彭彦昆,赵芳,白京,郑晓春,王文秀,孙群.基于图谱特征的番茄种子活力检测与分级[J].农业机械学报,2018,49(2):327-333.
PENG Yankun,ZHAO Fang,BAI Jing,ZHENG Xiaochun,WANG Wenxiu,SUN Qun.Detection and Classification of Tomato Seed Vitality Based on Image Processing[J].Transactions of the Chinese Society for Agricultural Machinery,2018,49(2):327-333.
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基于图谱特征的番茄种子活力检测与分级   [下载全文]
Detection and Classification of Tomato Seed Vitality Based on Image Processing   [Download Pdf][in English]
投稿时间:2017-06-05  
DOI:10.6041/j.issn.1000-1298.2018.02.042
中文关键词:  番茄种子  活力  高光谱  图像处理
基金项目:国家重点研发计划项目(2017YFD0701205)和北京市科技计划项目(Z151100001015004)
作者单位
彭彦昆 中国农业大学 
赵芳 中国农业大学 
白京 中国农业大学 
郑晓春 中国农业大学 
王文秀 中国农业大学 
孙群 中国农业大学 
中文摘要:为解决现存分级过程中损伤种子问题,替代仅根据种子表面特征评价的粗筛查方法,本研究基于高光谱的图谱融合技术,提出了一种番茄种子图像采集并辨识种子特征进而将种子分级的算法。试验随机选取170粒番茄种子作为样品,校正集与验证集比例约为3∶1。通过标准发芽试验得到种子活力结果,基于连续投影算法(Successive projections algorithm, SPA)求得反映番茄种子活力的特征波长为:535、577、595、654、684、713、744、768、809、840nm。对特征波长下的光谱图像进行解析,通过双边滤波法、大津法、形态学变换算法提取了种子边缘轮廓,计算求出每粒种子的面积、圆形度以及图像灰度平均值。基于统计学分析,利用校正集128粒种子的特征值及其标准发芽试验结果求出分级阈值,其中有活力为合格,无活力为不合格。然后,利用验证集42粒种子的特征值对阈值进行验证,结果显示,在713nm波长下的图像特征对活力结果判断分级的正确率最高,校正集和验证集的正确率分别为93.75%和90.48%。
PENG Yankun  ZHAO Fang  BAI Jing  ZHENG Xiaochun  WANG Wenxiu  SUN Qun
China Agricultural University,China Agricultural University,China Agricultural University,China Agricultural University,China Agricultural University and China Agricultural University
Key Words:tomato seeds  vitality  hyperspectral  image processing
Abstract:Precision seeding put forward higher requirements for the seed quality. However, existing grading methods of seeds are destructive and unsuitable for sorting seeds based on internal characteristics, and a classification method based on the seed vitality is required. An algorithm of image acquisition and characteristics identification and classification of tomato seeds was proposed based on hyperspectral technique and image processing technology. Totally 170 grains of tomato seeds were randomly selected as the research object, and the ratio of calibration set and validation set was about 3∶1. Images of tomato seeds were collected by hyperspectral acquisition system which was composed of high performance lighting CCD camera, line scanning spectrometer, oriel instruments, image acquisition card and computer. The resolution of the camera and range of spectrometer were respectively 1376 pixels×1040 pixels and 400~1100nm. An average spectrum of interest region of each seed could be obtained. Then the results of seed vigor were obtained from the standard germination test. And the characteristic wavelengths of tomato seed vigor were acquired by successive projections algorithm (SPA), including 535nm, 577nm, 595nm, 654nm, 684nm, 713nm, 744nm, 768nm, 809nm and 840nm. The images under above characteristic wavelengths were preprocessed by bilateral filtering, Ostu and morphological transformation to gain the seed eigenvalues including area, circularity and average gray. The classification thresholds were calculated according to the eigenvalues and vitality results of the calibration set based on statistical regularity, and prediction analysis of validation set was carried out. The results showed that both of average of area and gray had significant difference between viable seeds and non-viable seeds, while the difference of circularity between viable seeds and non-viable seeds was insignificant. Classification accuracy of calibration and validation sets was above 85% in eight characteristics wavelengths. And 713nm gave the best result, the accuracy of the calibration and validation set were 93.75% and 90.48%, respectively. The results provided a new method for rapid nondestructive grading of tomato seeds, and lay the foundation for the development of tomato seed grading equipment based on seed vitality.

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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