Abstract
In this study, a new perspective for increasing the measurement accuracy of a chickpea pile volume and weight estimation tool is proposed. The system proposed uses the principles of machine learning methods and focuses on the adaptation of Moore-neighbor tracing algorithm in sphericity calculation of chickpeas. An experimental setup including a two degrees of freedom moving mechanism, a low-cost laser scanning rangefinder sensor, and a vision unit combining a camera and image processing algorithm is constructed. The methodology proposed is tested to estimate the weight of a chickpea pile using this experimental setup. In order to make comparisons, the estimations are also performed by the use of approaches proposed in the literature. The results of the experimental studies show that at least 5% weight estimation error is obtained when the estimation procedures given in the literature are used. On the other hand, the algorithm proposed in this study yields estimation error less than 0.57%. The details of the procedure proposed, the experimental setup designed and built, the computational environment developed and the experiments conducted are presented in this study.
Similar content being viewed by others
References
J.J. Roldan, J. Cerro, D.G. Ramos, P.G. Aunon, M. Garzon, J. Leon, A. Barrientos, Robots in agriculture: state of art and practical experiences. Service robots, IntechOpen, 67–90 (2018)
M. Kitazume, The Sand Compaction Pile Method (CRC Press, Boca Raton, 2005)
P.L. Raeva, S.L. Filipova, D.G. Filipova, Volume computation of a stockpile – a study case comparing GPS and UAV measurements in an open quarry. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLI-B1, 999-1004 (2016)
Y. Zhao, L. Gong, Y. Huang, C. Liu, A review of key techniques of vision-based control for harvesting robot. Computers and Electronics in Agriculture 127, 311–323 (2016)
C.C. Hardy, Guidelines for estimating volume, biomass, and smoke production for piled slash. United States Department of Agriculture, Forest Service, Pacific Northwest Research Station, General Technical Report PNW-GTR-364 (1996)
P.V. Pramiu, R.L. Rizzi, N.V. Prado, S.R. Coelho, P.Z. Bassinello, Numerical modelling of chickpea (Cicer arietinum) hydration: the effects of temperature and low pressure. J. Food Eng. 165, 112–123 (2015)
S.P. Mukhopadhyay, A.J. Saliba, B.T. Carr, C.L. Blanchard, J.A. Wood, P.D. Prenzler, Sensory profiling and preference mapping of Australian puffed desi chickpeas. LWT 89, 229–236 (2018)
R. Costa, F. Fusco, J.F.M. Gandara, Mass transfer dynamics in soaking of chickpea. J. Food Eng. 227, 42–50 (2018)
J.A. Wood, C.F. Keir, A simple method to compare seed shape in chickpea (Cicer arietinum). Indian J. Agric. Sci. 78(12), 1048–1052 (2008)
T.C. Hales, Cannonballs and honeycombs. Notices of AMS 47(4), 440–449 (2008)
T.C. Hales, Dense sphere packing: a blueprint for formal proofs (Cambridge Unv. Press, New York, 2012)
S.S. Tulluri, Analysis of random packing of uniform spheres using the monte-carlo simulation method. PhD Thesis, New Jersey Institute of Technology, US (2003)
Z.H. Stachurski, Fundamentals of Amorphous Solids: Structure and Properties, 1st edn. (Wiley-VCH, New York, 2015)
H. Wadell, Volume, shape and roundness of quartz particles. J. Geol. 43(3), 250–280 (1935)
G. Bagheri, C. Bonadonna, Chapter 2 - Aerodynamics of volcanic particles: characterization of size, shape, and settling velocity. Volcanic Ash: Hazard Observation, (Elsevier Ltd, Amsterdam, 2016)
W.L. McCabe, J.C. Smith, P. Harriott, Unit Operations of Chemical Engineering, 6th edn. (McGraw-Hill Co, Boston, 2001), p. 158
C.J. Geankoplis, Transport Processes and Unit Operations, 2nd edn. (Allyn and Bacon Inc., Boston, 1983), pp. 132–135
M. Bayram, Determination of the sphericity of granular food materials. J. Food Eng. 68, 385–390 (2005)
T.H. Mandeel, M.I. Ahmad, M.N.M. Isa, S.A. Anwar, R. Ngadiran, Palmprint region of interest cropping based on Moore-neighbor tracing algorithm. Sensing and Imaging 19, 1–15 (2018)
P. Sharma, M. Diwakar, N. Lal, Edge detection using Moore neighborhood. Int. J. Comput. Appl. 61(3), 26–30 (2013)
R. Pradhan, S. Kumar, R. Agarwal, M.P. Pradhan, M.K. Ghose, Contour line tracing algorithm for digital topographic maps. International Journal of Image Processing 4(2), 156–163 (2010)
F.P. Miller, A.F. Vandome, J. McBrewster, Moore Neighborhood (Alphascript Publishing, Lewisburg, 2010)
C. Sha, J. Hou, H. Cui, A robust 2D Otsu’s thresholding method in image segmentation. J. Visual Commun. Image Represent. 41, 339–351 (2016)
Hokuyo, Technical specifications of the laser scanning rangefinder sensor, Hokuyo-URG-04LX-UG01, https://www.hokuyo-aut.jp/search/single.php?serial=166. Accessed August 13, 2020
L.G. Boi, Classic Asian Noodles, 1st edn. (Marshall Cavendish Cuisine, Asia, 2007)
P. Tripathy, P.K. Tripathy, Fundamentals of Research: A Dissective View (Anchor Academic Publishing, New York, 2017)
Acknowledgments
The author would like to thank to the Engineering Faculty of Zonguldak Bulent Ecevit University for its support in this research.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The author declares that there is no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Bayar, G. Increasing measurement accuracy of a chickpea pile weight estimation tool using Moore-neighbor tracing algorithm in sphericity calculation. Food Measure 15, 296–308 (2021). https://doi.org/10.1007/s11694-020-00637-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11694-020-00637-4