Potato feature prediction based on machine vision and 3D model rebuilding

Depth camera application, image processing for potato depth images.Potato 3D surface model rebuilding.Potato length, width, thickness, volume and mass prediction with mathematical models. Machine vision based on color, multispectral, and hyperspectral cameras to develop potato quality grading can be used to predict length, width, and mass, as well as defects on the interior and exterior of a sample. However, the images obtained by these cameras are limited by two-dimensional shape information, including width, length, and boundary. Other vital elements of appearance data related to potato mass and quality, including thickness, volume, and surface gradient changes are difficult to detect due to slight surface color differences and device limitations. In this study, we recorded the depth images of 110 potatoes using a depth camera, including samples with uniform shapes or with deformations (e.g., bumps and divots). A novel method was developed for estimating potato mass and shape information and three-dimensional models were built utilizing a new image processing algorithm for depth images. Other features, including length, width, thickness, and volume were also calculated as mass prediction related factors. Experimental results indicate that the proposed models accurately predict potato length, width, and thickness; the mean absolute errors for these predictions were 2.3mm, 2.1mm, and 2.4mm, respectively, while the mean percentage errors were 2.5%, 3.5%, and 4.4%. Mass prediction based on a 3D volume model for both normal and deformed potato samples proved to be more accurate compared to models based on area calculation. Thus 93% of samples were graded by the correct size group using the volume density model while only 73% were graded correctly using the area density. This depth image processing is an effective potential method for future non-destructive post-harvesting grading, especially for products where size, shape, and surface condition are important factors.

[1]  Dieter Fox,et al.  Interactive 3D modeling of indoor environments with a consumer depth camera , 2011, UbiComp '11.

[2]  Joni Pajarinen,et al.  Robotic manipulation in object composition space , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[3]  Manuel Berenguel,et al.  A machine vision system for seeds quality evaluation using fuzzy logic , 2001 .

[4]  R. K. Runge,et al.  Mobile 3D Computer Vision: Introducing a portable system for potato size grading , 2014 .

[5]  Wang Chenglong,et al.  Automatic detecting and grading method of potatoes based on machine vision , 2012 .

[6]  Anil K. Jain.  Fundamentals of Digital Image Processing , 2018, Control of Color Imaging Systems.

[7]  Matthew O'Donnell,et al.  Ultrasound detection using polymer microring optical resonator , 2004 .

[8]  Zhengyou Zhang,et al.  Microsoft Kinect Sensor and Its Effect , 2012, IEEE Multim..

[9]  Hiroshi Okamoto,et al.  Discrimination between potato tubers and clods by detecting the significant wavebands , 2008 .

[10]  Daniel E. Guyer,et al.  Rapid and/or nondestructive quality evaluation methods for potatoes: A review , 2015, Comput. Electron. Agric..

[11]  Baining Guo,et al.  Kinect Identity: Technology and Experience , 2011, Computer.

[12]  Jake K. Aggarwal,et al.  Human detection using depth information by Kinect , 2011, CVPR 2011 WORKSHOPS.

[13]  Dieter Fox,et al.  RGB-D mapping: Using Kinect-style depth cameras for dense 3D modeling of indoor environments , 2012, Int. J. Robotics Res..

[14]  Toshiaki Fujii,et al.  View Generation with 3D Warping Using Depth Information for FTV , 2008, 2008 3DTV Conference: The True Vision - Capture, Transmission and Display of 3D Video.

[15]  José Blasco,et al.  In-line sorting of irregular potatoes by using automated computer-based machine vision system , 2012 .

[16]  Ruigang Yang,et al.  Accurate 3D pose estimation from a single depth image , 2011, 2011 International Conference on Computer Vision.

[17]  Takashi Kataoka,et al.  An image processing algorithm for detecting in-line potato tubers without singulation , 2010 .

[18]  Naoshi Kondo,et al.  Automation on fruit and vegetable grading system and food traceability. , 2010 .

[19]  Navid Razmjooy,et al.  A real-time mathematical computer method for potato inspection using machine vision , 2012, Comput. Math. Appl..

[20]  R. Lange,et al.  Solid-state time-of-flight range camera , 2001 .

[21]  Junsong Yuan,et al.  Robust hand gesture recognition with kinect sensor , 2011, ACM Multimedia.

[22]  Bayram Cetisli,et al.  Design of an automatic apple sorting system using machine vision , 2016, Comput. Electron. Agric..

[23]  Bundit Jarimopas,et al.  An experimental machine vision system for sorting sweet tamarind , 2008 .

[24]  H. Gonzalez-Jorge,et al.  A genetic algorithm approach for feature selection in potatoes classification by computer vision , 2009, 2009 35th Annual Conference of IEEE Industrial Electronics.

[25]  José Blasco,et al.  Multispectral inspection of citrus in real-time using machine vision and digital signal processors , 2002 .

[26]  Vikram Chalana,et al.  PC‐based machine vision system for real‐time computer‐aided potato inspection , 1998 .

[27]  José Blasco,et al.  Original paper: Automatic sorting of satsuma ( Citrus unshiu ) segments using computer vision and morphological features , 2009 .

[28]  Nuria Aleixos,et al.  Erratum to: Advances in Machine Vision Applications for Automatic Inspection and Quality Evaluation of Fruits and Vegetables , 2011 .