Quantitative potato tuber phenotyping by 3D imaging

[1]  W. Stiekema,et al.  Multiple alleles for tuber shape in diploid potato detected by qualitative and quantitative genetic analysis using RFLPs. , 1994, Genetics.

[2]  Mark Meyer,et al.  Discrete Differential-Geometry Operators for Triangulated 2-Manifolds , 2002, VisMath.

[3]  M. Knoche,et al.  Analysing fruit shape in sweet cherry (Prunus avium L.) , 2002 .

[4]  Karri Muinonen,et al.  Three-dimensional Stochastic Shape Modelling for Potato Tubers , 2006, Potato Research.

[5]  P. Langridge,et al.  Breeding Technologies to Increase Crop Production in a Changing World , 2010, Science.

[6]  Yutaka Satoh,et al.  Object detection based on a robust and accurate statistical multi-point-pair model , 2011, Pattern Recognit..

[7]  Jyoti A Kodagali,et al.  Computer Vision and Image Analysis based Techniques for Automatic Characterization of Fruits – A Review , 2012 .

[8]  M. Westoby,et al.  ‘Structure-from-Motion’ photogrammetry: A low-cost, effective tool for geoscience applications , 2012 .

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

[10]  Michael M. Kazhdan,et al.  Screened poisson surface reconstruction , 2013, TOGS.

[11]  Hans-Peter Seidel,et al.  An efficient construction of reduced deformable objects , 2013, ACM Trans. Graph..

[12]  L. Zotarelli,et al.  Rate and timing of nitrogen fertilizer application on potato ‘FL1867’. Part I: Plant nitrogen uptake and soil nitrogen availability , 2015 .

[13]  L. Zotarelli,et al.  Rate and timing of nitrogen fertilizer application on potato ‘FL1867’ part II: Marketable yield and tuber quality , 2015 .

[14]  M. Bonierbale,et al.  Tuber shape and eye depth variation in a diploid family of Andean potatoes , 2015, BMC Genetics.

[15]  Nelson L. Max,et al.  Structured Light-Based 3D Reconstruction System for Plants , 2015, Sensors.

[16]  Erees Queen B. Macabebe,et al.  Image segmentation using K-means color quantization and density-based spatial clustering of applications with noise (DBSCAN) for hotspot detection in photovoltaic modules , 2016, 2016 IEEE Region 10 Conference (TENCON).

[17]  Bo Li,et al.  A novel 3D imaging system for strawberry phenotyping , 2017, Plant Methods.

[18]  S. Sankaran,et al.  Image-based automated potato tuber shape evaluation , 2018, Journal of Food Measurement and Characterization.

[19]  Hong Sun,et al.  Potato feature prediction based on machine vision and 3D model rebuilding , 2017, Comput. Electron. Agric..

[20]  Satoshi Yamamoto,et al.  3D reconstruction of apple fruits using consumer-grade RGB-depth sensor , 2018, Engineering in Agriculture, Environment and Food.

[21]  I. Dan,et al.  CLASSIFICATION OF STRAWBERRY FRUIT SHAPE BY MACHINE LEARNING , 2018, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences.

[22]  Hong Sun,et al.  Potato quality grading based on machine vision and 3D shape analysis , 2018, Comput. Electron. Agric..

[23]  W. Grzebisz,et al.  The early prognosis of tuber yield based on nitrogen status in potato tops , 2018, Plant, Soil and Environment.

[24]  Wenhao Zhang,et al.  Innovative 3D and 2D machine vision methods for analysis of plants and crops in the field , 2018, Comput. Ind..

[25]  D. Kostova,et al.  Application of high-throughput phenotyping tool Tomato Analyzer to characterize Balkan Capsicum fruit diversity , 2020, Scientia Horticulturae.

[26]  A geometric model for estimating the volume and surface area of apples , 2020 .

[27]  L. Xiong,et al.  Crop Phenomics and High-throughput Phenotyping: Past Decades, Current Challenges and Future Perspectives. , 2020, Molecular plant.

[28]  Bo Li,et al.  Defining strawberry shape uniformity using 3D imaging and genetic mapping , 2020, Horticulture Research.

[29]  A. Samal,et al.  Intelligent Image Analysis for Plant Phenotyping , 2020 .

[30]  Yonghuai Liu,et al.  FFD: Fast Feature Detector , 2020, IEEE Transactions on Image Processing.