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Machine Vision Technique for Rapid Measurement of Soybean Seed Vigor
Lee, Hoonsoo,Huy, Tran Quoc,Park, Eunsoo,Bae, Hyung-Jin,Baek, Insuck,Kim, Moon S.,Mo, Changyeun,Cho, Byoung-Kwan Korean Society for Agricultural Machinery 2017 바이오시스템공학 Vol.42 No.3
Purpose: Morphological properties of soybean roots are important indicators of the vigor of the seed, which determines the survival rate of the seedlings grown. The current vigor test for soybean seeds is manual measurement with the human eye. This study describes an application of a machine vision technique for rapid measurement of soybean seed vigor to replace the time-consuming and labor-intensive conventional method. Methods: A CCD camera was used to obtain color images of seeds during germination. Image processing techniques were used to obtain root segmentation. The various morphological parameters, such as primary root length, total root length, total surface area, average diameter, and branching points of roots were calculated from a root skeleton image using a customized pixel-based image processing algorithm. Results: The measurement accuracy of the machine vision system ranged from 92.6% to 98.8%, with accuracies of 96.2% for primary root length and 96.4% for total root length, compared to manual measurement. The correlation coefficient for each measurement was 0.999 with a standard error of prediction of 1.16 mm for primary root length and 0.97 mm for total root length. Conclusions: The developed machine vision system showed good performance for the morphological measurement of soybean roots. This image analysis algorithm, combined with a simple color camera, can be used as an alternative to the conventional seed vigor test method.
Machine Vision Technique for Rapid Measurement of Soybean Seed Vigor
( Hoonsoo Lee ),( Tran Quoc Huy ),( Eunsoo Park ),( Hyung-jin Bae ),( Insuck Baek ),( Moon S Kim ),( Changyeun Mo ),( Byoung-kwan Cho ) 한국농업기계학회 2017 바이오시스템공학 Vol.42 No.3
Purpose: Morphological properties of soybean roots are important indicators of the vigor of the seed, which determines the survival rate of the seedlings grown. The current vigor test for soybean seeds is manual measurement with the human eye. This study describes an application of a machine vision technique for rapid measurement of soybean seed vigor to replace the time-consuming and labor-intensive conventional method. Methods: A CCD camera was used to obtain color images of seeds during germination. Image processing techniques were used to obtain root segmentation. The various morphological parameters, such as primary root length, total root length, total surface area, average diameter, and branching points of roots were calculated from a root skeleton image using a customized pixel-based image processing algorithm. Results: The measurement accuracy of the machine vision system ranged from 92.6% to 98.8%, with accuracies of 96.2% for primary root length and 96.4% for total root length, compared to manual measurement. The correlation coefficient for each measurement was 0.999 with a standard error of prediction of 1.16 mm for primary root length and 0.97 mm for total root length. Conclusions: The developed machine vision system showed good performance for the morphological measurement of soybean roots. This image analysis algorithm, combined with a simple color camera, can be used as an alternative to the conventional seed vigor test method.
Research and Technology Trend Analysis by Big Data-Based Smart Livestock Technology: a Review
김민지,모창연,김현태,조병관,홍순중,이대현,신창섭,장경제,김용현,Insuck Baek 한국농업기계학회 2021 바이오시스템공학 Vol.46 No.4
Purpose This study introduces the global research and technological trends related to various kinds of Information and Communications Technologies (ICTs) used and applied in the livestock industry by improving productivity via breeding, disease and optimal environment control, and smart business management. Method Prior research data was collected using “ICT,” “IoT,” “information technology (IT),” “ubiquitous technology,” “smart livestock,” and “big data” as main keywords. Results Most livestock farms in Korea adopt smart livestock technology that are mostly used in the 1st or 1.5th generations, while continuous developments are being carried out for technologies of the 2nd and 3rd generations. In the livestock house, camera vision, radio-frequency identification (RFID), beacon sensors, and environmental sensors are used in livestock farms and houses to collect information compiled into a database to introduce an automated system for livestock management. Conclusion The data collected from each individual and farm can enable precise breeding and ultimately improve the productivity and efficiency of smart livestock systems. It is necessary to prepare a systematic system at the national level for data collection, ownership, and sharing to improve the productivity and efficiency of the smart livestock system.
Lim, Jongguk,Kim, Giyoung,Mo, Changyeun,Kim, Moon S.,Chao, Kuanglin,Qin, Jianwei,Fu, Xiaping,Baek, Insuck,Cho, Byoung-Kwan Elsevier 2016 Talanta Vol.151 No.-
<P><B>Abstract</B></P> <P>Illegal use of nitrogen-rich melamine (C<SUB>3</SUB>H<SUB>6</SUB>N<SUB>6</SUB>) to boost perceived protein content of food products such as milk, infant formula, frozen yogurt, pet food, biscuits, and coffee drinks has caused serious food safety problems. Conventional methods to detect melamine in foods, such as Enzyme-linked immunosorbent assay (ELISA), High-performance liquid chromatography (HPLC), and Gas chromatography–mass spectrometry (GC–MS), are sensitive but they are time-consuming, expensive, and labor-intensive. In this research, near-infrared (NIR) hyperspectral imaging technique combined with regression coefficient of partial least squares regression (PLSR) model was used to detect melamine particles in milk powders easily and quickly. NIR hyperspectral reflectance imaging data in the spectral range of 990–1700nm were acquired from melamine-milk powder mixture samples prepared at various concentrations ranging from 0.02% to 1%. PLSR models were developed to correlate the spectral data (independent variables) with melamine concentration (dependent variables) in melamine-milk powder mixture samples. PLSR models applying various pretreatment methods were used to reconstruct the two-dimensional PLS images. PLS images were converted to the binary images to detect the suspected melamine pixels in milk powder. As the melamine concentration was increased, the numbers of suspected melamine pixels of binary images were also increased. These results suggested that NIR hyperspectral imaging technique and the PLSR model can be regarded as an effective tool to detect melamine particles in milk powders.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Melamine particles contained in milk powder were detected by NIR hyperspectral imaging. </LI> <LI> Regression coefficient values were used to reconstruct the PLS images. </LI> <LI> PLS images were used to discriminate the melamine pixels from milk powder pixels. </LI> <LI> Melamine particles at 200ppm in milk powder were confirmed without pretreatment. </LI> </UL> </P> <P><B>Graphical abstract</B></P> <P>[DISPLAY OMISSION]</P>