Online Detection of Tomatoes for Robotic Harvesting

Document Type : Original Research

Authors

1 Department of Biosystem Engineering, Faculty of Agriculture, University of Tabriz, Tabriiz, Iran.

2 Department of Electronic, Faculty of Electrical Engineering, University of Tabriz, Tabriz, Iran.

Abstract

To minimize potential damage, it is crucial to carefully harvest greenhouse crops like tomatoes at the optimal time. To improve this process, the use of robotic harvesting methods has been proposed. The robotic harvester consists of important components including a mobile platform with robotics, displacement units that can move linearly or rotationally, a manipulator, a gripper, a camera, an image processing-based fruit detection unit, and a depth sensor. A robotic manipulator with three linear degrees of freedom was created in the Cartesian coordinate system. To enhance its capabilities, a gripper mechanism was incorporated, providing an additional rotational degree of freedom. The primary objective of this robot was to autonomously detect the position of ripe tomatoes. To achieve this, the displacement control of both the robot arms and gripper was executed through commands from the image processing unit. Different channel of some color space was studied. The effectiveness of this channels was assessed by conducting tests in the presence of tomato plants. The accuracy of the system in approaching the crop were thoroughly evaluated. Channels H of HSV color space, Cr of YCrCb color space, and a of Lab color space showed better result. The accuracy of detecting ripe tomatoes in channel H of HSV color space was the highest and 87%.

Keywords

References

Abdi-pour, M., & Shamsi, M. (2023). Design Methodology and Optimum Camera Setups for an Experimental Remote-Control Manipulator for Servicing Date Palms. Biomechanism and Bioenergy Research, 2(1), 76-83. 10.22103/BBR.2023.20646.1038
Benavides, M., Cantón-Garbín, M., Sánchez-Molina, J., & Rodríguez, F. (2020). Automatic tomato and peduncle location system based on computer vision for use in robotized harvesting. Applied Sciences, 10(17), 5887. https://doi.org/10.3390/app10175887
Chen, X. (2015). Simplicity Driven Solution for Recognition and Localization of Tomatoes and Fruits in a Robotic Harvesting Environment University of Guelph].
Feng, Q., Wang, X., Wang, G., & Li, Z. (2015). Design and test of tomatoes harvesting robot. 2015 IEEE international conference on information and automation, https://doi.org/10.1109/ICInfA.2015.7279423
Feng, Q., Zou, W., Fan, P., Zhang, C., & Wang, X. (2018). Design and test of robotic harvesting system for cherry tomato. International Journal of Agricultural and Biological Engineering, 11(1), 96-100. https://doi.org/10.25165/j.ijabe.20181101.2853
Gonzalez, R. C. (2009). Digital image processing. Pearson education india.
Hashimoto, A., Suehara, K.-i., & Kameoka, T. (2012). Quantitative evaluation of surface color of tomato fruits cultivated in remote farm using digital camera images. SICE Journal of Control, Measurement, and System Integration, 5(1), 18-23. https://doi.org/10.9746/jcmsi.5.18
Ikeda, T., Fukuzaki, R., Sato, M., Furuno, S., & Nagata, F. (2021). Tomato recognition for harvesting robots considering overlapping leaves and stems. Journal of Robotics and Mechatronics, 33(6), 1274-1283. https://doi.org/10.20965/jrm.2021.p1274
Lili, W., Bo, Z., Jinwei, F., Xiaoan, H., Shu, W., Yashuo, L., . . . Chongfeng, W. (2017). Development of a tomato harvesting robot used in greenhouse. International Journal of Agricultural and Biological Engineering, 10(4), 140-149. https://doi.org/10.25165/j.ijabe.20171004.3204
Malik, M. H., Zhang, T., Li, H., Zhang, M., Shabbir, S., & Saeed, A. (2018). Mature tomato fruit detection algorithm based on improved HSV and watershed algorithm. IFAC-PapersOnLine, 51(17), 431-436. https://doi.org/10.1016/j.ifacol.2018.08.183
Menesatti, P., Angelini, C., Pallottino, F., Antonucci, F., Aguzzi, J., & Costa, C. (2012). RGB color calibration for quantitative image analysis: The “3D Thin-Plate Spline” warping approach. Sensors, 12(6), 7063-7079. https://doi.org/10.3390/s120607063
Mohammadi Manour, H., Alimardani, R., & Omid, M. (2013). Computer vision system for automatic harvesting of greenhouse tomatoes under natural light conditions. Agricultural machines, 3(1), 9-15.
Wan, P., Toudeshki, A., Tan, H., & Ehsani, R. (2018). A methodology for fresh tomato maturity detection using computer vision. Computers and electronics in agriculture, 146, 43-50. https://doi.org/10.1016/j.compag.2018.01.011
Wang, X.-X., Zhao, F., Zhang, G., Zhang, Y., & Yang, L. (2017). Vermicompost improves tomato yield and quality and the biochemical properties of soils with different tomato planting history in a greenhouse study. Frontiers in plant science, 8, 1978. https://doi.org/10.3389/fpls.2017.01978
Wei, X., Jia, K., Lan, J., Li, Y., Zeng, Y., & Wang, C. (2014). Automatic method of fruit object extraction under complex agricultural background for vision system of fruit picking robot. Optik, 125(19), 5684-5689. https://doi.org/10.1016/j.ijleo.2014.07.001
Whittaker, D., Miles, G., Mitchell, O., & Gaultney, L. (1987). Fruit location in a partially occluded image. Transactions of the ASAE, 30(3), 591-0596. https://doi.org/10.13031/2013.30444
Yin, H., Chai, Y., Yang, S. X., & Mittal, G. S. (2009). Ripe tomato extraction for a harvesting robotic system. 2009 IEEE International Conference on Systems, Man and Cybernetics, https://doi.org/10.1109/ICSMC.2009.5345994
Yoshida, T., Fukao, T., & Hasegawa, T. (2019). A tomato recognition method for harvesting with robots using point clouds. 2019 IEEE/SICE International Symposium on System Integration (SII). https://doi.org/10.1109/SII.2019.8700358
Zeng, T., Li, S., Song, Q., Zhong, F., & Wei, X. (2023). Lightweight tomato real-time detection method based on improved YOLO and mobile deployment. Computers and electronics in agriculture, 205, 107625. https://doi.org/10.1016/j.compag.2023.107625
 
Biomechanism and Bioenergy Research
Volume 2, Issue 2
December 2023
Pages 130-142

Files

History

  • Receive Date: 02 November 2023
  • Revise Date: 25 December 2023
  • Accept Date: 25 December 2023

Share

How to cite

Statistics