Design and test of stem diameter inspection spherical robot

Longzhe Quan, Ci Chen, Yajun Li, Yajing Qiao, Dejun Xi, Tianyu Zhang, Wenfeng Sun

Abstract


Stem diameter is an important parameter in the process of plant growth which can indicate the growth state and moisture content of the plant, its automatic detection is necessary. Traditional devices have many drawbacks that limit their practical uses in general case. To solve those problems, a stem diameter inspection spherical robot was developed in this study. The particular mechanism of the robot has turned out to be suitable for performing monitoring tasks in greenhouse mainly due to its spherical shape, small size, low weight and traction system that do not produce soil compacting or erosion. The mechanical structure and hardware architecture of the spherical robot were described, the algorithm based on binocular stereo vision was developed to measure the stem diameter of the plant. The effectiveness of the prototype robot was confirmed by field experiments in a tomato greenhouse. The results showed that the machine measurement data was linearly correlated with the manual measurement data with R2 of 0.9503. There was no significant difference for each attribute between machine measurement data and manual measurement data (sig > 0.05). The results showed that this method was feasible for nondestructive testing of the stem diameter of greenhouse plants.
Keywords: stem diameter inspection, spherical robot, binocular stereo vision, Census transform
DOI: 10.25165/j.ijabe.20191202.4163

Citation: Quan L Z, Chen C, Li Y J, Qiao Y J, Xi D J, Zhang T Y, et al. Design and test of stem diameter inspection spherical robot. Int J Agric & Biol Eng, 2019; 12(2): 141–151.

Keywords


stem diameter inspection, spherical robot, binocular stereo vision, Census transform

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References


Trejo-Perea M, Herrera-Ruiz G, Rios-Moreno J, Castaneda Miranda R, Rivas-Araiza E. Greenhouse energy consumption prediction using neural networks models. Int J Agric & Biol Eng, 2009; 11(1): 1–6.

Campillo C, Garcia MI, Daza C, Prieto M H. Study of a non-destructive method for estimating the leaf area index in vegetable crops using digital images. Hortscience, 2010; 45(10): 1459–63.

Hwang J, Shin C, Yoe H. Study on an agricultural environment monitoring server system using wireless sensor networks. Sensors, 2010; 10(12): 11189–11211.

Jesus Roldan J, Joossen G, Sanz D, del Cerro J, Barrientos A. Mini-UAV based sensory system for measuring environmental variables in greenhouses. Sensors, 2015; 15(2): 3334–3350.

Gallardo M, Thompson R B, Valdez L C, Fernandez M D. Use of stem diameter variations to detect plant water stress in tomato. Irrigation Science, 2006; 24(4): 241–55.

Meng Z, Duan A, Liu J, Zhang J. Advances on diagnosis of crop moisture content from changes in stem diameters of plants. Transactions of the CSAE, 2005; 21(2): 30–33. (in Chinese)

Intrigliolo D S, Castel J R. Continuous measurement of plant and soil water status for irrigation scheduling in plum. Irrigation Science, 2004; 23(2): 93–102.

Stao N, Hasegawa K. A computer controlled irrigation system for muskmelon using stem diameter sensor. Acta Horticulturae, 1995; 226: 91–98.

Ying Y B, Fu B Z, Jiang Y Y, Zhao Y. Application of machine vision technique to automation of agricultural production. Transactions of the CSAE, 1999; 15(3): 199–203. (in Chinese)

Takahashi T, Zhang S, Fukuchi H. Binocular stereo vision system for measuring distance of apples in orchard (Part 1) – Method due to composition of left and right images. Journal of the Japanese Society of Agricultural Machinery, 2000; 62(1): 89–99.

Takahashi T, Zhang S, Fukuchi H. Binocular stereo vision system for measuring distance of apples in orchard (Part 2) – Analysis of and solution to the correspondence problem. Journal of the Japanese Society of Agricultural Machinery, 2000; 62(3): 94–102.

Van Henten E J, Hemming J, Van Tuijl B A J. An autonomous robot for harvesting cucumbers in greenhouse. Autonomous Robots, 2002; 13: 241–258.

Xiang R, Ying Y B, Jiang H Y, Peng Y S. Localization of tomatoes based on binocular stereo vision. Transactions of the CSAE, 2012; 28(5): 161–167. (in Chinese)

Rovira-Más F, Zhang Q, Reid F. Stereo vision threedimensional terrain maps for precision agriculture. Computers and Electronics in Agriculture, 2008；60(2): 133－143.

Zhai Z, Zhu Z, Du Y, Song Z, Mao E. Multi-crop-row detection algorithm based on binocular vision. Biosystems Engineering, 2016; 150: 89–103.

Yang F Z, Liu S, Chen L P, Song H B, Wang Y J, Lan Y B. Detection method of various obstacles in farmland based on stereo vision technology. Transactions of the CSAM, 2012; 28(5): 161–167. (in Chinese)

Huang Y, Young K. Binocular image sequence analysis: Integration of stereo disparity and optic flow for improved obstacle detection and tracking. EURASIP Journal on Advances in Signal Processing, 2008; 843232. https://doi.org/10.1155/2008/843232.

Hernandez J D, Barrientos J, del Cerro J, Barrientos A, Sanz D. Moisture measurement in crops using spherical robots. Industrial Robot-an International Journal, 2013; 40(1): 59–66.

Cancar L, Sanz D, Hernandez J D, del Cerro J, Barrientos A. Precision humidity and temperature measuring in farming using newer ground mobile robots. Springer International Publishing, 2014-06-15

Bruhn F C, Kratz H, Warell J, Lagerkvist C-I, Kaznov V, Jones J A, et al. A preliminary design for a spherical inflatable microrover for planetary exploration. Acta Astronautica, 2008; 63(5-6): 618–31.

Michaud F, Lafontaine J D, Caron S A. Spherical robot for planetary surface exploration. Proceeding of the 6th International Symposium on Artificial Intelligence and Robotics & Automation in Space, 2001.

Seeman M, Broxvall M, Saffiotti A, Wide P. An autonomous spherical robot for security tasks. Proceedings of the 2006 IEEE International Conference on Computational Intelligence for Homeland Security and Personal Safety, 2006; pp.51–55.

Michaud F, Caron S. Roball, the Rolling Robot. Autonomous Robots, 2002; 12(2): 211–222

Quan L Z, Chen C, Zhang T Y, Qiao Y J. A Spherical robot with obstacle avoidance function for crop bottom stem inspection. China Patent, ZL201610383579.3. 2016-06-0.

Bekker G. Introduction to terrain-vehicle systems. Ann Arbor: University of Michigan Press, 1969.

Geng R Y, Zhang D L, Wang X Y, Yang Z D. New agricultural mechanics. Beijing: National Defense Industry Press, 2015. (in Chinese)

Zhou C D. Research on the key technologies of magnetic guided automated guided vehicle and applications. PhD dissertation. Nanjing: Nanjing University of Aeronautics and Astronautics, 2012. (in Chinese)

Brown M Z, Burschka D, Hager G D. Advances in computational stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003; 25(8): 993–1008.

Zhai Z Q, Du Y F, Zhu Z X, Lang J, Mao E R. Three-dimensional reconstruction method of farmland scene based on Rank transformation. Transactions of the CSAE, 2015; 31(20): 157–164. (in Chinese)




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