Design and experiment of intelligent sorting and transplanting system for healthy vegetable seedlings
Abstract
Healthy vegetable seedlings are surviving seedlings with good biological characteristics. Selective planting of healthy seedlings in the mechanized transplanting process can effectively avoid the reduction in yield caused by missed planting. Aiming at the current transplanting machinery that cannot achieve the selective planting of healthy seedlings, a healthy seedling intelligent sorting and transplanting system was proposed. The system consisted of a seedling delivery mechanism, sorting mechanism, photoelectric sensor, image sensor, PLC control system, and computer control system. It can realize automatic transmission of seedling trays, automatically identify the information of healthy seedlings in the trays and selectively transplant them. Also it can reduce the missed planting rate caused by the poor quality of plug seedlings after planting and the lack of seedlings in the hole. A sorting test of plug seedlings was carried out for the age-appropriate pepper plug seedlings cultivated in the factory. The results showed that the system had an average recognition accuracy rate of 89.14% and an average sorting success rate of 93.20% in the process of sorting suitable age pepper plug seedlings. The whole system can identify, sort and transplant the plug seedlings of appropriate age according to healthy information, and effectively avoid missing planting. This research can provide technical support for the intelligent upgrade of transplanting equipment. Keywords: intelligent agriculture, sorting and transplanting system, healthy vegetable seedling, design, experiment, image, sensor DOI: 10.25165/j.ijabe.20211404.6169 Citation: Li M Y, Jin X, Ji J T, Li P G, Du X W. Design and experiment of intelligent sorting and transplanting system for healthy vegetable seedlings. Int J Agric & Biol Eng, 2021; 14(4): 208–216.References
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[2] Jin X, Zhao K X, Ji J T, Du X W, Ma H, Qiu Z M. Design and implementation of intelligent transplanting system based on photoelectric sensor and PLC. Future Generation Computer Systems, 2018; 88: 127–139.
[3] Pei D M, Meng F J, Wang H L. Research progress of visual inspection of tray seedling and the system of automatic transplanting. International Journal of Multimedia and Ubiquitous Engineering, 2016; 11(7): 57–68.
[4] Jin X, Yuan Y W, Ji J T, Zhao K X, Li M Y, Chen K K. Design and implementation of anti-leakage planting system for transplanting machine based on fuzzy information. Computers and Electronics in Agriculture, 2020; 169: 105204. doi: 10.1016/j.compag.2019.105204.
[5] Wang Y W, He Z L, Wang J, Wu C Y, Yu G H, Tang Y H. Experiment on transplanting performance of automatic vegetable pot seedling transplanter for dry land. Transactions of the CSAE, 2018; 34(3): 19–25. (in Chinese)
[6] Jin X, Chen K K, Yang Z, Ji J T, Pang J. Simulation of hydraulic transplanting robot control system based on fuzzy PID controller. Measurement, 2020; 164: 108023.
[7] Yang Y, Chu Q, Yang Y L, Zhang X J, Xu X P, Gu S. Online grading method for tissue culture seedlings of Spathiphyllum floribundum based on machine vision. Transactions of the CSAE, 2016; 32(8): 33–40. (in Chinese)
[8] Wang Y W, Xiao X Z, Liang X F, Wang J, Wu C Y, Chen J K. Plug hole positioning and seedling shortage detecting system on automatic seedling supplementing test-bed for vegetable plug seedlings. Transactions of the CSAE, 2018; 34(12): 35–41. (in Chinese)
[9] Ji J T, Sun J W, Jin X, Li M Y, Du X W. Development of a PVDF sensor for potted seedling clamping force of vegetable transplanting. Int J Agric & Biol Eng, 2019; 12(5): 111–118.
[10] Ji L, Lie T. Developing a low-cost 3D plant morphological traits characterization system. Computers and Electronics in Agriculture, 2017; 143: 1–13.
[11] Ren L, Wang N, Cao W B, Li J Q, Ye X C. Fuzzy PID control of manipulator positioning for taking the whole row seedlings of tomato plug seedlings. Transactions of the CSAE, 2020; 36(8): 21–30. (in Chinese)
[12] Wu J M, Zhang X C, Jin X, Liu Z J, Zhu L C, Sun X, et al. Design and experiment on transplanter pot seedling disk conveying and positioning control system. Transactions of the CSAE, 2015; 31(1): 47–52.
[13] Prasanna Kumar G V, Raheman H. Automatic feeding mechanism of a vegetable transplanter. Int J Agric & Biol Eng, 2012; 5(2): 20–27.
[14] Feng Q C, Zhao C J, Jiang K, Fan P F, Wang X. Design and test of tray-seedling sorting transplanter. Int J Agric & Biol Eng, 2015; 8(2): 14–20.
[15] Tong J H, Shi H F, Wu C Y, Jiang H Y, Yang T W. Skewness correction and quality evaluation of plug seedling images based on Canny operator and Hough transform. Computers and Electronics in Agriculture, 2018; 155: 461–472.
[16] Ge L Z, Yang Z L, Sun Z, Zhang G, Zhang M, Zhang K F, et al. A method for broccoli seedling recognition in natural environment based on binocular stereo vision and Gaussian mixture model. Sensors, 2019; 19(5): 1132. doi: 10.3390/s19051132.
[17] Khadatkar A, Mathur S M, Gaikwad B. Automation in transplanting: a smart way of vegetable cultivation. Current Science, 2018; 115(10): 1884–1892.
[18] Zhang S W, Huang W Z, Zhang C L. Three-channel convolutional neural networks for vegetable leaf disease recognition. Cognitive Systems Research, 2019; 53: 31–41.
[19] Zhang L N, Tan Y, Lyu H T, Li B S, Jiang Y Y, Wang S. Optimization of automatic transplanting path for plug seedlings in greenhouse. Transactions of the CSAE, 2020; 36(15): 65–72. (in Chinese)
[20] Rahul K, Raheman H, Paradkar V. Design of a 4 DOF parallel robot arm and the firmware implementation on embedded system to transplant pot seedlings. Artificial Intelligence in Agriculture, 2020; 4: 172–183.
[21] Khadatkar A, Mathur S M; Gaikwad B. Automation in transplanting: A smart way of vegetable cultivation. Current Science, 2018; 115(10): 1884–1892.
[22] Samiei S, Rasti P, Ly Vu J, Buitink J, Rousseau D. Deep learning-based detection of seedling development. Plant Methods, 2020; 16(1): 1–11.
[23] Fabiyi S D, Vu H, Tachtatzis C, Murray P, Harle D, Dao T K, et al. Varietal classification of rice seeds using RGB and hyperspectral images. IEEE Access, 2020; 8: 22493–22505.
[24] Liu L Q, Xiang J T, Wu Z Q. Research of health seedlings recognition method using color features. Agricultural Science & Technology and Equipment, 2012; 216(6): 26–28.
[25] Jiang H Y, Shi J H, Ren Y, Ying Y B. Application of machine vision on automatic seedling transplanting. Transactions of the CSAE, 2009; 25(5): 127–131. (in Chinese)
[26] Yang Z Y, Zhang W Q, Li W, Chen Y, Song P. Information acquisition method of potted-seedling transplanting fitness using monocular vision. Transactions of the CSAE, 2014; 30(3): 112–119. (in Chinese)
[27] Sun J, He X F, Tan W J, Wu X H, Shen J F, Lu H. Recognition of crop seedling and weed recognition based on dilated convolution and global pooling in CNN. Transactions of the CSAE, 2018, 34(11): 159–165. (in Chinese)
[28] Zhang W Q, Li W, Yang Z Y, Han J D. Height information acquisition method of seedling with machine vision. In: 2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), Shenyang, China: IEEE, 2015; pp.1446–1449. doi: 10.1109/CYBER.2015.7288157.
[29] Huang Y J, Lee F F. Classification of Phalaenopsis plantlet parts and identification of suitable grasping point for automatic transplanting using machine vision. Applied Engineering in Agriculture, 2008; 24(1): 89–99.
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Published
2021-07-31
How to Cite
Li, M., Jin, X., Ji, J., Li, P., & Du, X. (2021). Design and experiment of intelligent sorting and transplanting system for healthy vegetable seedlings. International Journal of Agricultural and Biological Engineering, 14(4), 208–216. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/6169
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Information Technology, Sensors and Control Systems
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