Mobile Vision for Plant Biometric System

In human’s life plant plays an important part to balance the nature and supply food-&-medicine. The traditional manual plant species identification method is tedious and time-consuming process and requires expert knowledge. The rapid developments of mobile and ubiquitous computing make automated plant biometric system really feasible and accessible for anyone-anywhere-anytime. More and more research are ongoing to make it a more realistic tool for common man to access the agro-information by just a click. Based on this, the chapter highlights the significant growth of plant identification and leaf disease recognition over past few years. A wide range of research analysis is shown in this chapter in this context. Finally, the chapter showed the future scope and applications of AaaS and similar systems in agro-field.

[1]  R. C. Tripathi,et al.  Plant leaf species identification using Curvelet transform , 2011, 2011 2nd International Conference on Computer and Communication Technology (ICCCT-2011).

[2]  Ying Zhu,et al.  Review of Plant Identification Based on Image Processing , 2016, Archives of Computational Methods in Engineering.

[3]  Yunyoung Nam,et al.  Utilizing venation features for efficient leaf image retrieval , 2008, J. Syst. Softw..

[4]  Seishi Ninomiya,et al.  Analysis of petal shape variation of Primula sieboldii by elliptic fourier descriptors and principal component analysis. , 2004, Annals of botany.

[5]  Berrin A. Yanikoglu,et al.  Automatic plant identification from photographs , 2014, Machine Vision and Applications.

[6]  Timothy Jassmann Mobile Leaf Classification Application Utilizing a Convolutional Neural Network , 2015 .

[7]  Naif Alajlan,et al.  Shape retrieval using triangle-area representation and dynamic space warping , 2007, Pattern Recognit..

[8]  Yuxuan Wang,et al.  A Leaf Recognition Algorithm for Plant Classification Using Probabilistic Neural Network , 2007, 2007 IEEE International Symposium on Signal Processing and Information Technology.

[9]  André Ricardo Backes,et al.  Texture analysis and classification using deterministic tourist walk , 2010, Pattern Recognit..

[10]  Yuan Yan Tang,et al.  ApLeaf: An efficient android-based plant leaf identification system , 2015, Neurocomputing.

[11]  Yung-Sheng Chen,et al.  Leaf Segmentation, Its 3D Position Estimation and Leaf Classification from a Few Images with Very Close Viewpoints , 2009, ICIAR.

[12]  J. V. Stafford,et al.  How wireless will change agriculture. , 2007 .

[13]  Puteh Saad,et al.  Plant leaf identification using moment invariants & General Regression Neural Network , 2011, 2011 11th International Conference on Hybrid Intelligent Systems (HIS).

[14]  Hongbin Zha,et al.  Image-based plant modeling by knowing leaves from their apexes , 2008, 2008 19th International Conference on Pattern Recognition.

[15]  K. Selçuk Candan,et al.  SEMCOG: an integration of SEMantics and COGnition-based approaches for image retrieval , 1997, SAC '97.

[16]  Shanwen Zhang,et al.  A Method of Plant Classification Based on Wavelet Transforms and Support Vector Machines , 2009, ICIC.

[17]  J. Endler,et al.  The Relative Success of Some Methods for Measuring and Describing the Shape of Complex Objects , 1998 .

[18]  Bin Wang,et al.  MARCH: Multiscale-arch-height description for mobile retrieval of leaf images , 2015, Inf. Sci..

[19]  Paolo Remagnino,et al.  The Extraction of Venation from Leaf Images by Evolved Vein Classifiers and Ant Colony Algorithms , 2010, ACIVS.

[20]  Nozha Boujemaa,et al.  Semantic-based automatic structuring of leaf images for advanced plant species identification , 2016, Multimedia Tools and Applications.

[21]  Kimmo Rumpunen,et al.  Comparison of differentiation estimates based on morphometric and molecular data, exemplified by various leaf shape descriptors and RAPDs in the genus Chaenomeles (Rosaceae) , 2002 .

[22]  Phil Culverhouse,et al.  Time to automate identification , 2010, Nature.

[23]  Juan C. Caicedo,et al.  Fine-tuning Deep Convolutional Networks for Plant Recognition , 2015, CLEF.

[24]  Anil K. Jain,et al.  Automatic image analysis of plant root structures , 1992, Proceedings., 11th IAPR International Conference on Pattern Recognition. Vol.II. Conference B: Pattern Recognition Methodology and Systems.

[25]  Sean White,et al.  Designing a mobile user interface for automated species identification , 2007, CHI.

[26]  K. Selçuk Candan,et al.  Hierarchical Image Modeling for Object-Based Media Retrieval , 1998, Data Knowl. Eng..

[27]  Chia-Ling Lee,et al.  Classification of leaf images , 2006, Int. J. Imaging Syst. Technol..

[28]  Eamonn J. Keogh,et al.  Time series shapelets: a new primitive for data mining , 2009, KDD.

[29]  Sean White,et al.  Virtual Vouchers: Prototyping a Mobile Augmented Reality User Interface for Botanical Species Identification , 2006, 3D User Interfaces (3DUI'06).

[30]  D. Warren Automated leaf shape description for variety testing in chrysanthemums , 1997 .

[31]  Gang Chen,et al.  A flower image retrieval method based on ROI feature , 2004, Journal of Zhejiang University. Science.

[32]  Dorothy Ndedi Monekosso,et al.  Artificial Ants to Extract Leaf Outlines and Primary Venation Patterns , 2008, ANTS Conference.

[33]  Andrew Zisserman,et al.  Delving deeper into the whorl of flower segmentation , 2010, Image Vis. Comput..

[34]  Sean White,et al.  LeafView: A User Interface for Automated Botanical Species Identification and Data Collection , 2006 .

[35]  Debashis Ghosh,et al.  Multi-resolution mobile vision system for plant leaf disease diagnosis , 2016, Signal Image Video Process..

[36]  R. C. Tripathi,et al.  Relative sub-image based features for leaf recognition using support vector machine , 2011, ICCCS '11.

[37]  Fritz Brugger,et al.  Mobile applications in agriculture. , 2011 .

[38]  Don Kirkup,et al.  The use of digital image-based morphometrics to study the phenotypic mosaic in taxa with porous genomes , 2009 .

[39]  A. Ferrer,et al.  Pixel classification methods for identifying and quantifying leaf surface injury from digital images , 2014 .

[40]  Haibin Ling,et al.  Shape Classification Using the Inner-Distance , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[41]  Paolo Remagnino,et al.  Plant species identification using digital morphometrics: A review , 2012, Expert Syst. Appl..

[42]  R. Melville,et al.  The Accurate Definition of Leaf Shapes by Rectangular Coordinates , 1937 .

[43]  Thomas S. Colvin,et al.  Using Soil Attributes and GIS for Interpretation of Spatial Variability in Yield , 2000 .

[44]  Oskar Söderkvist,et al.  Computer Vision Classification of Leaves from Swedish Trees , 2001 .

[45]  Jarbas Joaci de Mesquita Sá Junior,et al.  Plant leaf identification using Gabor wavelets , 2009 .

[46]  Matjaz Gams,et al.  Automatic recognition of gait-related health problems in the elderly using machine learning , 2012, Multimedia Tools and Applications.

[47]  Odemir Martinez Bruno,et al.  Fractal dimension applied to plant identification , 2008, Inf. Sci..

[48]  Pankaj Doke,et al.  GappaGoshti™: Digital inclusion for rural mass , 2012, 2012 Fourth International Conference on Communication Systems and Networks (COMSNETS 2012).

[49]  D. K. Withanage,et al.  Computer assisted plant identification system for Android , 2015, 2015 Moratuwa Engineering Research Conference (MERCon).

[50]  Conor Meade,et al.  Multivariate analysis of leaf shape patterns in Asian species of the Uvaria group (Annonaceae) , 2003 .

[51]  Jong Wook Kim,et al.  RanKloud: Scalable Multimedia Data Processing in Server Clusters , 2011, IEEE MultiMedia.

[52]  Arun Pande,et al.  Late Blight Forecast Using Mobile Phone Based Agro Advisory System , 2009, PReMI.

[53]  Paul Wilkin,et al.  A morphometric study of species delimitation in Sternbergia lutea (Alliaceae, Amaryllidoideae) and its allies S. sicula and S. greuteriana , 2008 .

[54]  Robert W. Scotland,et al.  How many species of seed plants are there , 2003 .

[55]  Jonathan Y. Clark Identification of botanical specimens using artificial neural networks , 2004, 2004 Symposium on Computational Intelligence in Bioinformatics and Computational Biology.

[56]  Jonathan Y. Clark Neural networks and cluster analysis for unsupervised classification of cultivated species of Tilia Malvaceae , 2009 .

[57]  Tristan Perez,et al.  Fine-Grained Plant Classification Using Convolutional Neural Networks for Feature Extraction , 2014, CLEF.

[58]  Debashis Ghosh,et al.  Energy efficient mobile vision system for plant leaf disease identification , 2014, 2014 IEEE Wireless Communications and Networking Conference (WCNC).

[59]  Udoyara S. Tim,et al.  A Spatial Decision Support System for Livestock Production Planning and Environmental Management , 1995 .

[60]  Paolo Remagnino,et al.  Deep-plant: Plant identification with convolutional neural networks , 2015, 2015 IEEE International Conference on Image Processing (ICIP).

[61]  U. S. Tim,et al.  The application of GIS in environmental health sciences: opportunities and limitations. , 1995, Environmental research.

[62]  Srinivasan Karthik,et al.  mKRISHI: Simplification Of IVR Based Services For Rural Community , 2014, IHCI.

[63]  A. Samal,et al.  Plant species identification using Elliptic Fourier leaf shape analysis , 2006 .

[64]  Noel E. O'Connor,et al.  A multiscale representation method for nonrigid shapes with a single closed contour , 2004, IEEE Transactions on Circuits and Systems for Video Technology.

[65]  Yan Li,et al.  Leaf Vein Extraction Using Independent Component Analysis , 2006, 2006 IEEE International Conference on Systems, Man and Cybernetics.

[66]  Anne Verroust-Blondet,et al.  A shape-based approach for leaf classification using multiscaletriangular representation , 2013, ICMR.

[67]  Zheru Chi,et al.  Combined thresholding and neural network approach for vein pattern extraction from leaf images , 2006 .

[68]  Debashis Ghosh,et al.  AgroMobile: A Cloud-Based Framework for Agriculturists on Mobile Platform , 2013 .

[69]  Debashis Ghosh,et al.  An efficient low vision plant leaf shape identification system for smart phones , 2017, Multimedia Tools and Applications.

[70]  Xiao-Ping Zhang,et al.  Advances in Intelligent Computing, International Conference on Intelligent Computing, ICIC 2005, Hefei, China, August 23-26, 2005, Proceedings, Part I , 2005, ICIC.

[71]  Guang Zeng,et al.  Rapid automated detection of roots in minirhizotron images , 2010, Machine Vision and Applications.

[72]  A. Mechelli,et al.  Using Support Vector Machine to identify imaging biomarkers of neurological and psychiatric disease: A critical review , 2012, Neuroscience & Biobehavioral Reviews.

[73]  Massimiliano Ruggeri,et al.  Wireless communication protocol for agricultural machines synchronization and fleet management , 2010, 2010 IEEE International Symposium on Industrial Electronics.

[74]  Paul M. de Zeeuw,et al.  Computer-assisted tree taxonomy by automated image recognition , 2009, Eng. Appl. Artif. Intell..

[75]  G. Natho,et al.  Variationsbreite und Bastardbildung bei mitteleuropäischen Birkensippen , 1959 .

[76]  De-shuang Huang,et al.  Computer-Aided Plant Species Identification (CAPSI) Based on Leaf Shape Matching Technique , 2006 .

[77]  Debashis Ghosh,et al.  Mobile plant species classification: A low computational aproach , 2013, 2013 IEEE Second International Conference on Image Information Processing (ICIIP-2013).

[78]  Zhiyong Wang,et al.  Shape based leaf image retrieval , 2003 .

[79]  David J Hearn Shape analysis for the automated identification of plants from images of leaves. , 2009 .

[80]  Cordelia Schmid,et al.  Computer Vision – ECCV 2012 , 2012, Lecture Notes in Computer Science.

[81]  Yunyoung Nam,et al.  A similarity-based leaf image retrieval scheme: Joining shape and venation features , 2008, Comput. Vis. Image Underst..

[82]  Zhi-Kai Huang,et al.  Bark Classification Based on Contourlet Filter Features Using RBPNN , 2006, ICIC.

[83]  C. Berg,et al.  Comparative morphology of populations of Monstera Adans. (Araceae) from natural forest fragments in Northeast Brazil using elliptic Fourier Analysis of leaf outlines , 2008, Kew Bulletin.

[84]  Yongyun Cho,et al.  An Approach for a Self-Growing Agricultural Knowledge Cloud in Smart Agriculture , 2013, MUE.