Determining the number of sugar beet seedling using image processing method

Document Type : Scientific - Research

Authors

1 Assistant Professor of Agricultural Engineering Research Department, Fars Agricultural and Natural Resources Research Center, Agricultural Research, Education and Extension Organization (AREOO), Shiraz, Iran.

2 Assistant Professor of Sugar beet Reseach Department, Fars Agricultural and Natural Resources Research Center, Agricultural Research, Education and Extension Organization (AREOO), Shiraz, Iran.

3 3- Assistant Professor of Biosystem Engineering Department, Faculty of Agriculture, Shiraz University, Shiraz, Iran.

Abstract

The germination percentage of different sugar beet cultivars and hybrids in the field is of special importance for the breeder as a superior feature of the improved cultivar. The aim of this study was to provide an image processing-based method for rapid and accurate counting of seedling number of various sugar beet cultivars in the field. The images were taken using a digital camera from a fixed height and transferred to the Matlab software environment in May 2017 at Zarghan Agricultural Research Station. Using image processing techniques and functions in the software, the algorithm for counting and determining the number of sugar beet seedling in a fixed length was coded and presented. The proposed algorithm was related to situations in which the leaves of sugar beet seedling were far from each other or only in contact with each other. The accuracy of the algorithm was 90.32% and its execution time was about 1.67 seconds. The actual number of seedlings and the number observed by the algorithm were statistically analyzed by paired t-student test and it was found no significant difference between them (P<0.05). Also, different sugar beet cultivars had no effect on the implementation of the algorithm and its accuracy. The results showed that instead of visually counting the number of seedlings, this procedure can be done with more than 90% accuracy for the above two conditions by imaging and using the algorithm.

Keywords

References

Alharbi N, Zhou J, Wang W. Automatic Counting of Wheat Spikes from Wheat Growth Images. Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods; 2018.
Bairwa N, Agrawal N, Gupta S. Development of counting algorithm for overlapped agricultural products. International Journal of Computer Application. 2014; 16–19.
Brunes AP, Araújo AS, Dias LW, Villela FA, Aumonde TZ. Seedling length in wheat determined by image processing using mathematical tools. Revista Ciência Agronômica. 2016; 47(2): 374-379. 
Ducournau S, Feutry A, Plainchault P, Revollon P, Vigouroux B, Wagner MH. An image acquisition system for au­tomated monitoring of the germination rate of sunflower seeds. Computers and Electronics in Agriculture. 2004; 44(3): 189–202.
Geneve RL, Kester ST. Evaluation of seedling size following germination using computer-aided analysis of digital images from flat-bed scanner. Horticultural Science. 2001; 36: 1117–1120.
Guerin D, Cointault F, Gee F, Guillemin J. Feasibility study of a wheatears counting vision system. Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods. 2004.
Harmsen SR, Koenderink NJJP. Multi-target Tracking for Flower Counting using Adaptive Motion Models. Computers and Electronics in Agriculture. 2009; 65: 7-18.
Heidari AR. Image processing in Matlab, Behavara and Kelk Zarin Publishers, 2012; pp. 228. (in Persian, abstract in English)
Liu T, Wu W, Chen W, Sun C. Automated image-processing for counting seedlings in a wheat field. Precision Agriculture. 2016; 17(4): 392-406.
Lurstwut B, Pornpanomchai C. Application of image processing and computer vision on rice seed germination analysis. International Journal of Applied Engineering Research. 2016; 11: 6800-6807.
Orek H, Abdanan Mehdizadeh S, Saadi M. Predicting sugar beet performance by online image processing. Journal of Sugar Beet. 2019; 34(2): 181-191.  
Sako Y, McDonald MB, Fujimura K, Evans AF, Bennett MA. A system for automated seed vigor assessment. Seed Science and Technology. 2001; 29(3): 625-636.
Shaker M, Minaei S, Khoshtaghaza MH, Banakar A, Jafari AA. Utilization of Machine Vision for Performance Improvement and Reduction of Losses in Paddy Husker. Agricultural Mechanization and systems Research. 2015; 16(65): 47-64. (in Persian, abstract in English)
Zhao MJ, Qin SL, Liu Z, Cao J, Yao X, Ye S, Li L. An automatic counting method of maize ear grain based on image processing. Springer International Publishing. 2015; 452: 521–533.
Journal of Sugar Beet
Volume 36, Issue 1
April 2020
Pages 71-79

Files

Share

How to cite

Statistics