Design of fruit grading system based on machine vision

Authors

LIU Jia-hao, College of Automation, Qingdao University, Qingdao, Shandong 266071, China;Shandong Provincial Key Laboratory of Industrial Control Technology, Qingdao, Shandong 266071, China
GAO Jun-wei, College of Automation, Qingdao University, Qingdao, Shandong 266071, China;Shandong Provincial Key Laboratory of Industrial Control Technology, Qingdao, Shandong 266071, ChinaFollow
ZHANG Bing-xing, College of Automation, Qingdao University, Qingdao, Shandong 266071, China;Shandong Provincial Key Laboratory of Industrial Control Technology, Qingdao, Shandong 266071, China
WANG Jian-chong, College of Automation, Qingdao University, Qingdao, Shandong 266071, China;Shandong Provincial Key Laboratory of Industrial Control Technology, Qingdao, Shandong 266071, China

Corresponding Author(s)

高军伟（1972—），男，青岛大学教授，博士。E-mail：qdgao163@163.com

Abstract

Objective: In order to solve the problems of low efficiency and high false detection rate of current fruit classification detection methods, a fruit classification system based on machine vision was designed with apples as the sorting object. Methods: The apple image collected in real time was preprocessed, the improved Canny edge detection algorithm was used to extract the edge, and the radius of the apple cross-section was obtained by fitting the edge coordinates with the minimum peripheral circle method. The acquired RGB image was converted to HSI image, and the proportion of red region is calculated according to the range of H component to determine the color of apple. Count the number of pixels in the area, and calculate the area and circumference of the apple respectively to calculate the roundness of the apple. Combined with three characteristic values of diameter length, color and roundness, apple was graded comprehensively. Results: Through the test of 50 apple samples, the error range of fruit diameter measured by the fruit grading system and manual sorting was within ±1.5 mm, the color characteristics of the samples were consistent with the actual appearance of the apple, and the result size of the roundness value was consistent with the actual shape. Conclusion: The system can meet the demand of apple classification in actual production and help to realize the accurate identification of apple grade.

Publication Date

10-20-2023

First Page

112

Last Page

118

DOI

10.13652/j.spjx.1003.5788.2022.80967

Recommended Citation

Jia-hao, LIU; Jun-wei, GAO; Bing-xing, ZHANG; and Jian-chong, WANG (2023) "Design of fruit grading system based on machine vision," Food and Machinery: Vol. 39: Iss. 6, Article 18.
DOI: 10.13652/j.spjx.1003.5788.2022.80967
Available at: https://www.ifoodmm.cn/journal/vol39/iss6/18

References

[1] 李先锋, 朱伟兴, 花小朋, 等. 基于D-S证据理论的决策级多特征融合苹果分级方法[J]. 农业机械学报, 2011, 42（6）: 188-192. LI X F, ZHU W X, HUA X P, et al. Multi-feature decision fusion method based on D-S evidence theory for apple grading[J]. Transactions of the Chinese Society for Agricultural Machinery, 2011, 42（6）: 188-192.
[2] 杨小青, 党宏社. 基于像素点变换法的苹果颜色分级系统研究[J]. 农机化研究, 2012, 34（3）: 203-205, 241. YANG X Q, DANG H S. Research on apple color classification system based on pixel transformation method[J]. Agricultural Mechanization Research, 2012, 34（3）: 203-205, 241.
[3] 于蒙, 李雄, 杨海潮. 基于图像识别的苹果等级分级研究[J]. 自动化与仪表, 2019, 34（7）: 39-43, 47. YU M, LI X, YANG H C. Research on apple grading algorithm based on image recognition[J]. Automation & Instrumentation, 2019, 34（7）: 39-43, 47.
[4] MOALLEM P, SERAJODDIN A, POURGHASSEM H. Computer vision-based apple grading for golden delicious apples based on surface features[J]. Information Processing in Agriculture, 2017, 4（1）: 33-40.
[5] 姚敏, 牟雪儿, 陈鹏, 等. 图像中印章的检测定位与识别研究[J]. 信息技术与信息化, 2018（12）: 148-150. YAO M, MOU X E, CHEN P, et al. Research on seal detection location and recognition in image[J]. Information Technology and Information Technology, 2018（12）: 148-150.
[6] KAMIYAMA M, TAGUCHI A. Color Conversion formula with saturation correction from HSI color space to RGB color space[J]. IEICE Transactions on Fundamentals of Electronics, Communications & computer sciences, 2021, E104.A（7）: 1 000-1 005.
[7] 谷学静, 刘秋月, 马冠征, 等. 基于双边滤波和词袋模型的图像匹配算法[J]. 激光杂志, 2022, 43（7）: 70-74. GU X J, LIU Q Y, MA G Z, et al. Image matching algorithm based on bilateral filtering and bag of words model[J]. Laser Journal, 2022, 43（7）: 70-74.
[8] KAPUR J, SAHOP P, WONG A. A new method for grey-level picture thresholding using the entropy of the histogram[J]. Computer Vision Graphics and Image Processing, 1985（29）: 210-239.
[9] 张伟光, 钟靖涛, 于建新, 等. 基于机器学习和图像处理的路面裂缝检测技术研究[J]. 中南大学学报（自然科学版）, 2021, 52（7）: 2 402-2 415. ZHANG W G, ZHONG J T, YU J X, et al. Research on pavement crack detection technology based on convolution neural network[J]. Journal of Central South University （Science and Technology）, 2021, 52（7）: 2 402-2 415.
[10] 齐艳丽, 汪代俊. 图像边缘检测方法性能比较[J]. 中国标准化, 2022（14）: 141-144. QI Y L, WANG D J. Comparison of image edge detection methods[J]. China Standardization, 2022（14）: 141-144.
[11] 李靖, 王慧, 闫科, 等. 改进Canny算法的图像边缘增强方法[J]. 测绘科学技术学报, 2021, 38（4）: 398-403. LI J, WANG H, YAN K, et al. Improved Canny algorithm for image edge enhancement[J]. Journal of Surveying and Mapping Science and Technology, 2021, 38（4）: 398-403.
[12] 刘新庭, 匡迎春, 陈熵, 等. 基于最小外接圆直径的苹果分级研究[J]. 中国农学通报, 2015, 31（20）: 239-243. LIU X T, KUANG Y C, CHEN S, et al. Research on apple grading based on the minimum circumferential circle diameter[J]. Chinese Agricultural Science Bulletin, 2015, 31（20）: 239-243.
[13] 施健, 何建国, 张冬, 等. 基于计算机视觉鲜枣大小分级系统研究[J]. 食品与机械, 2013, 29（5）: 134-137. SHI J, HE J G, ZHANG D, et al. Study on jujube size-grading system based on computer vision[J]. Food & Machinery, 2013, 29（5）: 134-137.
[14] 刘忠超, 范灵燕, 盖晓华. 基于机器视觉的苹果重量检测研究[J]. 江苏农业科学, 2021, 49（21）: 201-205. LIU Z C, FAN L Y, GAI X H. Research on apple weight detection based on machine vision[J]. Jiangsu Agricultural Sciences, 2021, 49（21）: 201-205.
[15] 谢忠红, 郭小清, 姬长英. 基于颜色模型和纹理特征的彩色水果图像分割方法[J]. 计算机工程与科学, 2010, 32（1）: 64-66, 100. XIE Z H, GUO X Q, JI C Y. A method of color fruit image segmentation based on the color model and the texture feature[J]. Computer Engineering and Science, 2010, 32（1）: 64-66, 100.
[16] 刘亚东. 基于图像处理的苹果外观品质分级研究[D]. 北京: 中国地质大学（北京）, 2021: 23. LIU Y D. Research on apple appearance quality grading based on image processing[D]. Beijing: China University of Geosciences （Beijing）, 2021: 23.