The week's pick
Random Articles
Reseach Article
Almond Dispersion Detector for a New Almond Picker Apparatus using Coupled Image Segmentation and Genetic Algorithm
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 124 - Number 9 |
| Year of Publication: 2015 |
| Authors: Farzad Rahmani, Mahmood Mahmoodi-Eshkaftaki |
10.5120/ijca2015905426
|
Farzad Rahmani, Mahmood Mahmoodi-Eshkaftaki . Almond Dispersion Detector for a New Almond Picker Apparatus using Coupled Image Segmentation and Genetic Algorithm. International Journal of Computer Applications. 124, 9 ( August 2015), 24-30. DOI=10.5120/ijca2015905426
Abstract
The objective of this study was to develop a machine vision system for detecting almond drop from trees on the ground during the harvesting stage. To attach this goal, in the machine vision system, the segmentation technique was coupled by genetic algorithm technique. The proposed method consists of three steps; Preprocessing included conversion images to gray level, noise reduction and edge dissimilarity enhancement; Image segmentation included K-means clustering algorithm, connected components labeling and remove small region; Region merge procedure using GA. The developed method was compared with usual image segmentation, thresholding and color segmentation. The results of fruit detection in the images showed that the developed method could detect 93% of all fruits in the images. While fruit detections using segmentation-thresholding and color segmentation in the images were 80% and 78%, respectively. The results confirmed that our method is found to be suitable, and effective for detecting almonds.
References
- Mahmoodi-Eshkaftaki, M., Ebrahimi, R., and Torki-Harchegani, M. 2013. Determination of critical conditions for puncturing almonds using coupled response surface methodology and genetic algorithm. Food Technol. Biotechnol. 51(4), 500–508.
- Torki-Harchegani, M., Ebrahimi, R., and Mahmoodi-Eshkaftaki, M. 2015. Almond production in Iran: An analysis of energy use efficiency (2008–2011). Renewable and Sustainable Energy Reviews, 41, 217–224.
- Ercisli, S., Sayinci, B., Kara, M., Yildiz, C., and Ozturk, I. 2012. Determination of size and shape features of walnut (Juglans Regia L.) cultivars using image processing. Sci. Hortic. 133(6), 47–55.
- Ghezelbash, J., Borghaee, A. M., Minaei, S., Fazli, S., and Moradi, M. 2013. Design and implementation of a low cost computer vision system for sorting of closed-shell pistachio nuts. Afr. J. Agric. Res. 49(8), 6479‒6484.
- Bulanon, D. M., and Kataoka, T. 2010. A fruit detection system and an end effector for robotic harvesting of Fuji apples. E-j.- CIGR. 12, 1‒14.
- Zhang, Y., and Wu, L. 2012. Classification of fruits using computer vision and multiclass support vector machine. Sensor, 12, 12489‒12505.
- Patel, H. N., Jain, R. K., and Joshi, M. V. 2012. Automatic segmentation and yield measurement of fruit using shape analysis. Int. J. Comput. Appl. T. 45(7).
- Wang, Y., Kan, J., Li, W., and Zhan, C. 2013. Image segmentation and maturity recognition algorithm based on color features of Lingwu long jujube. Adv. J. Food Sci. Tech. 5(12), 1625‒1631.
- `Yamamoto, K., Guo, W., Yoshioka, Y., and Ninomiya, S. 2014. On plant detection of intact tomato fruits using image analysis and machine learning methods. Sensors, 14, 12191‒12206.
- Dorj, U. O., Lee, M., and Han, S. 2013. A comparative study on tangerine detection, counting and yield estimation algorithm. IJSIA. 7(3), 405‒412.
- Choin, D., Lee, W. S., and Ehsani, R. 2013. Detecting and counting citrus fruit on the ground using machine vision. An ASABE Meeting Presentation, Paper Number 1591603.
- Bulanon, D., Kataoka, T., Ota, Y., and Hiroma, T. 2002a. A color model for recognition of apples by a robotic harvesting system. JSAM. 64(5), 123‒133.
- Ling, P., Ehsani, R., Ting, K. C., Chi, Y., Ramalingam, N., Klingman, M., and Draper, C. 2004. Sensing and end-effector for a robotic tomato harvester. An ASABE Meeting Presentation, Paper Number 043088.
- Bulanon, D., Kataoka, T., Ota, Y., and Hiroma, T. 2002b. A segmentation algorithm for the automatic recognition of Fuji apples during harvest. Biosystems Engineering, 83(4), 405‒412.
- Lak, M. B., Minaee, S., Amiriparian, J., and Behesht, B. 2011. Machine vision recognition algorithm development as the first stage of apple robotic harvesting. Symposium of Actual Tasks on Agricultural Engineering, Opatija, Croatia, 361‒366.
- Davis, L. 1991 Handbook of Genetic Algorithm. L. Davis (Ed.) New York, Van Nostrand Reinhold.
- Ying Ho, S., and Zheng Lee, K. 2003. Design and analysis of an efficient evolutionary image segmentation algorithm. J. Vlsi. Signal Proc. 35, 29‒42.
- Chun, N., and Yang, H. S. 1996. Robust image segmentation using genetic algorithm with a fuzzy measure. Pattern Recogn. 29(7), 1195‒1211.
- Jaiswal, L., Kurda, L., and Singh, V. 2013. Reboost image segmentation using genetic algorithm. Int. J. Comput. Appl. 69(19), 1‒7.
- Singh, V., and Garg, P. 2014. Adaptive image segmentation using a genetic algorithm. IJSWS, 82‒87.
- Gholami-Doborjeh, M. 2012 Genetic Optimization for Image Segmentation. Institute of Graduate Studies and Research, for the Degree of Master of Science in Computer Engineering, Eastern Mediterranean University, Gazimağusa, North Cyprus.
- Zhang, Y. J. 1996. A survey on evolutionary methods for image segmentation. Pattern Recogn. 29(8), 1335‒1345.
- Hole, D. K. R., Gulhane, V. S., and Shellokar, N. D. 2013. Application of genetic algorithm for image enhancement and segmentation. IJARCET, 2(4), 1342‒1346.
- Capraraa, A., Kellererb, H., Pferschyb, U., and Pisingerc, D. 2000. Approximation algorithms for knapsack problems with cardinality constraints. Eur. J. Oper. Res., 123(8), 333‒345.
Index Terms
Keywords