Segmentation of Bean-Plants Using Clustering Algorithms
DOI:
https://doi.org/10.7160/aol.2020.120304How to Cite
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.References
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: https://doi.org/10.1016/j.agrformet.2011.06.015
Cook, R. J. and Veseth, R. J. (1991) “Wheat health management”, St. Paul. MN. American Phytopathological Society.
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: https://doi.org/10.1093/comjnl/20.4.364
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: https://doi.org/10.17221/412/2015-PSE
Gebbers, R. and Adamchuk, V. I. (2010) “Precision agriculture and food security”, Science, Vol. 327, No. 5967, pp. 828-831. ISSN 1095-9203. DOI: https://doi.org/10.1126/science.1183899
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: https://doi.org/10.1016/j.eswa.2012.03.040
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: https://doi.org/10.2480/agrmet.D-18-00012
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: https://doi.org/10.1016/j.biosystemseng.2014.11.007
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: https://doi.org/10.1109/WF-IoT.2016.7845467
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: https://doi.org/10.1016/j.compag.2018.02.016
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: https://doi.org/10.1016/j.compag.2018.10.013
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: https://doi.org/10.1098/rstb.2007.2164
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: https://doi.org/10.17221/188/2013-PSE
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: https://doi.org/10.17221/515/2014-PSE
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: https://doi.org/10.14254/2071-8330.2019/12-1/7
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: https://doi.org/10.1016/j.compag.2015.11.018
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: https://doi.org/10.1016/j.compag.2012.11.009
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: https://doi.org/10.14254/2071-789X.2019/12-3/20
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: https://doi.org/10.1016/j.biosystemseng.2014.11.013
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: https://doi.org/10.1016/j.compag.2017.03.010
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: https://doi.org/10.1007/0-387-25465-X_15
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: https://doi.org/10.1007/s10492-014-0063-5
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: https://doi.org/10.1016/j.agrformet.2011.10.014
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: https://doi.org/10.1093/comjnl/16.1.30
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: https://doi.org/10.1016/j.biosystemseng.2018.06.017
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: https://doi.org/10.1016/j.biosystemseng.2015.01.008
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: https://doi.org/10.2307/2282967
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.
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: https://doi.org/10.1109/TIP.2011.2176743
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: https://doi.org/10.1016/j.compag.2018.10.029
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: https://doi.org/10.3390/rs11020211
