孙宝霞,汤林越,何志良,邹湘军,熊俊涛.基于机器视觉的采后荔枝表皮微损伤实时检测[J].农业机械学报,2016,47(7):35-41.
Sun Baoxia,Tang Linyue,He Zhiliang,Zou Xiangjun,Xiong Juntao.Real-time Detection of Micro-damage on Peel of Postharvest Litchi Based on Machine Vision[J].Transactions of the Chinese Society for Agricultural Machinery,2016,47(7):35-41.
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基于机器视觉的采后荔枝表皮微损伤实时检测   [下载全文]
Real-time Detection of Micro-damage on Peel of Postharvest Litchi Based on Machine Vision   [Download Pdf][in English]
投稿时间:2016-05-30  
DOI:10.6041/j.issn.1000-1298.2016.07.006
中文关键词:  荔枝  损伤  光谱分析  机器视觉  信息融合
基金项目:
作者单位
孙宝霞 广东工程职业技术学院 
汤林越 华南农业大学 
何志良 华南农业大学 
邹湘军 华南农业大学 
熊俊涛 华南农业大学 
中文摘要:利用机器视觉技术进行采后荔枝的品质检测与分级有重要意义。首先结合摄像机与荧光光谱仪进行了荔枝图像的光谱分析,荧光作为激发光进行荔枝果皮的发射光谱特性分析,确定了不同荧光照射荔枝果实表皮的视觉检测方法的可行性;然后设计了具有不同颜色光照转换控制功能的机器视觉系统,选定了红色、蓝色和绿色荧光灯,对正常和微损伤两种品质状态的荔枝果实荧光图像进行灰度直方图统计分析,确定了利用蓝色荧光作为照射光源以及HSV颜色空间的V分量进行微损伤荔枝果实图像识别的方法,利用探索性分析法对荔枝果实视觉检测试验结果进行统计与分析,确定了正常与微损伤荔枝果实图像分割的灰度图阈值范围,结合优化的圆拟合算法,实现了荔枝果实视觉智能分级系统的设计。试验结果表明:该研究方法对正常荔枝和表皮微损伤荔枝的识别正确率为92%,为荔枝产后智能化检测分级提供了技术支持。
Sun Baoxia  Tang Linyue  He Zhiliang  Zou Xiangjun  Xiong Juntao
Guangdong Engineering Polytechnic,South China Agricultural University,South China Agricultural University,South China Agricultural University and South China Agricultural University
Key Words:litchi  damage  spectral analysis  machine vision  information fusion
Abstract:It has great significance that using the machine vision technology to detect the quality of postharvest litchi fruit. Firstly, the camera and fluorescence spectrometer were used for the spectrum analysis of litchi image, the emission spectrum characteristics were analyzed under the fluorescence as excitation light, which determines the feasibility of the visual detection method of litchi fruits with different fluorescence exposures. Then, the machine vision system of different light switch controls were designed, the red, blue and green fluorescent lamp were selected, and the single chip microcomputer system was used to control the switch of the LED lamps, of which the interval is 1s; meanwhile, the image acquisition system triggered the camera to take images, the frequency of the light switch in keeping with the number of taking image times. The grey level histogram of the fluorescence image for normal and micro damaged state of two kinds of litchi fruit was statistic analyzed, the image recognition method for the micro damaged litchi fruit was determined by using blue fluorescent as light source and the V component of HSV color space. Then the exploratory analysis was used for the statistics and analysis on test results of litchi fruit visual inspection. The gray scale image segmentation threshold of the normal and micro damaged litchi fruit was determined. The gray scale image threshold segmentation, the morphology processing and the optimized Hough circle fitting method were used to the litchi images, which realized the design of the machine vision intelligent classification system for litchi fruit. The test results show that: the recognition accuracy of the normal and micro damaged litchi fruit is 92%, which can provide technical support to intelligent detection technology for postharvest fruit and vegetable.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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