Journal of Biosystems Engineering

Korean Society for Agricultural Machinery (한국농업기계학회)
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Vision-based Potato Detection and Counting System for Yield Monitoring

  • Lee, Young-Joo (Department of Biosystems Engineering, Kangwon National University) ;
  • Kim, Ki-Duck (Department of Biosystems Engineering, Kangwon National University) ;
  • Lee, Hyeon-Seung (Department of Biosystems Engineering, Kangwon National University) ;
  • Shin, Beom-Soo (Department of Biosystems Engineering, Kangwon National University)
  • Received : 2018.05.11
  • Accepted : 2018.06.07
  • Published : 2018.06.01
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Abstract

Purpose: This study has been conducted to develop a potato yield monitoring system, consisting of a segmentation algorithm to detect potatoes scattered on a soil surface and a counting system to count the number of potatoes and convert the data from two-dimensional images to masses. Methods: First, a segmentation algorithm was developed using top-hat filtering and processing a series of images, and its performance was evaluated in a stationary condition. Second, a counting system was developed to count the number of potatoes in a moving condition and calculate the mass of each using a mass estimation equation, where the volume of a potato was obtained from its two-dimensional image, and the potato density and a correction factor were obtained experimentally. Experiments were conducted to segment potatoes on a soil surface for different potato sizes. The counting system was tested 10 times for 20 randomly selected potatoes in a simulated field condition. Furthermore, the estimated total mass of the potatoes was compared with their actual mass. Results: For a $640{\times}480$ image size, it took 0.04 s for the segmentation algorithm to process one frame. The root mean squared deviation (RMSD) and average percentage error for the measured mass of potatoes using this counting system were 12.65 g and 7.13%, respectively, when the camera was stationary. The system performance while moving was the best in L1 (0.313 m/s), where the RMSD and percentage error were 6.92 g and 7.79%, respectively. For 20 newly prepared potatoes and 10 replication measurements, the counting system exhibited a percentage error in the mass estimation ranging from 10.17-13.24%. Conclusions: At a travel speed of 0.313 m/s, the average percentage error and standard deviation of the mass measurement using the counting system were 12.03% and 1.04%, respectively.

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References

  1. Ehlert, D. 2000. Measuring mass flow by bounce plate for yield mapping of potatoes. Precision Agriculture 2(2): 119-130. https://doi.org/10.1023/A:1011469429338
  2. ElMasry, G., S. Cubero, E. Molto and J. Blasco. 2012. In-line sorting of irregular potatoes by using automated computer-based machine vision system. Journal of Food Engineering 112(1-2): 60-68. https://doi.org/10.1016/j.jfoodeng.2012.03.027
  3. Gogineni, S., J. G. White, J. A. Thomasson, P. G. Thompson, J. R. Wooten and M. Shankle. 2002. Image-based sweetpotato yield and grade monitor. In: 2002 ASAE Annual Meeting, ASAE Paper No. 021169. St. Joseph, MI: ASAE. http://doi.org/10.13031/2013.10586
  4. Hofstee, J. W. and G. J. Molema. 2002. Machine vision based yield mapping of potatoes. In: 2002 ASAE Annual Meeting, ASAE Paper No. 021200. St. Joseph, MI: ASAE. http://doi.org/10.13031/2013.9699
  5. Hofstee, J. W. and G. J. Molema. 2003. Volume estimation of potatoes partly covered with dirt tare. In: 2003 ASAE Annual Meeting, ASAE Paper No. 031001. St. Joseph, MI: ASAE. http://doi.org/10.13031/2013.15380
  6. Kumhala, F., V. Prosek and J. Blahovec. 2009. Capacitive throughput sensor for sugar beets and potatoes. Biosystems Engineering 102(1): 36-43. https://doi.org/10.1016/j.biosystemseng.2008.10.002
  7. MATLAB. 2016. Image Processing Toolbox User's Guide. Ver. R2016a. Natick, MA, USA: The MathWorks, Inc.
  8. Park D. S. 2008. Development of steering controller for autonomous-guided orchard sprayer. Unpublished MS thesis. Chuncheon, Gangwon-do, Rep. Korea: Department of Biological Systems Engineering, Kangwon National University.
  9. Persson, D. A., L. Eklundh and P.-A. Algerbo. 2004. Evaluation of an optical sensor for tuber yield monitoring. Transaction of the ASAE 47(5): 1851-1856. http://doi.org/10.13031/2013.17602
  10. Pitts, M. J., G. M. Hyde and R. P. Cavalieri. 1987. Modeling potato tuber mass with tuber dimensions. Transactions of the ASAE 30(4): 1154-1159. http://doi.org/10.13031/2013.30536
  11. Sylla, C. 2002. Experimental investigation of human and machine-vision arrangements in inspection tasks. Control Engineering Practice 10(3): 347-361. https://doi.org/10.1016/S0967-0661(01)00151-4
  12. Vellidis, G., C. D. Perry, J. S. Durrence, D. L. Thomas, R. W. Hill, C. K. Kvien, T. K. Hamrita and G. Rains. 2001. The peanut yield monitoring system. Transactions of the ASAE 44(4): 775-785. http://doi.org/10.13031/2013.6239