Crop species identification using machine vision of computer extracted individual leaves

João Camargo Neto; George E. Meyer

doi:10.1117/12.631715

7 November 2005 Crop species identification using machine vision of computer extracted individual leaves

João Camargo Neto, George E. Meyer

Proceedings Volume 5996, Optical Sensors and Sensing Systems for Natural Resources and Food Safety and Quality; 599608 (2005) https://doi.org/10.1117/12.631715
Event: Optics East 2005, 2005, Boston, MA, United States

Abstract

An unsupervised method for plant species identification was developed which uses computer extracted individual whole leaves from color images of crop canopies. Green canopies were isolated from soil/residue backgrounds using a modified Excess Green and Excess Red separation method. Connected components of isolated green regions of interest were changed into pixel fragments using the Gustafson-Kessel fuzzy clustering method. The fragments were reassembled as individual leaves using a genetic optimization algorithm and a fitness method. Pixels of whole leaves were then analyzed using the elliptic Fourier shape and Haralick's classical textural feature analyses. A binary template was constructed to represent each selected leaf region of interest. Elliptic Fourier descriptors were generated from a chain encoding of the leaf boundary. Leaf template orientation was corrected by rotating each extracted leaf to a standard horizontal position. This was done using information provided from the first harmonic set of coefficients. Textural features were computed from the grayscale co-occurrence matrix of the leaf pixel set. Standardized leaf orientation significantly improved the leaf textural venation results. Principle component analysis from SAS^(R) was used to select the best Fourier descriptors and textural indices. Indices of local homogeneity, and entropy were found to contribute to improved classification rates. A SAS classification model was developed and correctly classified 83% of redroot pigweed, 100% of sunflower 83% of soybean, and 73% of velvetleaf species. An overall plant species correct classification rate of 86% was attained.

Citation Download Citation

João Camargo Neto and George E. Meyer "Crop species identification using machine vision of computer extracted individual leaves", Proc. SPIE 5996, Optical Sensors and Sensing Systems for Natural Resources and Food Safety and Quality, 599608 (7 November 2005); https://doi.org/10.1117/12.631715

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