Segmentation of Bean-Plants Using Clustering Algorithms

Serkan KartalSunita ChoudharyMichal StočesPavel ŠimekTomáš VokounVojtěch Novák

DOI 10.7160/aol.2020.120304
No 3/2020, September
pp. 36-43

Kartal, S., Choudhary, S., Stočes, M., Šimek, P., Vokoun, T. and Novák, V. (2020) “Segmentation of Bean-Plants Using Clustering Algorithms", AGRIS on-line Papers in Economics and Informatics, Vol. 12, No. 3, pp. 36-43. ISSN 1804-1930. DOI 10.7160/aol.2020.120304.

Abstract

In recent years laser scanning platforms have been proven to be a helpful tool for plants traits analysing in agricultural applications. Three-dimensional high throughput plant scanning platforms provide an opportunity to measure phenotypic traits which can be highly useful to plant breeders. But the measurement of phenotypic traits is still carried out with labor-intensive manual observations. Thanks to the computer vision techniques, these observations can be supported with effective and efficient plant phenotyping solutions. However, since the leaves and branches of some plant types overlap with other plants nearby after a certain period of time, it becomes challenging to obtain the phenotypical properties of a single plant. In this study, it is aimed to separate bean plants from each other by using common clustering algorithms and make them suitable for trait extractions. K-means, Hierarchical and Gaussian mixtures clustering algorithms were applied to segment overlapping beans. The experimental results show that K-means clustering is more robust and faster than the others.

Keywords

Clustering, segmentation, point cloud, sustainable agriculture.

References

  1. Bannayan, M. and Sanjani, S. (2011) “Weather conditions associated with irrigated crops in an arid and semi arid environment”, Agricultural and Forest Meteorology, Vol. 151, No. 12, pp. 1589-1598. ISSN 0168-1923. DOI 10.1016/j.agrformet.2011.06.015.
  2. Cook, R. J. and Veseth, R. J. (1991) “Wheat health management”, St. Paul. MN. American Phytopathological Society.
  3. Defays, D. (1977) “An efficient algorithm for a complete link method”, The Computer Journal, Vol. 20, No. 4, pp. 364-366. E-ISSN 1460-2067, ISSN 0010-4620. DOI 10.1093/comjnl/20.4.364.
  4. Domínguez, J. A., Kumhálová, J. and Novák, P. (2015) “Winter oilseed rape and winter wheat growth prediction using remote sensing methods”, Plant, Soil and Environment, Vol. 61, No. 9, pp. 410-416. E-ISSN 1805-9368, ISSN 1214-1178. DOI 10.17221/412/2015-PSE.
  5. Gebbers, R. and Adamchuk, V. I. (2010) “Precision agriculture and food security”, Science, Vol. 327, No. 5967, pp. 828-831. ISSN 1095-9203. DOI 10.1126/science.1183899.
  6. Guerrero, J. M., Pajares, G., Montalvo, M., Romeo, J., and Guijarro, M. (2012) “Support vector machines for crop/weeds identification in maize fields”, Expert Systems with Applications, Vol. 39, No. 12, pp. 11149-11155. ISSN 0957-4174. DOI 10.1016/j.eswa.2012.03.040.
  7. Itakura, K. and Hosoi, F. (2018) “Automatic individual tree detection and canopy segmentation from three-dimensional point cloud images obtained from ground-based lidar”, Journal of Agricultural Meteorology, Vol. 74, No. 3, pp. 109-113. ISSN 1881-0136, ISSN 0021-8588. DOI 10.2480/agrmet.D-18-00012.
  8. Jannoura, R., Brinkmann, K., Uteau, D., Bruns, C. and Joergensen, R. G. (2015) “Monitoring of crop biomass using true colour aerial photographs taken from a remote controlled hexacopter”, Biosystems Engineering, Vol. 129, pp. 341-351. ISSN 1537-5110. DOI 10.1016/j.biosystemseng.2014.11.007.
  9. Kamilaris, A., Gao, F., Prenafeta-Boldu, F. X. and Ali, M. I. (2016) “Agri-IoT: A semantic framework for Internet of Things-enabled smart farming applications”, In 2016 IEEE 3rd World Forum on Internet of Things (WF-IoT), pp. 442-447). DOI 10.1109/WF-IoT.2016.7845467.
  10. Kamilaris, A. and Prenafeta-Boldú, F. X. (2018) “Deep learning in agriculture: A survey”, Computers and Electronics in Agriculture, Vol. 147, pp. 70-90. ISSN 0168-1699. DOI 10.1016/j.compag.2018.02.016.
  11. Khan, M. A., Akram, T., Sharif, M., Awais, M., Javed, K., Ali, H. and Saba, T. (2018) “CCDF: Automatic system for segmentation and recognition of fruit crops diseases based on correlation coefficient and deep CNN features”, Computers and Electronics in Agriculture, Vol. 155, pp. 220-236. ISSN 0168-1699. DOI 10.1016/j.compag.2018.10.013.
  12. Kitzes, J., Wackernagel, M., Loh, J., Peller, A., Goldfinger, S., Cheng, D. and Tea, K. (2008) “Shrink and share: humanity's present and future Ecological Footprint”, Philosophical Transactions of the Royal Society B: Biological Sciences, Vol. 363, No. 1491, pp. 467-475. ISSN 09628436. DOI 10.1098/rstb.2007.2164.
  13. Kumhálová, J., Kumhála, F., Novák, P. and Matějková, Š. (2013) “Airborne laser scanning data as a source of field topographical characteristics”, Plant, Soil and Environment, Vol. 59 No. 9, pp. 423-431. E-ISSN 1805-9368, ISSN 1214-1178. DOI 10.17221/188/2013-PSE.
  14. Kumhálová, J., Zemek, F., Novák, P., Brovkina, O. and Mayerová, M. (2014). “Use of Landsat images for yield evaluation within a small plot”, Plant, Soil and Environment, Vol. 60, No. 11, pp. 501-506. E-ISSN 1805-9368, ISSN 1214-1178. DOI 10.17221/515/2014-PSE.
  15. Muhammad, K., Mastuki, N., Darus, F. and Ghani, E. K. (2019) "Accounting information system change in an agriculture company: Examination using burns and scapens framework",.Journal of International Studies, Vol. 12, No. 1, pp. 105-118. E-ISSN 2306-3483, ISSN 2071-8330. DOI 10.14254/2071-8330.2019/12-1/7.
  16. Pantazi, X. E., Moshou, D., Alexandridis, T., Whetton, R. L. and Mouazen, A. M. (2016) “Wheat yield prediction using machine learning and advanced sensing techniques”, Computers and Electronics in Agriculture, Vol. 121, pp. 57-65. ISSN 0168-1699. DOI 10.1016/j.compag.2015.11.018.
  17. Payne, A. B., Walsh, K. B., Subedi, P. P. and Jarvis, D. (2013) “Estimation of mango crop yield using image analysis–segmentation method”, Computers and Electronics in Agriculture, Vol. 91, pp. 57-64. ISSN 0168-1699. DOI 10.1016/j.compag.2012.11.009.
  18. Raišienė, A. G., Podviezko, A., Skulskis, V. and Baranauskaitė, L. (2019) "Interest-balanced agricultural policy-making: Key participative and collaborative capacities in the opinion of NGOs’ experts", Economics and Sociology, Vol. 12, No. 3, pp. 301-318. E-ISSN 2306-3459, ISSN 2071-789X. DOI 10.14254/2071-789X.2019/12-3/20.
  19. Pourreza, A., Lee, W. S., Etxeberria, E. and Banerjee, A. (2015) “An evaluation of a vision-based sensor performance in Huanglongbing disease identification”, Biosystems Engineering, Vol. 130, pp. 13-22. ISSN 1537-5110. DOI 10.1016/j.biosystemseng.2014.11.013.
  20. Ramos, P. J., Prieto, F. A., Montoya, E. C. and Oliveros, C. E. (2017) “Automatic fruit count on coffee branches using computer vision”, Computers and Electronics in Agriculture, Vol. 137, pp. 9-22. ISSN 0168-1699. DOI 10.1016/j.compag.2017.03.010.
  21. Rokach, L. and Maimon, O. (2005) “Clustering methods”, In Data mining and knowledge discovery handbook, pp. 321-352. Springer, Boston, MA. ISBN 978-0-387-24435-8. DOI 10.1007/0-387-25465-X_15.
  22. Sabo, K., and Scitovski, R. (2014) “Interpretation and optimization of the k-means algorithm”, Applications of mathematics, Vol. 59, No. 4, pp. 391-406. E-ISSN 1572-9109, ISSN 0862-7940. DOI 10.1007/s10492-014-0063-5.
  23. Sakamoto, T., Gitelson, A. A., Nguy-Robertson, A. L., Arkebauer, T. J., Wardlow, B. D., Suyker, A. E., ... and Shibayama, M. (2012) “An alternative method using digital cameras for continuous monitoring of crop status”, Agricultural and Forest Meteorology, Vol. 154, pp. 113-126. ISSN 0168-1923. DOI 10.1016/j.agrformet.2011.10.014.
  24. Sibson, R. (1973) “SLINK: an optimally efficient algorithm for the single-link cluster method”, The Computer Journal, Vol. 16, No. 1, pp. 30-34. ISSN 1460-2067, ISSN 0010-4620. DOI 10.1093/comjnl/16.1.30.
  25. Suh, H. K., Ijsselmuiden, J., Hofstee, J. W. and Van Henten, E. J. (2018) “Transfer learning for the classification of sugar beet and volunteer potato under field conditions”, Biosystems engineering, Vol. 174, pp. 50-65. ISSN 1537-5110. DOI 10.1016/j.biosystemseng.2018.06.017.
  26. Vega, F. A., Ramirez, F. C., Saiz, M. P., and Rosúa, F. O. (2015) “Multi-temporal imaging using an unmanned aerial vehicle for monitoring a sunflower crop”, Biosystems Engineering, Vol. 132, pp. 19-27. ISSN 1537-5110. DOI 10.1016/j.biosystemseng.2015.01.008.
  27. Ward Jr, J. H. (1963) “Hierarchical grouping to optimize an objective function”, Journal of the American Statistical Association, Vol. 58, No. 301, pp. 236-244. E-ISSN 1537-274X, ISSN 0162-1459. DOI 10.2307/2282967.
  28. Yuehua, C., Xiaoguang, H. and Changli, Z. (2007) “Algorithm for segmentation of insect pest images from wheat leaves based on machine vision”, Transactions of the Chinese Society of Agricultural Engineering, Vol. 23, No. 12.pp. 187-191. ISSN 10026819.
  29. Yu, G., Sapiro, G. and Mallat, S. (2011) “Solving inverse problems with piecewise linear estimators: From Gaussian mixture models to structured sparsity”, IEEE Transactions on Image Processing, Vol. 21, No. 5, pp. 2481-2499. ISSN 10577149. DOI 10.1109/TIP.2011.2176743.
  30. Zhang, J., He, L., Karkee, M., Zhang, Q., Zhang, X. and Gao, Z. (2018) “Branch detection for apple trees trained in fruiting wall architecture using depth features and Regions-Convolutional Neural Network (R-CNN)”, Computers and Electronics in Agriculture, Vol. 155, pp. 386-393. ISSN 0168-1699. DOI 10.1016/j.compag.2018.10.029.
  31. Zhang, W., Wan, P., Wang, T., Cai, S., Chen, Y., Jin, X. and Yan, G. (2019) “A novel approach for the detection of standing tree stems from plot-level terrestrial laser scanning data”, Remote sensing, Vol. 11, No. 2. ISSN 2072-4292. DOI 10.3390/rs11020211.

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