基于图像特征识别的马铃薯薯皮粗糙度分级研究

doi:10.3864/j.issn.0578-1752.2023.22.006

Abstract

Abstract:

【Objective】The classification analysis of potato tuber skin roughness could provide the non-destructive testing methods for tuber appearance quality traits, which would establish the theoretical and practical base for the objective evaluation of tuber quality and high-throughput screening varieties.【Method】Seventy-nine potato varieties (lines) were selected as materials, and the images of tuber skin with and without bud-eyes were taken by camera. The tuber skin images were preprocessed using MATLAB R2016a software. Eight materials were randomly selected to compare the effect of image graying, enhancement and denoising using the correlation function indicators. The image characteristic parameters, angular second moment (ASM), entropy (ENT), contrast (CON) and correlation (COR) were extracted using the gray level co-occurrence matrix (GLCM), and the suitable distance (d) of GLCM were determined. The differences in two types of tuber skin image feature parameters were compared, and the set of tuber skin image features with less difference was selected for statistical analysis and classification recognition. The support vector machine (SVM) and backpropagation neural network (BPNN) models were constructed for tuber skin roughness classification, and the evaluation indexes of model grading accuracy were accuracy, precision, recall and harmonic mean, respectively. 【Result】The texture structure of tuber skin image after grayscale processing using the weighted average method was clear, and the evaluation value of image clarity was 2.5698±0.5959, which was significantly higher than that of the mean method (1.8035±0.4856) and the maximum method (1.0535±0.4088). The gray scale range of tuber skin image after histogram equalization enhancement was expanded from 100-200 to 0-200, which made the gray distribution wider. The salt noise denoising effect of tuber skin images using the median filter under 3×3 sliding windows was obvious, and the peak signal-to-noise ratio (PSNR) was maximum (28.6250±3.9784 Bp), which was significantly higher than that under 3×3 and 5×5 windows. Two types of tuber skin image feature parameters extracted by GLCM (d=4) were significantly different, and the set of tuber skin image features (without bud-eyes) with less difference was selected for statistical analysis and classification recognition. The results indicated that the variation coefficient of these parameters was varied significantly. The variation coefficient of contrast was the largest (0.40), followed by the angular second moment (0.24) and correlation (0.23), and the variation coefficient of entropy was the smallest (0.18). Using the feature set as the input variable of tuber skin classification model, the overall classification performance of SVM was higher than BP neural network, and the accuracy reached 87.5%. Especially, the prediction accuracy and recognizability of SVM for smooth and heavy hemp skins was the highest. The accuracy reached 100%, the recall reached 85.7% and 100%, and the harmonic mean reached 100% and 92.3%, respectively. 【Conclusion】The combination of the image processing techniques presented in this study and the GLCM extracted texture feature parameters could effectively characterize potato tuber skin roughness variations. The tuber skin roughness grading based on machine vision could be achieved by constructing SVM classification model, and the accuracy reached 87.5%.

Key words: Solanum tuberosum, tuber skin roughness, image characteristic, machine vision, support vector machine

TANG ZhenSan, YUAN JianLong, KANG LiangHe, CHENG LiXiang, LÜ Tai, YANG Chen, ZHANG Feng. Potato Tuber Skin Roughness Classification Analysis Based on Image Characteristics Recognition[J].Scientia Agricultura Sinica, 2023, 56(22): 4428-4440.

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References 44

[1]	国家市场监督管理总局, 中国国家标准化管理委员会. 植物品种特异性、一致性和稳定性测试指南马铃薯:GB/T 19557.28—2018. 北京: 中国标准出版社, 2018.
	State Administration for Market Regulation; Standardization Administration of the People's Republic of China. Guidelines for the conduct of tests for distinctness, uniformity and stability-Potato (Solanum tuberosum L.): GB/T 19557.28-2018. Beijing: Standard Press of China, 2018. (in Chinese)
[2]	韩仲志, 赵友刚. 利用花生荚果图像特征识别品种与检验种子. 作物学报, 2012, 38(3): 535-540.
	HAN Z Z, ZHAO Y G. Variety identification and seed test by peanut pod image characteristics. Acta Agronomica Sinica, 2012, 38(3): 535-540. (in Chinese) doi: 10.3724/SP.J.1006.2012.00535
[3]	王红军, 熊俊涛, 黎邹邹, 邓建猛, 邹湘军. 基于机器视觉图像特征参数的马铃薯质量和形状分级方法. 农业工程学报, 2016, 32(8): 272-277.
	WANG H J, XIONG J T, LI Z Z, DENG J M, ZOU X J. Potato grading method of weight and shape based on imaging characteristics parameters in machine vision system. Transactions of the Chinese Society of Agricultural Engineering, 2016, 32(8): 272-277. (in Chinese)
[4]	姜宏, 于永波, 章翔峰, 陈宇彤. 马铃薯外部品质分级方法综述. 科学技术与工程, 2022, 22(14): 5519-5527.
	JIANG H, YU Y B, ZHANG X F, CHEN Y T. Potato external quality grading methods: a review. Science Technology and Engineering, 2022, 22(14): 5519-5527. (in Chinese)
[5]	周竹, 黄懿, 李小昱, 文东东, 汪成龙, 陶海龙. 基于机器视觉的马铃薯自动分级方法. 农业工程学报, 2012, 28(7): 178-183.
	ZHOU Z, HUANG Y, LI X Y, WEN D D, WANG C L, TAO H L. Automatic detecting and grading method of potatoes based on machine vision. Transactions of the Chinese Society of Agricultural Engineering, 2012, 28(7): 178-183. (in Chinese)
[6]	刘丽, 匡纲要. 图像纹理特征提取方法综述. 中国图象图形学报, 2009, 14(4): 622-635.
	LIU L, KUANG G Y. Overview of image textural feature extraction methods. Journal of Image and Graphics, 2009, 14(4): 622-635. (in Chinese)
[7]	展慧, 李小昱, 王为, 汪成龙, 周竹, 黄懿. 基于机器视觉的板栗分级检测方法. 农业工程学报, 2010, 26(4): 327-331.
	ZHAN H, LI X Y, WANG W, WANG C L, ZHOU Z, HUANG Y. Determination of chestnuts grading based on machine vision. Transactions of the Chinese Society of Agricultural Engineering, 2010, 26(4): 327-331. (in Chinese)
[8]	薄华, 马缚龙, 焦李成. 图像纹理的灰度共生矩阵计算问题的分析. 电子学报, 2006, 34(1): 155-158, 134.
	BO H, MA F L, JIAO L C. Research on computation of GLCM of image texture. Acta Electronica Sinica, 2006, 34(1): 155-158, 134. (in Chinese)
[9]	SINGH K R, CHAUDHURY S. Comparative analysis of texture feature extraction techniques for rice grain classification. IET Image Processing, 2020, 14(11): 2532-2540. doi: 10.1049/ipr2.v14.11
[10]	LADDI A, SHARMA S, KUMAR A, KAPUR P. Classification of tea grains based upon image texture feature analysis under different illumination conditions. Journal of Food Engineering, 2013, 115(2): 226-231. doi: 10.1016/j.jfoodeng.2012.10.018
[11]	SHAFI U, MUMTAZ R, HAQ I U, HAFEEZ M, IQBAL N, SHAUKAT A, ZAIDI S M H, MAHMOOD Z. Wheat yellow rust disease infection type classification using texture features. Sensors, 2021, 22(1): 146. doi: 10.3390/s22010146
[12]	张超, 乔敏, 刘哲, 金虹杉, 宁明宇, 孙海艳. 基于无人机和卫星遥感影像的制种玉米田识别纹理特征尺度优选. 农业工程学报, 2017, 33(17): 98-104.
	ZHANG C, QIAO M, LIU Z, JIN H S, NING M Y, SUN H Y. Texture scale analysis and identification of seed maize fields based on UAV and satellite remote sensing images. Transactions of the Chinese Society of Agricultural Engineering, 2017, 33(17): 98-104. (in Chinese)
[13]	谢元澄, 徐焕良, 谢庄. 基于牛肉大理石花纹标准(BMS)图像的纹理特征分析. 中国农业科学, 2010, 43(24): 5121-5128. doi: 10.3864/j.issn.0578-1752.2010.24.016.
	XIE Y C, XU H L, XIE Z. Analysis of texture features based on beef marbling standards (BMS) images. Scientia Agricultura Sinica, 2010, 43(24): 5121-5128. doi: 10.3864/j.issn.0578-1752.2010.24.016. (in Chinese)
[14]	高程程, 惠晓威. 基于灰度共生矩阵的纹理特征提取. 计算机系统应用, 2010, 19(6): 195-198.
	GAO C C, HUI X W. GLCM-based texture feature extraction. Computer Systems & Applications, 2010, 19(6): 195-198. (in Chinese)
[15]	刘涛, 仲晓春, 孙成明, 郭文善, 陈瑛瑛, 孙娟. 基于计算机视觉的水稻叶部病害识别研究. 中国农业科学, 2014, 47(4): 664-674. doi: 10.3864/j.issn.0578-1752.2014.04.006.
	LIU T, ZHONG X C, SUN C M, GUO W S, CHEN Y Y, SUN J. Recognition of rice leaf diseases based on computer vision. Scientia Agricultura Sinica, 2014, 47(4): 664-674. doi: 10.3864/j.issn.0578-1752.2014.04.006. (in Chinese)
[16]	党满意, 孟庆魁, 谷芳, 顾彪, 胡耀华. 基于机器视觉的马铃薯晚疫病快速识别. 农业工程学报, 2020, 36(2): 193-200.
	DANG M Y, MENG Q K, GU F, GU B, HU Y H. Rapid recognition of potato late blight based on machine vision. Transactions of the Chinese Society of Agricultural Engineering, 2020, 36(2): 193-200. (in Chinese)
[17]	GOMES J F S, LETA F R. Applications of computer vision techniques in the agriculture and food industry: A review. European Food Research and Technology, 2012, 235(6): 989-1000. doi: 10.1007/s00217-012-1844-2
[18]	GUZMÁN E, BAETEN V, PIERNA J A F, GARCÍA-MESA J A. Determination of the olive maturity index of intact fruits using image analysis. Journal of Food Science and Technology, 2015, 52(3): 1462-1470. doi: 10.1007/s13197-013-1123-7 pmid: 25745214
[19]	WANG A C, ZHANG W, WEI X H. A review on weed detection using ground-based machine vision and image processing techniques. Computers and Electronics in Agriculture, 2019, 158(2019): 226-240. doi: 10.1016/j.compag.2019.02.005
[20]	LI B, XU X M, ZHANG L, HAN J W, BIAN C S, LI G C, LIU J G, JIN L P. Above-ground biomass estimation and yield prediction in potato by using UAV-based RGB and hyperspectral imaging. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 162: 161-172. doi: 10.1016/j.isprsjprs.2020.02.013
[21]	RAZMJOOY N, MOUSAVI B S, SOLEYMANI F. A real-time mathematical computer method for potato inspection using machine vision. Computers & Mathematics with Applications, 2012, 63(1): 268-279. doi: 10.1016/j.camwa.2011.11.019
[22]	纪娜, 何国荣. 基于灰度识别的猕猴桃形状疤痕图像纹理特征提取方法. 自动化与仪器仪表, 2019(1): 159-162.
	JI N, HE G R. Texture feature extraction method of kiwifruit shape scar image based on gray level recognition. Automation & Instrumentation, 2019(1): 159-162. (in Chinese)
[23]	李祚林, 李晓辉, 马灵玲, 胡玥, 唐伶俐. 面向无参考图像的清晰度评价方法研究. 遥感技术与应用, 2011, 26(2): 239-246.
	LI Z L, LI X H, MA L L, HU Y, TANG L L. Research of definition assessment based on no-reference digital image quality. Remote Sensing Technology and Application, 2011, 26(2): 239-246. (in Chinese)
[24]	宋凤菲. 彩色图像灰度化及其效果的客观评价方法研究[D]. 泉州: 华侨大学, 2014.
	SONG F F. Research on objective evaluation method of color image graying and its effect[D]. Quanzhou: Huaqiao University, 2014. (in Chinese)
[25]	杨卫中, 徐银丽, 乔曦, 饶伟, 李道亮, 李振波. 基于对比度受限直方图均衡化的水下海参图像增强方法. 农业工程学报, 2016, 32(6): 197-203.
	YANG W Z, XU Y L, QIAO X, RAO W, LI D L, LI Z B. Method for image intensification of underwater sea cucumber based on contrast-limited adaptive histogram equalization. Transactions of the Chinese Society of Agricultural Engineering, 2016, 32(6): 197-203. (in Chinese)
[26]	孙宏琦, 施维颖, 巨永锋. 利用中值滤波进行图像处理. 长安大学学报(自然科学版), 2003, 23(2): 104-106.
	SUN H Q, SHI W Y, JU Y F. Image processing with medium value filter. Journal of Chang’an University (Natural Science Edition), 2003, 23(2): 104-106. (in Chinese)
[27]	刘祝华. 图像去噪方法的研究[D]. 南昌: 江西师范大学, 2005.
	LIU Z H. Research on image denoising method[D]. Nanchang: Jiangxi Normal University, 2005. (in Chinese)
[28]	DASS A K. Improvising MSN and PSNR for finger-print image noised by GAUSSIAN and SALT & PEPPER. The International Journal of Multimedia & Its Applications, 2012, 4(4): 59-72.
[29]	HARALICK R M, SHANMUGAM K, DINSTEIN I. Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, 1973, SMC-3(6): 610-621. doi: 10.1109/TSMC.1973.4309314
[30]	侯群群, 王飞, 严丽. 基于灰度共生矩阵的彩色遥感图像纹理特征提取. 自然资源遥感, 2013, 25(4): 26-32.
	HOU Q Q, WANG F, YAN L. Extraction of color image texture feature based on gray-level co-occurrence matrix. Remote Sensing for Natural Resources, 2013, 25(4): 26-32. (in Chinese)
[31]	蔡苇荻, 张羽, 刘海燕, 郑恒彪, 程涛, 田永超, 朱艳, 曹卫星, 姚霞. 基于成像高光谱的小麦冠层白粉病早期监测方法. 中国农业科学, 2022, 55(6): 1110-1126. doi: 10.3864/j.issn.0578-1752.2022.06.005.
	CAI W D, ZHANG Y, LIU H Y, ZHENG H B, CHENG T, TIAN Y C, ZHU Y, CAO W X, YAO X. Early detection on wheat canopy powdery mildew with hyperspectral imaging. Scientia Agricultura Sinica, 2022, 55(6): 1110-1126. doi: 10.3864/j.issn.0578-1752.2022.06.005. (in Chinese)
[32]	李智峰, 朱谷昌, 董泰锋. 基于灰度共生矩阵的图像纹理特征地物分类应用. 地质与勘探, 2011, 47(3): 456-461.
	LI Z F, ZHU G C, DONG T F. Application of GLCM-based texture features to remote sensing image classification. Geology and Exploration, 2011, 47(3): 456-461. (in Chinese)
[33]	郑冠楠, 谭豫之, 张俊雄, 李伟. 基于计算机视觉的马铃薯自动检测分级. 农业机械学报, 2009, 40(4): 166-168, 156.
	ZHENG G N, TAN Y Z, ZHANG J X, LI W. Automatic detecting and grading method of potatoes with computer vision. Transactions of the Chinese Society for Agricultural Machinery, 2009, 40(4): 166-168, 156. (in Chinese)
[34]	黄辰, 费继友. 基于图像特征融合的苹果在线分级方法. 农业工程学报, 2017, 33(1): 285-291.
	HUANG C, FEI J Y. Online apple grading based on decision fusion of image features. Transactions of the Chinese Society of Agricultural Engineering, 2017, 33(1): 285-291. (in Chinese)
[35]	魏永康, 杨天聪, 臧少龙, 贺利, 段剑钊, 谢迎新, 王晨阳, 冯伟. 基于无人机多光谱影像特征融合的小麦倒伏监测. 中国农业科学, 2023, 56(9): 1670-1685. doi: 10.3864/j.issn.0578-1752.2023.09.005.
	WEI Y K, YANG T C, ZANG S L, HE L, DUAN J Z, XIE Y X, WANG C Y, FENG W. Monitoring wheat lodging based on UAV multi-spectral image feature fusion. Scientia Agricultura Sinica, 2023, 56(9): 1670-1685. doi: 10.3864/j.issn.0578-1752.2023.09.005. (in Chinese)
[36]	高传新, 殷勇. 数码相机ISO感光度的调整对刑事案件中痕迹检验照相质量的影响. 影像技术, 2008, 21(3): 33-35.
	GAO C X, YIN Y. Influence of digital camera ISO speed setting on the quality of criminological photography for traces detection. Image Technology, 2008, 21(3): 33-35. (in Chinese)
[37]	PHAM L H, TRAN D N N, RHIE C H, JEON J W. Analysis of the smartphone camera exposure effect on laser extraction//2021 International Conference on Electronics, Information, and Communication (ICEIC), 2021.
[38]	唐俊, 邓立苗, 陈辉, 栾涛, 马文杰. 基于机器视觉的玉米叶片透射图像特征识别研究. 中国农业科学, 2014, 47(3): 431-440. doi: 10.3864/j.issn.0578-1752.2014.03.003.
	TANG J, DENG L M, CHEN H, LUAN T, MA W J. Research on maize leaf recognition of characteristics from transmission image based on machine vision. Scientia Agricultura Sinica, 2014, 47(3): 431-440. doi: 10.3864/j.issn.0578-1752.2014.03.003. (in Chinese)
[39]	杨嘉能. 基于直方图均衡的图像增强算法优化研究[D]. 乌鲁木齐: 新疆大学, 2021.
	YANG J N. Optimization of image enhancement algorithm based on histogram equalization[D]. Urumqi: Xinjiang University, 2021. (in Chinese)
[40]	陈永亮. 灰度图像的直方图均衡化处理研究[D]. 合肥: 安徽大学, 2014.
	CHEN Y L. Research on histogram equalization processing of gray image[D]. Hefei: Anhui University, 2014. (in Chinese)
[41]	宁媛, 李皖. 图像去噪的几种方法分析比较. 贵州工业大学学报(自然科学版), 2005, 34(4): 63-66.
	NING Y, LI W. Analysis and comparison of some techniques on image denoising. Journal of Guizhou University of Technology (Natural Science Edition), 2005, 34(4): 63-66. (in Chinese)
[42]	杨光义. 图像质量评价及其在图像去噪中的应用研究[D]. 武汉: 武汉大学, 2018.
	YANG G Y. Image quality evaluation and its application in image denoising[D]. Wuhan: Wuhan University, 2018. (in Chinese)
[43]	苑丽红, 付丽, 杨勇, 苗静. 灰度共生矩阵提取纹理特征的实验结果分析. 计算机应用, 2009, 29(4): 1018-1021.
	YUAN L H, FU L, YANG Y, MIAO J. Analysis of texture feature extracted by gray level co-occurrence matrix. Journal of Computer Applications, 2009, 29(4): 1018-1021. (in Chinese) doi: 10.3724/SP.J.1087.2009.01018
[44]	王璨, 李志伟. 利用融合高度与单目图像特征的支持向量机模型识别杂草. 农业工程学报, 2016, 32(15): 165-174.
	WANG C, LI Z W. Weed recognition using SVM model with fusion height and monocular image features. Transactions of the Chinese Society of Agricultural Engineering, 2016, 32(15): 165-174. (in Chinese)

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品系编号 Variety code	CIP编号 CIP entry	品系编号 Variety code	CIP编号 CIP entry	品系编号 Variety code
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类别Category	A	B	C	D
重麻皮 Heavy hemp skin
麻皮 Hemp skin
略麻皮 Slightly hemp skin
光滑皮 Smooth skin

类别 Category	样本数 Number of samples	类平均标准差 Class mean standard deviation
A	8	1.8035±0.4856c	C-A	0.7663
B	8	1.0535±0.4088b	C-B	1.5163
C	8	2.5698±0.5959a	C-C	0

类别 Category	A	B	C	D
重麻皮 Heavy hemp skin
麻皮 Hemp skin
略麻皮 Slightly hemp skin
光滑皮 Smooth skin

类别 Category	原始图像 Original images	3×3窗口 3×3 Window		5×5窗口 5×5 Window
类别 Category	原始图像 Original images	高斯噪声 Gaussian noise	椒盐噪声 Salt and pepper noise	高斯噪声 Gaussian noise	椒盐噪声 Salt and pepper noise
A
B
C
D

Potato Tuber Skin Roughness Classification Analysis Based on Image Characteristics Recognition

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类别 Category	样本数 Number of samples	类平均标准差 Class mean standard deviation
A	8	28.6250±3.9784a	A-A	0
B	8	23.5650±2.1651a	A-B	5.0600
C	8	26.7350±2.1425b	A-C	1.8900
D	8	21.5675±1.6186b	A-D	7.0575

类别 Category	平均值 Average	标准差 Standard deviation	最小值 Min value	最大值 Max value	变异系数 Coefficient of variation
角二阶矩 ASM	0.33	0.08	0.17	0.51	0.24
熵 ENT	1.55	0.22	1.03	2.29	0.18
对比度 CON	0.18	0.07	0.06	0.39	0.40
相关度 COR	1.65	0.38	0.76	2.41	0.23

类别 Category	测试集 Test set	支持向量机Support vector machine				BP神经网络 BP neural network
类别 Category	测试集 Test set	准确率 Accuracy (%)	精准率 Precision (%)	召回率 Recall (%)	F1 (%)	准确率 Accuracy (%)	精准率 Precision (%)	召回率 Recall (%)	F1 (%)
重麻皮 Heavy hemp skin	24	87.5	100	100	100	58.3	-	-	-
麻皮 Hemp skin	24		100	77.8	87.6		50	57.2	53.4
略麻皮 Slightly hemp skin	24		70	100	82.4		60	66.6	63.3
光滑皮 Smooth skin	24		100	85.7	92.3		67.7	57.1	62.4

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