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Development of Color Co-occurrence Matrix Based Machine Vision Algorithms for Wild Blueberry Fields

2012; American Society of Agricultural and Biological Engineers; Volume: 28; Issue: 3 Linguagem: Inglês

10.13031/2013.42321

1943-7838

Young Chang, Q. U. Zaman, Arnold W. Schumann, David Percival, Travis J. Esau, Gashaw Ayalew,

Remote Sensing in Agriculture

Co-occurrence matrix-based textural features were analyzed and three algorithms were developed to identify bare spots, wild blueberry plants, and weeds with the aim of applying agrochemicals to wild blueberry cropping fields in a spot-specific manner. Images were acquired using four cameras and a ruggedized laptop with custom-written programs coded in Microsoft Visual C++. Textural features were extracted from the images using MATLAB and analyzed with SAS. Forty-four textural features were extracted from co-occurrence matrices of NTSC luminance (L), hue, saturation, and intensity (HSI) images. Multiple discriminant analysis using all 44 features (DF_ALL model) showed 98.1% of overall classification accuracy and 83 ms of processing time of an image with C++ calculation. Based on the results of multiple discriminant analysis and two-step linear discrimination plotting, the DF_HSISD, DF_SISD, and HSILD algorithms are preferred algorithms with overall accuracy of 94.9%, 92.7%, and 91.4%, and processing time of 55, 27, and 29 ms, respectively. Any of three reduced textural feature algorithms can be employed for spot-specific application of agrochemicals in wild blueberry cropping fields. The choice of one algorithm over another will depend on whether processing speed or accuracy is more important for the end-users application.