To solve the picking problem of famous green tea, a new technology was proposed to classify machine picked tea in this paper. Fresh tea leaves plucked by machine are often mixed, with tea stalks and have a high broken rate and thereby only suitable for making general green tea. The convex hull area, convex hull perimeter, long axial length, short axial length and other morphological features of machine plucked tea leaves were analyzed by Labview vision, image processing technology and neural network to screen high quality tea. Industrial CCD camera with appropriate optical system was used to collect object classification features. User interface was developed by Labview, which can realize the data interaction, convenient operation, short development cycle and meet different users’ requirements. Finally, sample test showed that the correct rate of tea classification could reach about 90%, which provides a good theoretical basis for further research of tea grading equipment.
Key words
grading /
image processing technology /
machine plucked tea /
neural network
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
References
[1] 唐小林, 李文萃, 范起业. 机采茶鲜叶分类分级技术及相关设备研究进展[J]. 中国茶叶加工, 2015(2): 5-8.
[2] 杨娟, 李中林, 袁林颖, 等. 机采茶鲜叶分级技术初步研究[J]. 中国茶叶加工, 2015(2): 41-45.
[3] 张兰兰, 董迹芬, 唐萌, 等. 名优茶机采鲜叶分级技术研究[J]. 浙江大学学报: 农业与生命科学版, 2012, 38(5): 593-598.
[4] 李明珠, 倪超, 张晓, 等. 基于彩色线阵CCD的茶叶分选识别算法研究[J]. 中国农机化学报, 2015, 36(4): 124-129.
[5] Zhe T, Yuancheng S, Meng J.A local binary pattern based texture descriptors for classification of tea leaves[J]. Neurocomputing, 2015, 168(30): 1011-1023.
[6] Shahabi M, Rafiee S, Mohtasebi SS, et al.Image analysis and green tea color change kinetics during thin-layer drying[J]. Food Science and Technology International, 2014, 20(6): 465-476.
[7] Gagandeep SG, Amod K, Ravinder A.Monitoring and grading of tea by computer vision[J]. Journal of Food Engineering, 2011(106): 13-19.
[8] Laddi A, Sharma S, Kumar A,et al.Classification of tea grains based upon image texture feature analysis under different illumination conditions[J]. Journal of Food Engineering, 2013, 115(2): 226-231.
[9] 张春燕, 陈笋, 张俊峰. 基于最小风险贝叶斯分类器茶叶茶梗分类[J]. 计算机工程与应用, 2012, 48(28): 187-192.
[10] 常春, 陈怡群, 肖宏儒, 等. 基于神经网络图像分析的智能鲜茶叶分选机[J]. 中国农机化学报, 2013, 34(1): 137-141.
[11] 赵吉文, 高尚, 魏正翠, 等. 基于FPGA的西瓜子机器视觉色选系统[J]. 农业机械学报, 2011, 42(8): 173-177.
[12] 郑华军, 张宪, 乔欣. 蚁群聚类分析算法在茶叶等级分类识别中的应用[J]. 轻工机械, 2011, 25(9): 91-93.
[13] 施健, 何建国, 张冬, 等. 基于计算机视觉的鲜枣大小分级系统研究[J]. 食品与机械, 2013, 29(5): 134-137.
[14] 孔彦龙, 高晓阳, 李红玲, 等. 基于机器视觉的马铃薯质量和形状分选方法[J]. 农业工程学报, 2012, 28(17): 143-148.
[15] Kurtulmus F, Alibas I, Kavdir I.Classification of pepper seeds using machine vision based on neural network[J]. Int J Agric and Biol Eng, 2016, 9(1): 51-62.
PDF(1133 KB)
浙公网安备 33019902000101号 
