Real-time grain breakage sensing for rice combine harvesters using machine vision technology
Keywords:
combine harvester, breakage rate monitoring, sampling box structure, machine vision, color classificationAbstract
Breakage rate is one of the most important indicators to evaluate the harvesting performance of a combine harvester. It is affected by operating parameters of a combine such as feeding rate, the peripheral speed of the threshing cylinder and concave clearance, and shows complex non-linear law. Real-time acquisition of the breakage rate is an effective way to find the correlation of them. In addition, real-time monitoring of the breakage rate can help the driver optimize and adjust the operating parameters of a combine harvester to avoid the breakage rate exceeding the standard. In this study, a real-time monitoring method for the grain breakage rate of the rice combine harvester based on machine vision was proposed. The structure of the sampling device was designed to obtain rice kernel images of high quality in the harvesting process. According to the working characteristics of the combine, the illumination and installation of the light source were optimized, and the lateral lighting system was constructed. A two-step method of “color training-verification” was applied to identify the whole and broken kernels. In the first step, the local threshold algorithm was used to get the edge of kernel particles in a few training images with binary transformation, extract the color spectrum of each particle in color-space HSL and output the recognition model file. The second step was to verify the recognition accuracy and the breakage rate monitoring accuracy through grabbing and processing images in the laboratory. The experiments of about 2300 particles showed that the recognition accuracy of 96% was attained, and the monitoring values of breakage rate and the true artificial monitoring values had good trend consistency. The monitoring device of grain breakage rate based on machine vision can provide technical supports for the intellectualization of combine harvester. Keywords: combine harvester, breakage rate monitoring, sampling box structure, machine vision, color classification DOI: 10.25165/j.ijabe.20201303.5478 Citation: Chen J, Lian Y, Zou R, Zhang S, Ning X B, Han M N. Real-time grain breakage sensing for rice combine harvesters using machine vision technology. Int J Agric & Biol Eng, 2020; 13(3): 194–199.References
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[4] Lashgari M, Mobli H, Omid M, Alimardani R, Mohtasebi S S. Qualitative analysis of wheat grain damage during harvesting with John Deere combine harvester. International Journal of Agriculture and Biology (Pakistan), 2008; 10(2): 201–204.
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[6] Jahari M, Yamamoto K, Miyamoto M, Kondo N, Ogawa Y, Suzuki T, et al. Double lighting machine vision system to monitor harvested paddy grain quality during head-feeding combine harvester operation. Machines, 2015; 3(4): 352–363.
[7] Maertens K, Reyns P, De Baerdemaeker J. On-line measurement of grain quality with NIR technology. Transactions of the ASAE, 2004; 47(4): 1135–1140.
[8] Georg H, Guth N, Bockisch F J. Machine vision for the automatic measurement of broken grain fraction 1. IFAC Control Applications in Post-Harvest and Proceeding Technology, 1995; 28(6): 139–142.
[9] Jahari M, Yamamoto K, Miyamoto M, Kondo N, Ogawa Y, Suzuki T, et al. Monitoring harvested paddy during combine harvesting using a machine vision-Double lighting system. Engineering in Agriculture Environment & Food, 2016; 10(2): 140–149.
[10] Lashgari M, Mobli H, Omid M, Alimardani R, Mohtasebi S S. Qualitative analysis of wheat grain damage during harvesting with john deere combine harvester. International Journal of Agriculture & Biology, 2008; 10(2): 201–204.
[11] Chen J, Lian Y, Li Y M, Wang Y H, Liu X Y, Gu Y. Design of sampling device for rice grain impurity sensor in grain-bin of combine harvester. Transactions of the CSAE, 2019; 35(5): 18–25. (in Chinese)
[12] Li N Y, Ye J W, Ji Y, Ling H B, Yu J Y. Saliency detection on light field. IEEE Conference on Computer Vision and Pattern Recognition,
Columbus: IEEE, 2014; pp. 2806–2813.
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[14] Liao K W, Lee Y T. Detection of rust defects on steel bridge coatings via digital image recognition. Automation in Construction, 2016; 71, 294–306.
[15] Sun Z, Feng W, Zhao Q, Huang L. Brightness preserving image enhancement based on a gradient and intensity histogram. Journal of Electronic Imaging, 2015; 24(5): 053006.1–053006.11.
[16] Domsch H, Heisig M, Witzke K. Estimation of yield zones using aerial images and yield data from a few tracks of a combine harvester. Precision Agriculture, 2008; 9(5): 321–337.
[17] Sofu M M, Er O, Kayacan M C, Cetisli B. Design of an automatic apple sorting system using machine vision. Computers & Electronics in Agriculture, 2016; 127: 395–405.
[18] Adriaan V D M, Auger F, Frederix S, Morel M H. Image analysis of dough development: impact of mixing parameters and wheat cultivar on the gluten phase distribution. Journal of Food Engineering, 2015; 171: 102–110.
[19] Li N Y, Ye J W, Ji Y, Ling H B, Yu J Y. Saliency detection on light field. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014; pp.2806–2813.
[20] Changyeun M, Giyoung K, Jongguk L, Moon K, Hyunjeong C, Byoung-Kwan C. Detection of lettuce discoloration using hyperspectral reflectance imaging. Sensors, 2015; 15(11): 29511–29534.
[21] Shi P, Wan M, Hong J, Chen J, Zhang L. A parallel fish image processing pipeline of high-throughput chromosomal analysis. International Conference on Information Technology in Medicine & Education, IEEE, 2015; pp.337–342.
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Published
2020-06-08
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Chen, J., Lian, Y., Zou, R., Zhang, S., Ning, X., & Han, M. (2020). Real-time grain breakage sensing for rice combine harvesters using machine vision technology. International Journal of Agricultural and Biological Engineering, 13(3), 194–199. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/5478
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Information Technology, Sensors and Control Systems
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