Vision-based Size Estimation and Centroid Positioning of Partially Occluded Fruits

Mya Thin Kyu Kyu; Nay Zar Aung

doi:10.29037/ajstd.636

Vol. 37 No. 3 (2020), Research Article

Vol. 37 No. 3 (2020)

Vision-based Size Estimation and Centroid Positioning of Partially Occluded Fruits

Research Article

https://doi.org/10.29037/ajstd.636

Published 2020-12-25 — Updated on 2020-12-25

Mya Thin Kyu Kyu⁺⁻
Nay Zar Aung⁺⁻

Mya Thin Kyu Kyu

Department of Mechatronic Engineering, Yangon Technological University, Insein Rd, Yangon 11101, Myanmar

Nay Zar Aung

Department of Mechatronic Engineering, Yangon Technological University, Insein Rd, Yangon 11101, Myanmar

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Keywords

Automatic harvesting system
Conic-section curve-fitting
Fruit-picking robot
Fruit size estimation
Vision system

Abstract

The objective of this study was to propose a simple and efficient image processing algorithm for estimating the size and centroid of partially occluded round fruits. In the proposed method, the size and centroid of partially occluded fruit were estimated based on the mathematical properties of the arc-radius. The experimental tests were conducted in a laboratory with orange, Sunkist, apple, and tomato fruits by setting different occlusion conditions. The occlusion percentage was varied between 0% and 90%. The accuracy and processing time of the proposed method were compared with that of the widely-used conic-section circle fitting method. The results showed that the proposed method resulted in an overall accuracy of 95.1% and processing time of 0.66 s, as opposed to 60.2% and 0.68 s, respectively, using the conic-section equation. Compared with the conic-section equation, the proposed method was able to give a more robust prediction, even for low resolution images.

https://doi.org/10.29037/ajstd.636

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References

Barnea E, Mairon R, Ben-Shahar O. 2016. Colour-agnostic shape-based 3D fruit detection for crop harvesting robots. Biosyst Eng. 146:57–70. doi:10.1016/j.biosystemseng.2016.01.013.

Bulanon DM, Burks TF, Alchanatis V. 2008. Study on temporal variation in citrus canopy using thermal imaging for citrus fruit detection. Biosyst Eng. 101(2):161–171. doi:10.1016/j.biosystemseng.2008.08.002.

Ge Y, Xiong Y, From PJ. 2019. Instance segmentation and localization of strawberries in farm conditions for automatic fruit harvesting. IFAC-PapersOnLine. 52(30):294–299. doi:10.1016/j.ifacol.2019.12.537.

Gené-Mola J, Sanz-Cortiella R, Rosell-Polo JR, Morros JR, Ruiz-Hidalgo J, Vilaplana V, Gregorio E. 2020. Fruit detection and 3D location using instance segmentation neural networks and structure-from-motion photogrammetry. Comput Electron Agric. 169:105165. doi:10.1016/j.compag.2019.105165.

Gongal A, Amatya S, Karkee M, Zhang Q, Lewis K. 2015. Sensors and systems for fruit detection and localization: a review. Comput Electron Agric. 116:8–19. doi:10.1016/j.compag.2015.05.021.

Ji W, Zhao D, Cheng F, Xu B, Zhang Y, Wang J. 2012. Automatic recognition vision system guided for apple harvesting robot. Comput Electron Agric. 38(5):1186–1195. doi:10.1016/j.compeleceng.2011.11.005.

Jidong L, De-An Z, Wei J, Shihong D. 2016. Recognition of apple fruit in natural environment. Optik. 127(3):1354–1362. doi:10.1016/j.ijleo.2015.10.177.

Jiménez AR, Jain AK, Ceres R, Pons JL. 1999. Automatic fruit recognition: a survey and new results using range/attenuation images. Pattern Recognit. 32(10):1719–1736. doi:10.1016/S0031-3203(98)00170-8.

Kang H, Chen C. 2020. Fruit detection, segmentation and 3D visualisation of environments in apple orchards. Comput Electron Agric. 171:105302. doi:10.1016/j.compag.2020.105302.

Le TT, Lin CY, Piedad EJ. 2019. Deep learning for noninvasive classification of clustered horticultural crops – a case for banana fruit tiers. Postharvest Biol Technol. 156:110922. doi:10.1016/j.postharvbio.2019.05.023.

Li P, Lee SH, Hsu HY. 2011. Review on fruit harvesting method for potential use of automatic fruit harvesting systems. Procedia Eng. 23:351–366. doi:10.1016/j.proeng.2011.11.2514.

Lu J, Sang N. 2015. Detecting citrus fruits and occlusion recovery under natural illumination conditions. Comput Electron Agric. 110:121–130. doi:10.1016/j.compag.2014.10.016.

Luo L, Tang Y, Zou X, Ye M, Feng W, Li G. 2016. Vision-based extraction of spatial information in grape clusters for harvesting robots. Biosyst Eng. 151:90–104. doi:10.1016/j.biosystemseng.2016.08.026.

Lv J, Wang Y, Ni H, Wang Q, Rong H, Ma Z, Yang B, Xu L. 2019a. Method for discriminating of the shape of overlapped apple fruit images. Biosyst Eng. 186:118–129. doi:10.1016/j.biosystemseng.2019.07.003.

Lv J, Wang Y, Xu L, Gu Y, Zou L, Yang B, Ma Z. 2019b. A method to obtain the near-large fruit from apple image in orchard for single-arm apple harvesting robot. Sci Hortic. 257:108758. doi:10.1016/j.scienta.2019.108758.

Maldonado W, Barbosa JC. 2016. Automatic green fruit counting in orange trees using digital images. Comput Electron Agric. 127:572–581. doi:10.1016/j.compag.2016.07.023.

Mehta SS, Ton C, Asundi S, Burks TF. 2017. Multiple camera fruit localization using a particle filter. Comput Electron Agric. 142:139–154. doi:10.1016/j.compag.2017.08.007.

Meng J, Wang S. 2015. The recognition of overlapping apple fruits based on boundary curvature estimation. Paper presented at: ISDEA 2015. Proceedings of 2015 Sixth International Conference on Intelligent Systems Design and Engineering Applications (ISDEA); Guiyang, China. p. 874–877. doi:10.1109/ISDEA.2015.221.

M?otek M, Kuta ?, Stopa R, Komarnicki P. 2015. The effect of manual harvesting of fruit on the health of workers and the quality of the obtained produce. Procedia Manuf. 3:1712–1719. doi:10.1016/j.promfg.2015.07.494.

Schertz CE, Brown GK. 1968. Basic considerations in mechanizing citrus harvest. Trans ASAE. 11(3):0343–0346. doi:10.13031/2013.39405.

Selesnick I. 2013. Least squares with examples in signal processing. OpenStax CNX. http://cnx.org/content/m46131/1.1/.

Xiang R, Jiang H, Ying Y. 2014. Recognition of clustered tomatoes based on binocular stereo vision. Comput Electron Agric. 106:75–90. doi:10.1016/j.compag.2014.05.006.

Yu Y, Zhang K, Yang L, Zhang D. 2019. Fruit detection for strawberry harvesting robot in non-structural environment based on mask-RCNN. Comput Electron Agric. 163:104846. doi:10.1016/j.compag.2019.06.001.

Zapotezny-Anderson P, Lehnert C. 2019. Towards active robotic vision in agriculture: a deep learning approach to visual servoing in occluded and unstructured protected cropping environments. IFAC-PapersOnLine. 52(30):120–125. doi:10.1016/j.ifacol.2019.12.508.

Zhang Q, Gao G. 2020. Prioritizing robotic grasping of stacked fruit clusters based on stalk location in RGB-D images. Comput Electron Agric. 172:105359. doi:10.1016/j.compag.2020.105359.

Zhuang JJ, Luo SM, Hou CJ, Tang Y, He Y, Xue XY. 2018. Detection of orchard citrus fruits using a monocular machine vision-based method for automatic fruit picking applications. Comput Electron Agric. 152:64–73. doi:10.1016/j.compag.2018.07.004.

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