The development of an automatic rubber seed sowing system with machine vision assistance
Tóm tắt
Natural rubber is an important world commodity to produce gloves, tyres and many other important goods. Rubber plant naturally grows from seeds and it can also be grafted artificially. Rubber seedlings in nurseries are currently manually sowed by labour. The rubber seed must be placed with the dorsal side facing upwards and the ventral side facing downwards. Due to the labour shortage in plantations currently faced in Malaysia, nursery automation would be beneficial to maintain production and to increase productivity. An automated nursery seeding system would be able to reduce labour requirements and further increase the seed placement accuracy of manual sowing. The objective of this paper is to elaborate on the development and testing of an automated robotic arm with camera vision assistance for sowing rubber seeds. The developed robotic arm was shown to successfully pick rubber seeds and place them in a polybag germinator in the correct orientation. An integrated machine vision was used to assist in identifying the surface of rubber seeds to get the correct positioning during sowing. The seed image frames were obtained from a recorded video from a smartphone camera and they were transferred to a computer via a wireless network and processed using Matlab to identify the dorsal and ventral side of the seed. The developed system had managed to achieve 90% accuracy under indoor light condition of 40 samples of rubber seeds tested. The system gave the highest precision (100%) at 50% motor speed setting. The average time of 10 s was taken to complete the operation from capturing the seed image to successfully placing the seed in the correct orientation.
Tài liệu tham khảo
LGM (2019) Natural rubber statistics. Malaysian Rubber Board. https://www.lgm.gov.my/webv2/pdfViewer/nrStatistic. Accessed 17 Mar 2021
DOSM (2019) Monthly rubber statistics Malaysia. Department of Statistics Malaysia Official Portal. https://www.dosm.gov.my/v1/index.php?r=column/cthemeByCat&cat=73&bul_id=aTR5UnlJS2R0Mnhtckhidkw4OGVIUT09&menu_id=Z0VTZGU1UHBUT1VJMFlpaXRRR0xpdz09. Accessed 17 Mar 2021
Marattukalam JG, Saraswathyamma CK (1992) Propagation and planting. Dev Crop Sci 23:164–199. https://doi.org/10.1016/B978-0-444-88329-2.50014-3
Ahmad B, Ibrahim N, Othman R (2006) The merits of using advanced planting materials in rubber forest plantation. J Rubber Res 9(3):178–192
Verheye W (2010) Growth and production of rubber. In: Verheye WH (ed) Land use, land cover and soil sciences. UNESCO-EOLSS Publishers, Oxford
Mohd Johari SNA, Bejo SK, Wan Ishak WI (2017) Identification of dorsal and vertical surface of rubber seeds using image processing approach. Sci Int (Lahore) 29(2):121–125
Russo M, Ceccarelli M, Corves B, Hüsing M, Lorenz M, Cafolla D, Carbone G (2017) Design and test of a gripper prototype for horticulture products. Robot Comput Integr Manuf 44:266–275. https://doi.org/10.1016/j.rcim.2016.09.005
Mehta SS, Burks TF (2014) Vision-based control of robotic manipulator for citrus harvesting. Comput Electron Agric 102:146–158. https://doi.org/10.1016/j.compag.2014.01.003
Feng Q, Zou W, Fan P, Zhang C, Wang X (2018) Design and test of robotic harvesting system for cherry tomato. Int J Agric Biol Eng 11(1):96–100. https://doi.org/10.25165/j.ijabe.20181101.2853
Xiong Y, Ge Y, Grimstad L, Pal J (2020) An autonomous strawberry-harvesting robot: design, development, integration, and field evaluation. J Field Robot 37(2):202–224. https://doi.org/10.1002/rob.21889
De Preter A, Anthonis J, De Baerdemaeker J (2018) Development of a robot for harvesting strawberries. IFAC PapersOnLine 51(17):14–19. https://doi.org/10.1016/j.ifacol.2018.08.054
Hayashi S, Shigematsu K, Yamamoto S, Kobayashi K, Kohno Y, Kamata J, Kurita M (2010) Evaluation of a strawberry-harvesting robot in a field test. Biosyst Eng 105(2):160–171. https://doi.org/10.1016/j.biosystemseng.2009.09.011
Jain R, Kasturi R, Schunck BG (1995) Machine vision. McGraw-Hill, New York
Radcliffe J, Cox J, Bulanon DM (2018) Machine vision for orchard navigation. Comput Ind 98:165–171. https://doi.org/10.1016/j.compind.2018.03.008
Gongal A, Karkee M, Amatya S (2018) Apple fruit size estimation using a 3D machine vision system. Inf Process Agric 5(4):498–503. https://doi.org/10.1016/j.inpa.2018.06.002
Qureshi WS, Payne A, Walsh KB, Linker R, Cohen O, Dailey MN (2017) Machine vision for counting fruit on mango tree canopies. Precis Agric 18(2):224–244. https://doi.org/10.1007/s11119-016-9458-5
Tian Z, Ma W, Yang Q, Duan F (2021) Application status and challenges of machine vision in plant factory—a review. Inf Process Agric. https://doi.org/10.1016/j.inpa.2021.06.003 (in press)
Hashim H, Osman FN, Al Junid SAM, Haron MA, Salleh HM (2010) An intelligent classification model for rubber seed clones based on shape features through imaging techniques. In: International conference on intelligent systems modelling and simulation (ISMS), p 25–31. https://doi.org/10.1109/ISMS.2010.16
Adjemout O, Hammouche K, Diaf M (2007) Automatic seeds recognition by size, form and texture features. In: 9th International symposium on signal processing and its applications. https://doi.org/10.1109/ISSPA.2007.4555428
Wang L, Zhao B, Fan J, Hu X, Wei S, Li Y, Zhou Q, Wei C (2017) Development of a tomato harvesting robot used in greenhouse. Int J Agric Biol Eng 10(4):140–149. https://doi.org/10.25165/j.ijabe.20171004.3204