Desigining of Computer Vision Algorithm to Detect Sweet Pepper for Robotic Harvesting Under Natural Light

ماشین های کشاورزی

Article 9, Volume 5, Issue 1 - Serial Number 9, 2015, Pages 82-91   

Document Type: Research Article

DOI: 10.22067/jam.v5i1.23528

Authors

A. Moghimi; M. H. Aghkhani; M. R. Golzarian^*

Ferdowsi University of Mashhad

Abstract

In recent years, automation in agricultural field has attracted more attention of researchers and greenhouse producers. The main reasons are to reduce the cost including labor cost and to reduce the hard working conditions in greenhouse. In present research, a vision system of harvesting robot was developed for recognition of green sweet pepper on plant under natural light. The major challenge of this study was noticeable color similarity between sweet pepper and plant leaves. To overcome this challenge, a new texture index based on edge density approximation (EDA) has been defined and utilized in combination with color indices such as Hue, Saturation and excessive green index (EGI). Fifty images were captured from fifty sweet pepper plants to evaluate the algorithm. The algorithm could recognize 92 out of 107 (i. e., the detection accuracy of 86%) sweet peppers located within the workspace of robot. The error of system in recognition of background, mostly leaves, as a green sweet pepper, decreased 92.98% by using the new defined texture index in comparison with color analysis. This showed the importance of integration of texture with color features when used for recognizing sweet peppers. The main reasons of errors, besides color similarity, were waxy and rough surface of sweet pepper that cause higher reflectance and non-uniform lighting on surface, respectively.

Keywords

Automatic harvesting; Computer vision; Image processing; Robot; Texture index

References

1. Canny, J. 1986. A computational approach to edge detection. The IEEE Transactions on Pattern Analysis and Machine Intelligence 8: 679-698.

2. De-An, Z., L. Jidong, J. Wei, Z. Ying, and C. Yu. 2011. Design and control of an apple harvesting robot. Biosystems Engineering 110: 112-122.

3. Golzarian, M. R., M. K. Lee, and J. M. A. Desbiolles. 2012. Evaluation of color indices for improved segmentation of plant images. Transactions of the ASABE 55: 261-273.

4. Hayashi, S., K. Shigematsu, S. Yamamoto, K. Kobayashi, Y. Kohno, J. Kamata, and M. Kurita. 2010. Evaluation of a strawberry-harvesting robot in a field test. Biosystems Engineering 105: 160-171.

5. Hemming, J., J. L. Ruizendaal, J. W. Hofstee, and E. J. van Henten. 2012. Fruit detectability analysis for different camera positions in sweet-pepper. Sensors 14 (4): 6032-6044.

6. Kassler, M. 2001. Agricultural automation in the new millennium. Computers and Electronics in Agriculture 30: 237-240.

7. Kitamura, S., K. Oka, K. Ikutomo, Y. Kimura, and Y. Taniguchi. 2008. A distinction method for fruit of sweet pepper using reflection of LED light. In SICE Annual Conference. The University Electro-Communications, Japan.

8. Lamm, R. D., D. C. Slaughter, and D. K. Giles. 2002. Precisionweed control system for cotton. Transactions of the ASAE 45 (1): 231-238.

9. Mohamadi Monavar, H., R. Alimardani, and M. Omid. 2013. Computer vision utilization for detection of green house tomato under natural illumination. Journal of Agricultural Machinery 3: 9-15. (In Farsi).

10. Ohta, Y., T. Kanade, and T. Sakai. 1980. Color information for region segmentation. Computer Graphics and Image Processing 13: 222-241.

11. Sarig, Y. 2005. Mechanized fruit harvesting-site specific solutions. Information and Technology for Sustainable Fruit and Vegetable Production, FRUTIC. Montpellier, France.

12. Tanigaki, K., T. Fujiura, A. Akase, and J. Imagawa. 2008. Cherry-harvesting robot. Computers and Electronics in Agriculture 63: 65-72.

13. Van Henten, E. J., B. A. J. Van Tuijl, J. Hemming, J. G. Kornet, J. Bontsema, and E. A. Van Os. 2003. Field test of an autonomous cucumber picking robot. Biosystems Engineering 86: 305-313.