Abstract
The aim of the paper was to present results of the vision system for plants/weeds classification testing of autonomous robot for sowing and wide row planting. Autonomous work of the robot in range of traction and agronomic processes will be implemented on the basis of data from a many sensors (cameras, position and distance). Positive test results will allow for the use of the robot in organic crops requiring mechanical removal of weeds or in crops with application of selective liquid agrochemicals limited to the minimum. Unless the control systems are improved and development costs are compensated, the production of autonomous agricultural systems will increase. So that very important is mentioned in this paper, vision system of plant/weed classification. The vision system for sugar beet/weed and sweet corn/weed classification was build and tested. The position of each plant must be determined for intra-row weeding. This means that plants have to be classified into two classes, i.e., sugar beet (sweet corn) or weed.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Szczepaniak, J., Tanas, W., Pawlowski, T., Kromulski, J.: Modelling of agricultural combination driver behaviour from the aspect of safety of movement. Ann. Agric. Env. Med. 21(2), 403–406 (2014). https://doi.org/10.5604/1232-1966.1108613
Kromulski, J., Pawłowski, T., Szczepaniak, J., Tanaś, W., Wojtyła, A., Szymanek, M., Tanaś, J., Izdebski, W.: Absorbed power distribution in the whole-body system of a tractor operator. Ann. Agric. Env. Med. 23(2), 373–376 (2016). https://doi.org/10.5604/12321966.1203908
18 October 2017. http://www.trp.uk.com/carre-farm-machinery/carre-meadow-maintenance/anatis.html
Jasiński, M., Mączak, J., Szulim, P., Radkowski, S.: Autonomous agricultural robot - collision avoidance methods overview. Proceed. Inst. Veh. 2(106), 37–44 (2016)
Gałęzia, A.: Teager-Kaiser energetic trajectory for machine diagnosis purposes. J. VibroEng. 19(2), 1014–1025 (2017). https://doi.org/10.21595/jve.2016.17568
Jasiński, M., Mączak, J., Radkowski, S., Korczak, S., Rogacki, R., Mac, J., Szczepaniak, J.: Autonomous agricultural robot — conception of inertial navigation system. In: Szewczyk, R., Zieliński, C., Kaliczyńska, M. (eds.) Challenges in Automation, Robotics and Measurement Techniques. Conference: Automation 2016, Warsaw, Poland, 02–04 March 2016. Advances in Intelligent Systems and Computing, vol. 440, pp. 669–679. https://doi.org/10.1007/978-3-319-29357-8_58
Jasiński, M., Mączak, J., Radkowski, S., Szulim, P., Heba, M.: Optical system for sugar beet/weed classification. In: Proceedings of XVI International Technical Systems Degradation Conference, Liptovský Mikuláš, Slovak Republic, 19–22 April 2017, pp. 138–141 (2017)
18 October 2017. https://forums.xilinx.com/t5/Xcell-Daily-Blog/New-Industrial-Controller-from-NI-boosts-video-processing/ba-p/669207
Tillett, N.D., Hague, T., Grundy, A.C., Dedousis, A.P.: Mechanical within-row weed control for transplanted crops using computer vision. Biosyst. Eng. 99(2), 171–178 (2008)
Burgos-Artizzu, X.P., Ribeiro, A., Guijarro, M., Pajares, G.: Real-time image processing for crop/weed discrimination in maize fields. Comput. Electron. Agric. 75(2), 337–346 (2011)
Dong, F., Heinemann, W., Kasper, R.: Development of a row guidance system for an autonomous robot for white asparagus harvesting. Comput. Electron. Agric. 79(2), 216–225 (2011)
Tellaeche, A., Pajares, G., Burgos-Artizzu, X.P., Ribeiro, A.: A computer vision approach for weeds identification through Support Vector Machines. Appl. Soft Comput. 11(1), 908–915 (2011)
Jiang, D., Yang, L., Li, D., Gao, F., Tian, L., Li, L.: Development of a 3D ego-motion estimation system for an autonomous agricultural vehicle. Biosyst. Eng. 121, 150–159 (2014)
Choi, K.H., Han, S.K., Han, S.H., Park, K.-H., Kim, K.-S., Kim, S.: Morphology-based guidance line extraction for an autonomous weeding robot in paddy fields. Comput. Electron. Agric. 113, 266–274 (2015)
Pantazi, X.-E., Moshou, D., Bravo, C.: Active learning system for weed species recognition based on hyperspectral sensing. Biosyst. Eng. 146, 193–202 (2016)
Astrand, B., Baerveldt, A.-J.: An agricultural mobile robot with vision-based perception for mechanical weed control. Auton. Robot. 13, 21–35 (2002)
Heba, M.: Conception of the optical recognition plant system. B.Sc. thesis, Warsaw University of Technology, Warsaw (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Jasiński, M., Mączak, J., Szulim, P., Radkowski, S. (2018). Autonomous Agricultural Robot – Testing of the Vision System for Plants/Weed Classification. In: Szewczyk, R., Zieliński, C., Kaliczyńska, M. (eds) Automation 2018. AUTOMATION 2018. Advances in Intelligent Systems and Computing, vol 743. Springer, Cham. https://doi.org/10.1007/978-3-319-77179-3_44
Download citation
DOI: https://doi.org/10.1007/978-3-319-77179-3_44
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-77178-6
Online ISBN: 978-3-319-77179-3
eBook Packages: EngineeringEngineering (R0)