Real-time monitoring of optimum timing for harvesting fresh tea leaves based on machine vision
Keywords:
agricultural machinery, fresh tea leaves, machine vision, intelligent recognition, real-time monitoringAbstract
The harvesting time of fresh tea leaves has a significant impact on product yield and quality. The aim of this study was to propose a method for real-time monitoring of the optimum harvesting time for picking fresh tea leaves based on machine vision. Firstly, the shapes of fresh tea leaves were distinguished from RGB images of the tea-tree canopy after graying with the improved B-G algorithm, filtering with a median filter algorithm, binary processing with the Otsu algorithm, and noise reduction and edge smoothing using open and close operations. Then the leaf characteristics, such as leaf area index, average length, and leaf identification index, were calculated. Based on these, the Bayesian discriminant principle and method were used to construct a discriminant model for fresh tea-leaf collection status. When this method was applied to a RGB tea-tree canopy image acquired at 45° shooting angle, the fresh tea-leaf recognition rate was 90.3%, and the accuracy for fresh tea-leaf harvesting status was 98% by cross validation. Hence, this method provides the basic conditions for future tea-plantation operation and management using information technology, automation, and intelligent systems. Keywords: agricultural machinery, fresh tea leaves, machine vision, intelligent recognition, real-time monitoring DOI: 10.25165/j.ijabe.20191201.3418 Citation: Zhang L, Zhang H D, Chen Y D, Dai S H, Li X M, Imou K, Liu Z H, et al. Real-time monitoring of optimum timing for harvesting fresh tea leaves based on machine vision. Int J Agric & Biol Eng, 2019; 12(1): 6–9.References
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[2] Borah S, Bhuyan M. Quality indexing by machine vision during fermentation in black tea. Proceedings of the SPIE International Society of Optical Engineering, 2003; pp.468–475.
[3] Ouyang, Q, Chen Q S, Zhao J W, Cai J R. Automated tea quality classification by hyperspectral imaging. Applied Optics, 2009; 48(19): 3557–3564.
[4] Liu Y, Xie H, Wang L G, Tan K Z. Hyperspectral band selection based on a variable precision neighborhood rough set. Applied Optics, 2016; 55(3): 462–472.
[5] Lin G, Yan J. Preliminary research on quantification of tea leaf appearance. Journal of Tea Science, 1994; 14(1):75–78.
[6] Wang J, Du S P. Identification investigation of tea based on HSI color space and figure. Journal of Tea Science, 2008; 28(6): 420–424
[7] Ji S M, Xiong S C, Wang L X, Xi J Z. Technique for on-Line tea stalk distinction and its application. Transactions of the CSAM, 1995; 26(1): 56–60. (in Chinese)
[8] Wang J. Segmentation algorithm of tea combined with the color and region growing. Journal of Tea Science, 2011; 31(1): 72–77.
[9] Wei J J, Chen Y, Jin X J, Zheng J Q, Shi Y Z, Zhang H. Researches on tender tea shoots identification under natural conditions. Journal of Tea Science, 2012; 32(5): 377–381.
[10] Wu X M, Zhang F G, Lv J T. Research on recognition of tea tender leaf based on image color information. Journal of Tea Science, 2013; 33(6): 584–589.
[11] Hu Y G, Liu S Z, Wu W Y, Wang J Z, Shen J W. Optimal flight parameters of unmanned helicopter for tea plantation frost protection. Int J Agric & Biol Eng, 2015; 8(5): 50–57.
[12] Tang Z, Su Y C, Er M J, Qi F, Zhang L, Zhou J Y. A local binary pattern based texture descriptors for classification of tea leaves. Neurocomputing, 2015; 168: 1011–1023.
[13] Hu Y G, Lu Y Z, Lu J. Comparative proteomics analysis of tea leaves exposed to subzero temperature: molecular mechanism of freeze injury. Int J Agric & Biol Eng, 2013; 6(4): 27–34.
[14] Lu Y Z, Hu Y G, Zhang X L, Li P P. Responses of electrical properties of tea leaves to low-temperature stress. Int J Agric & Biol Eng, 2015; 8(5): 170–175.
[15] Cai J R, Fang R M, Zhang S Q, Wu S Y. Application of computer vision technique to research on classifying system of tobacco leaves. Transactions of the CSAE, 2000; 16(3): 118–122. (in Chinese)
[16] Sun D-W, ed. Computer vision technology for food quality evaluation. Academic Press, 2016.
[17] Benalia S, Cubero S, Prats-Montalbán J M, Bernardi B, Zimbalatti G, Blasco J. Computer vision for automatic quality inspection of dried figs (Ficus carica L.) in real-time. Computers and Electronics in Agriculture, 2016; 120: 17–25.
[18] Wen K X, Xie Z M, Yang L M, Sun B Q, Wang J H, Sun Q. Computer vision technology determines optimal physical parameters for sorting Jindan 73 maize seeds. Seed Science and Technology, 2015; 43(1): 62–70.
[19] Arco J E, Górriz J M, Ramírez J, Álvarez I, Puntonet C G. Digital image analysis for automatic enumeration of malaria parasites using morphological operations. Expert Systems with Applications, 2015; 42(6): 3041–3047.
[20] Kamarudin N S, Makhtar M, Fadzli S A, Mohamad M, Mohamad F S. Comparison of image classification techniques using CALTECH 101 dataset. Journal of Theoretical & Applied Information Technology, 2015; 71(1): 79–86.
[21] Bora D J, Gupta A K. A new efficient color image segmentation approach based on combination of histogram equalization with watershed algorithm. Int. J. Comput. Eng, 2016; 4(6): 156–167.
[22] Hu L, Luo X W, Zeng S, Zhang Z G, Chen X F, Lin C X. Plant recognition and localization for intra-row mechanical weeding device based on machine vision. Transactions of the CSAE, 2013; 29(10): 12–18. (in Chinese)
[23] Li X L, He Y. Classification of tea grades by multispectral images and combined feature. Transaction of the CSAM, 2009; 40(Z): 119–118. (in Chinese)
[24] Tang Y P, Han W M, He A G, Wang W Y. Design and experiment of intelligentized tea-plucking machine for human riding based on machine vision. Transaction of the CSAM, 2016; 47(7): 15–20. (in Chinese)
[25] Chen Q S, Zhao J W, Cai J R, Wang X Y. Application of support vector machine in machine vision recognition of tea. Chinese Journal of Scientific Instrument, 2006; 27(12): 1704–1706. (in Chinese)
[26] Bishop C M. Pattern Recognition and Machine Learning (Information Science and Statistics). Springer-Verlag Inc., New York, 2006.
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Published
2019-02-01
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Zhang, L., Zhang, H., Chen, Y., Dai, S., Li, X., Kenji, I., … Li, M. (2019). Real-time monitoring of optimum timing for harvesting fresh tea leaves based on machine vision. International Journal of Agricultural and Biological Engineering, 12(1), 6–9. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/3418
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Applied Science, Engineering and Technology
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