain quality. Because seedling density affects wheat growth, selection of appropriate late-stage cultivation methods is mainly based on seedling density (Spaner et al. 2000). Therefore, accurate and timely determination of seedling density is essential for wheat crop management. Currently, manual counting wheat plant density is employed, which is labor-intensive and inaccurate. Digital image processing has been widely applied in agriculture (Sakamoto et al. 2012; Lee and Lee 2013). McCarthy et al. (2010) reviewed research for both outdoor and indoor applications of machine vision of plants, which indicated that lighting conditions were the limiting factor for automatic plant counting in the field environment. Praat et al. (2004) estimated vine biomass by counting green pixels in an image. Jia and Krutz (1992) found the intersecting point by detecting main veins along leaves to estimate the center of a corn plant. Shrestha and Steward (2003) used a truncated ellipsoidal decision surface to segment vegetation, and counted the plants based on two image features and a straightforward iterative rule base; image processing technology has been adopted in various fields to liberate people from tedious, lengthy and repetitive tasks. A thorough review of the literature revealed that the most challenging part of automatically counting objects, using digital image processing, is to extract the objects from the background and to isolate objects that overlap or touch. Segmentation of touching objects is also a hot research topic; many segmentation algorithms, including watershed, chain code, concave points matching and contour curvature analysis have been proposed for such a purpose. The watershed algorithm is an image segmentation algorithm for grey-scale, which simulates a flooding process (Bieniek and Moga 2000). Freeman chain code can be regarded as a sequence of numbers which control the movement of a virtual walker throughout all the boundary pixels of a region (Jusoh and Zain 2009). The concave points matching algorithm could be used to find concave points of the contour and divide the contour into different segments via the concave points (Bai et al. 2009). In the contour curvature segmentation algorithm, curvature analysis is used to detect characteristic touching points on the boundaries, and an appropriate splitting line is calculated by the computed extremum of curvature (Lin et al. 2014). All previous works show good performance for specific objects; however, these algorithms cannot be easily applied to other objects. Compared with other grains or fruits, the morphology characteristics of wheat seedlings are more irregular and vary depending on the cultivar. In addition, common features are difficult to obtain when splitting touching objects in images. As plant seedling density increases, the touching problem becomes more severe. Automated counting of wheat seedlings from imagery is difficult in several respects. First, the morphological characteristics of wheat seedlings in an image are more irregular and vary among different cultivars as compared with other grains and fruits. Therefore, common morphological features are difficult to extract. Second, wheat seedlings in an image have

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various stances that result in increased difficulty for distinguishing between objects that are touching. Finally, as the seedling density increases, the touching conditions become severe. The aim of the present study was to develop an image-processing-based technique for automatic wheat seedling counting in a natural field environment and to develop different counting algorithms for different overlapping regions (an overlapping region contains a number of overlapping wheat seedlings in the image). The results of this study will provide a technical and theoretical basis for automatic counting of wheat seedlings.

Materials and methods Field experiments were conducted at a research farm of Yangzhou University, Jiangsu Province, China (32°300 N, 119°250 E, 21 m altitude) during the wheat growing season (October–May) in 2015. The soil was a sandy loam (Typic fluvaquents, Etisols, US classification). Five different wheat cultivars including YangNuo 1 (YN1), XuMai 33 (XM33), YangFuMai 4 (YFM4), YangMai 158 (YM158), and YangMai 23 (YM23) were investigated. All cultivars were planted on 02 November 2014 and included five seeding densities: 135 9 104, 180 9 104, 225 9 104, 270 9 104 and 315 9 104 viable seeds/ha. Under normal conditions, a reasonable seedling density is between 135 9 104 and 240 9 104 ha-1 (Zi et al. 2014). After the wheat started to emerge, digital images were acquired every 2–3 days using a Sony NEX-5R digital camera (lens: Sony E 20 mm f2.8) until the second leaf emerged. The lens are composed of six plastic aspheric lens, the F-number is 2. 8 and FOV is 70. The sensor Exmor APS HD made by Sony with 16 mega pixels, maximum resolution of 4912 pixel 9 3264 pixel, pixel size of 4.7 lm, Nyquist sampling frequency of 209 lp/mm. Photos of the crops were taken under low light conditions or cloudy days and at a height of about 500 mm from the canopy. The speed of travel was estimated to be 0.5 m/s. A laptop was used to connect to the digital camera by WiFi, control the camera by the PlayMemories application (Sony Corp., Japan) and show a low-resolution version of the image in the camera lens. The laptop was used to control the camera to take pictures, and then seedling counting software was used to count the seedlings. After digital images were acquired, plants were manually counted. The images of the seedlings were taken from an aerial view at a distance of 500 mm around dawn or during a cloudy period when light intensity was relatively low. Images were captured in RGB color mode and processed using MATLAB 2014a software (The MathWorks, Inc., Natick, MA, USA). The original RGB image was normalized based on Eq. (1) before counting. 8 R > r¼ > > > R+G+B < G ð1Þ g¼ > R + G+B > > > B :b ¼ R+G+B

Image processing Images of five different wheat cultivars collected at the one-leaf stage were analyzed. At this stage, there were few weeds in the field and the images mainly contained wheat seedlings, soil and straw. Fields with lower seedling density had sparse wheat growth

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which made it relatively easy to count seedlings (Fig. 1). However, in the field with higher seedling density, wheat plants showed various stances and overlapped, which made counting relatively difficult.

Wheat field vegetation index analysis The seedlings must be accurately extracted from background noise to estimate the number of seedlings in the wheat field. Previous research used the Excess Green value (ExG, Eq. 2), green and red normalized difference index (NDIgr, Eq. 3), and green and blue normalized difference index (NDIgb, Eq. 4) for vegetation analysis (Meyer and Neto 2008). Figure 2 shows the six indices (red, green, blue, NDIgr, NDIgb, and ExG); among them, ExG of wheat seedlings was significantly more discriminating between wheat seedlings and other regions. Therefore, ExG was used to extract wheat seedlings information from the field images. 8 < 2g  r  b; 2g [ r þ b ð2Þ ExG ¼ 255; 2g  r  b [ 1 : 0; 2g  r  b\0 NDIgr ¼

gr gþr

ð3Þ

NDIgb ¼

gb gþb

ð4Þ

Extraction of wheat seedlings information Otsu’s method uses a threshold to transform an original image to a foreground and background (Otsu 1975). In this study, ExG and Otsu’s method were combined to extract wheat seedling information. Greyscale images that were obtained based on Eq. 2, were analyzed. The maximum between-class variance was used to convert the greyscale image into a purely binary image. Combination of ExG and Otsu’s method led to noise and holes in extracted images. These two issues were solved by applying mathematical morphologybased preprocessing (Fejes and Vajda 1994; Gonzalez et al. 2004) and hole-filling algorithms (Zhang et al. 2011). The procedures used to extract wheat seedling information are illustrated in Fig. 3.

Fig. 1 Raw images of the wheat field with different seedling densities

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Fig. 2 Vegetation index of the region indicated by the red line. a Vegetative index value selection across a linear profile shown in red line; b linear histogram index values (Red, Green, Blue) for plant and bare soil background; c Linear histogram index values (NDIgr, NDIgb) for plant and bare soil background; d linear histogram index values (ExG) for plant and bare soil background (Color figure online)

Wheat seedling counting Analysis of wheat seedlings in overlapping regions One key for counting of wheat seedlings is to be able to estimate the number of seedlings in an overlapping region. Wheat seedlings have various stances in the image and may have a variety of complicated patterns when touching. Therefore, traditional touching region segmentation algorithms are ineffective in an overlapping region formed by closely touching wheat seedlings. In this study, the images from fields with different seedling densities were analyzed and it was found, that under normal seedling conditions, the number of wheat seedlings in an overlapping region is typically \6. The number of seedlings in an overlapping region is difficult to determine from the region’s intrinsic visual characteristics. Several visual indices, including area eigenvalues, eccentricity eigenvalues, solidity value, seedlings corner points number and skeleton corner points number were evaluated for their potential to be used for counting seedlings within the overlapping region using a least-significant difference (LSD) method. Area eigenvalues is the actual number of pixels in the overlapping region; The ‘Eccentricity’ value is the eccentricity of the ellipse that has the same second-moments as the region. Eccentricity was calculated by Eq. 5.

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Fig. 3 An illustration of procedures for wheat seedling segmentation

rffiffiffiffiffiffiffiffiffiffiffiffiffi b2 e¼ 1 2 a

ð5Þ

where a is the major axes of ellipse, b is minor axes of ellipse. The ‘Solidity’ value is a scalar specifying the proportion of the pixels in the convex hull that are also in the region, which is computed as area/convex area. Seedlings corner points number is the number of corners detected in the overlapping region using the Harris corner detector (Harris 1988); skeleton corner points number is the number of corners detected in the skeleton of an overlapping region using the Harris corner detector. Seedlings corner points number is the number of corners detected in the overlapping region using the Harris corner detector; Skeleton corner points number is the number of corners detected in the skeleton of overlapping region using the Harris corner detector (Gonzalez et al. 2004). The least-significant difference (LSD) method uses plain t test, the critical value is ta/2,v, where v is the error degrees of freedom. However, a common feature of the skeleton transformed image of this region was the number of corner points. The representative images of a binarized overlapping region and a corresponding skeleton image are shown in Fig. 4.

Skeleton analysis of the touching wheat seedlings The Harris corner detector was used to identify corner points in skeleton images, with the types of corner points being defined in Fig. 5. If there was only one pixel in all eight

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Fig. 4 Binary image and skeleton image of the overlapping region Fig. 5 Illustration of corner point identification

directions of the vector (as in Fig. 5a, b), this corner point was defined as a skeleton endpoint. If there were three or four pixels in eight directions of the vector, this corner point was defined as a skeleton corner. The results of using corner point detection methods are shown in Fig. 6. The single wheat seedling had two endpoints and zero corners. The number of endpoints and corners increased as the number of wheat seedlings increased in the overlapping region. However, because the plants are touching each other in various ways, the increased numbers of endpoints and corners cannot fully represent the number of wheat seedlings in an overlapping region.

Optimization of the seedlings skeleton in the overlapping areas The Freeman chain code (Jusoh and Zain 2009) was utilized (Fig. 7) to further analyze the internal visual characteristics of the touching wheat seedlings and to simplify each seedling in the overlapping region into a line segment. The critical step was to match the endpoints in the overlapping region using the chain code.

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Fig. 6 Detection of corners in a wheat skeleton image. (red represents a corner, green represents an endpoint) (Color figure online)

Figure 8 shows the chain code for each pixel in two wheat seedling skeleton images with different overlapping patterns. Pixels A, B, C and D were endpoints of the skeleton; while pixels M, N and P were corners of the skeleton. The corners divided the skeleton into several line segments. For example, in Fig. 8a, corners M and N divided the skeleton into line segments AM, BN, DN, CM and MN. The mean value of the chain code for every pixel in the line segment was defined as the direction value of the line segment. If there was another line segment along the ray of one segment, and the difference direction value of these two line segments were in the range of 0–1 or 4–5, the two line segments were combined into one line segment and the searching continued. For example, the direction

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Fig. 7 Freeman chain code

Fig. 8 Freeman chain code of seedling skeleton

value of BN in Fig. 8a was 6.429, and that of MC was 6.333, the difference value between BN and MC was 0.096; therefore, line segments BN and MC were combined into line segment BC. Similarly, line segments AM and MN were combined into AN, and AN and ND combined into AD. If a line segment could not be combined with other line segments, it was kept as an individual line segment. Based on the method described above, each wheat seedling within the overlapping region of the image was simplified as a straight line (Fig. 9). These lines may intersect to form corners or a closed polygon. As a result, a new wheat plant skeleton can be constructed. The number of corner points and closed polygons in the newly formed skeleton images were then analyzed using corner point detectors and closing operations (Soille 2003). The results showed that after simplifying the skeleton of touching wheat seedlings into straight

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Fig. 9 Illustration of skeleton images of touching wheat seedlings after connecting the endpoints (red straight line indicates a simplified skeleton) (Color figure online)

Table 1 Analysis of visual characteristics of wheat seedlings that are touching Number of plants

Area (pixel numbers)

Eccentricity

Solidity

Seedling corner number

Skeleton corner number

1

944.62a

0.9642a

0.7418a

7.18a

0.30a

2

1772.80b

0.9452a

0.4065b

9.63ab

1.87b

3

1892.20b

0.8461bc

0.3667bc

11.57bc

3.85c

4

2549.43c

0.8518bc

0.3296c

13.83c

5.92d

5

2780.63c

0.8447c

0.3365c

19.32d

7.72e

6

3336.50c

0.8767d

0.3265c

21.80d

9.58f

Means within the same column followed by different letters were significantly different according to the LSD test (P \ 0.05)

lines, the relationship between the number of wheat seedling within the overlapping region, the number of corners, and the number of closed polygons could be described using Eq. 6. This equation can be used to accurately estimate the number of wheat seedlings in the region. NS ¼ NCP  NCR þ 1

ð6Þ

NS is the number of seedlings; NCP is the number of corner points; NCR is the number of closed regions. In this study, false positive rate (FPR) was defined as false positives/(true negatives ? false positives), false negative rate (FNR) as false negative/(true positives ? false negatives). The accuracy was defined as (1-FPR-FNR) 9 100 %.

Results and analysis Analysis of the visual characteristics As shown in Table 1, all these commonly used visual indices cannot efficiently count the wheat seedling number within the overlapping region. However, the numbers of corner points in skeleton images varied significantly between overlapping regions. Therefore, the number of corner points in the skeleton image is a useful index for counting wheat plants in overlapping regions.

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Fig. 10 Histogram of the number of corner points in different overlapping regions before using the skeleton optimization method

Further analysis of the number of corners in a skeleton image of various overlapping regions showed that the index also had some limitations for counting wheat seedlings. The histogram in Fig. 10 indicates that some overlapping regions, though different, had the same number of corner points. The Gaussian curves in Fig. 10 show the distribution of corner points’ number of different overlapping regions before the skeleton optimization method was used. This inconsistency deteriorated as the number of wheat seedlings in the region increased. This phenomenon was found by analyzing visual characteristics of skeleton images of these regions, which suggests that it was caused by the irregular shapes of the wheat seedlings and formation of closed areas in overlapping regions. The skeleton optimization method proposed in the wheat seedling counting section can effectively solve the problem of an inconsistent number of corner points within the skeleton images. As shown in Fig. 11, the consistency of corner number was improved after the skeleton image was optimized. The Gaussian curves in Fig. 12 show the distribution of corner points’ number of different overlapping regions after using the skeleton optimization method.

Counting of wheat seedlings Fifty images representing five wheat cultivars with five seedling densities were counted using the newly-developed method. The results showed that this method can effectively count the seedlings in a wheat field with an average accuracy of 89.94 %. Among the five seedling densities tested, the counting accuracy was highest (97.14 %) for the wheat field with 135 9 104 ha-1 seedling density. Among all five cultivars tested, the counting accuracy was highest (92.54 %) for YN1. The seedling density significantly affected the counting accuracy (Table 2), while the different cultivars had little effect.

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Precision Agric Fig. 11 Analysis of corner points and closed regions in linearized wheat seedling skeleton images (red represents a corner and green represents the center of a closed polygon) (Color figure online)

For all five tested cultivars, the counting accuracy decreased as the seedling density increased (Fig. 13). Automatic counting of seedlings was found to be less accurate at higher seedling densities.

Discussion In this study, each wheat seedling was transformed into a line segment based on their skeleton characteristics. Based on the resultant skeleton images and number of corner points, an automatic wheat seedling counting equation was developed (Eq. 6). This equation can accurately count the seedlings in the field with some limitations. First, the

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Fig. 12 Histogram of the number of corner points in different overlapping regions after using the skeleton optimization method

Table 2 Evaluation of accuracy rate of automatic counting method in wheat fields with different seedling densities and cultivars Density

Cultivars 4

-2

(1 9 10 ha )

YN1 (%)

YM158 (%)

YFM4 (%)

YM23 (%)

XM33 (%)

135

99.21a

97.76a

97.89a

95.6a

95.26a

180

97.26b

97.07a

96.27a

94.11a

94.65a

225

95.57c

90.44b

93.9a

92.3a

90.55b

270

89.16d

79.03c

88.59b

87.56b

85.03c

315

81.52e

78.37d

79.07d

76.97d

75.44d

F values Density

142.78**

Cultivars

1.26ns

Means within the same column followed by different letters were significantly different according to the LSD test (P \ 0.05) ns

P [ 0.05

** P \ 0.01

counting accuracy decreases as the seedling density increases; and the counting cannot proceed if the seedling density is too high. Second, the counting of wheat seedlings can only be performed in a short period which is between 7 days after seeding and before the second leaf emergence. Counting becomes very difficult for late-stage seedlings.

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Fig. 13 Relationship between counting accuracy of different cultivars with varying seedling densities

Conclusions The method presented in this study can count wheat seedlings at more than 92 % accuracy within this seedling density range. For wheat fields with seedling densities higher than 240 9 104 ha-1, the counting accuracy is still relatively high (85 %). The results demonstrate that the newly-developed method can be used for counting of wheat seedlings in the field at different seedling densities. On the other hand, this new methods showed similar high efficiency and accuracy for all five tested cultivars, which suggests that this method can be easily adapted to other cultivars. This study provides a theoretical perspective and technical support for automatic counting of wheat seedlings. The method reported here can also be adapted for rice and other gramineous plant counting. Acknowledgments This research was mainly supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions, the practice innovation training program of Jiangsu college students (201311117036Z), the graduate research and innovation projects in Jiangsu Province (CXLX_1419), the National Natural Science Foundation of China (31171480) and the National Science & Technology Pillar Program during the 12th Five-year Plan Period (2012BAD04B08).

References Bai, X., Sun, C., & Zhou, F. (2009). Splitting touching cells based on concave points and ellipse fitting. Pattern Recognition, 42, 2434–2446. Bieniek, A., & Moga, A. (2000). An efficient watershed algorithm based on connected components. Pattern Recognition, 33, 907–916. Cao, Q., He, M. R., Dai, X. L., Men, H. W., & Wang, C. Y. (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. Fejes, S., & Vajda, F. (1994). An efficient implementation technique of adaptive morphological operations. The Netherlands: Springer.

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Precision Agric Gonzalez, R. C., Wood, R. E., & Eddins, S. L. (2004). Digital image processing using Matlab. Upper Saddle River, NJ: Pearson Prentice Hall. Harris, C. G. (1988). A combined corner and edge detector. In C. J. Taylor (Ed.), Proceedings of the 4th Alvey Vision Conference in Manchester, UK: Alvey Vision Club Jia, J., & Krutz, G. W. (1992). Location of the maize plant with machine vision. Journal of Agricultural Engineering Research, 52, 169–181. Jusoh, N. A., & Zain, J. M. (2009). Application of Freeman chain codes: An alternative recognition technique for Malaysian car plates. International Journal of Computer Science and Network Security, 9(11), 222-227. http://is.ulsan.ac.kr/files/announcement/301/20091132.pdf. Accessed 25 Nov 2015. Lee, K., & Lee, B. (2013). Estimation of rice growth and nitrogen nutrition status using color digital camera image analysis. European Journal Agronomy, 48, 57–65. Lin, P., Chen, Y. M., He, Y., & Hu, G. W. (2014). A novel matching algorithm for splitting touching rice kernels based on contour curvature analysis. Computers and Electronics Agriculture, 109, 124–133. Liu, P., Guo, W. S., Xu, Y., Feng, C. N., Zhu, X. K., & Peng, Y. X. (2006). Effect of planting density on grain yield and quality of weak-gluten and medium-gluten wheat. Journal of Triticeae Crops, 26, 117–121. Mccarthy, C. L., Hancock, N. H., & Raine, S. R. (2010). Applied machine vision of plants: a review with implications for field deployment in automated farming operations. Intelligent Service Robotics, 3, 209–217. Meyer, G. E., & Neto, J. C. (2008). Verification of color vegetation indices for automated crop imaging applications. Computers and Electronics Agriculture, 63, 282–293. Otsu, N. (1975). A threshold selection method from gray-level histograms. Automatica, 11, 23–27. Praat, J., Bollen, F., & Irie, K. (2004). New approaches to the management of vineyard variability in New Zealand. In R. Blair, P. Williams, & S. Pretorius (Eds.), The 12th Australian Wine Industry Technical Conference, Managing Vineyard Variation (pp. 24–30). Urrbrae, Australia: Australian Wine Industry Technical Conference Inc. Sakamoto, T., Gitelson, A. A., Nguy-Robertson, A. L., Arkebauer, T. J., Wardlow, B. D., Suyker, A. E., et al. (2012). An alternative method using digital cameras for continuous monitoring of crop status. Agricultural and Forest Meteorology, 154, 113–126. Shrestha, D. S., & Steward, B. L. (2003). Automatic corn plant population measurement using machine vision. Transactions of the Asae, 46, 559–566. Soille, P. (2003). Morphological image analysis: principles and applications. New York: Springer-Verlag. Spaner, D., Todd, A. G., & McKenzie, D. B. (2000). The effect of seeding date, seeding rate and N fertilization on winter wheat yield and yield components in eastern Newfoundland. Canadian Journal of Plant Science, 80, 703–711. Spink, J. H., Semere, T., Sparkes, D. L., Whaley, J. M., Foulkes, M. J., Clare, R. W., et al. (2000). Effect of sowing date on the optimum plant density of winter wheat. Annals of Applied Biology, 137, 179–188. Xue, Y., Zhang, W., Liu, D., Yue, S., Cui, Z., Chen, X., et al. (2014). Effects of nitrogen management on root morphology and zinc translocation from root to shoot of winter wheat in the field. Field Crops Research, 161, 38–45. Zhang, D. C., Zhou, C. G., Zhou, Q., Chi, S. Z., & Wang, S. J. (2011). Hole-filling algorithm based on contour. Journal of Jilin University (Science Edition), 49, 82–86. Zi, Y., Ding, J. F., Che, Z., Zhou, D. D., Yuan, Y., Feng, C. N., et al. (2014). Effect of planting density on grain yield and population characteristics of waxy wheat variety Yangnuomai 1. Journal of Triticeae Crops, 34, 521–527.

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