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 × 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.
Similar content being viewed by others
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.
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.
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).
Author information
Authors and Affiliations
Corresponding authors
Rights and permissions
About this article
Cite this article
Liu, T., Wu, W., Chen, W. et al. Automated image-processing for counting seedlings in a wheat field. Precision Agric 17, 392–406 (2016). https://doi.org/10.1007/s11119-015-9425-6
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11119-015-9425-6