Optimising the parameters influencing performance and weed (goldenrod) identification accuracy of colour co-occurrence matrices. (June 2018)

• Record Type: Journal Article
• Authors: Rehman, Tanzeel U.; Zaman, Qamar U.; Chang, Young K.; Schumann, Arnold W.; Corscadden, Kenneth W.; Esau, Travis J.
• Is Part Of: Biosystems engineering. Volume 170(2018)
• Page Start: 85
• Page End: 95
• Publication Date: 2018-06
• Subjects: Digital photography -- Graphical user interface -- Colour co-occurrence matrices -- Texture analysis; Bioengineering -- Periodicals; Agricultural engineering -- Periodicals; Biological systems -- Periodicals; Génie rural -- Périodiques; Systèmes biologiques -- Périodiques; 631
• DOI: 10.1016/j.biosystemseng.2018.04.002
• Languages: English
• ISSNs: 1537-5110

Abstract

Abstract : Wild blueberry crop yields are dependent on heavy agrochemical applications to control weeds competing with the crop. Goldenrod is a creeping herbaceous perennial weed that occurred in 90% of wild blueberry fields surveyed in Nova Scotia. A colour co-occurrence matrices (CCMs) based algorithm can be used for spot-application of herbicide on goldenrod within wild blueberry fields. The objective of this study was to analyse the effect of different parameters on computational complexity and goldenrod identification accuracy of CCMs in wild blueberry cropping system. An image acquisition graphical user interface (GUI) and CCMs based textural analysis algorithm were developed in Microsoft Visual ® C# programming language. The GUI was used to acquire 2244 area of interest images along with a set of 2244 full frame images from two different wild blueberry fields in central Nova Scotia, Canada. Six image intensity levels, seven image sizes, and three CCMs were used to for the study. The results indicated that intensity levels and image size significantly influenced the computational requirements of CCMs. Images with 256 or 128 intensity levels could be used as these levels correctly classified 94% and 89% of test observations respectively. The processing times were increased from 535 μs to 10, 650 μs and 9864 μs to 63, 750 μs as the intensity increased from 8 to 256 levels and image size increased from 16 × 16 to 1024 × 1024 pixels, respectively. The time required for textural feature extraction was not statistically significant for different image sizes used in this study. Overall, the results indicated that intensity levels of 128 or 256, a unit image size of 128 × 128 pixels, and saturation colour plane alone or in combination with hue can help to minimise the processing burden without compromising the classification accuracy for real-time applications. Highlights: Factors impacting the performance of colour co-occurrence matrices were optimised. The accuracy of 256 and 128 intensity levels was 94% and 89%, respectively. Image size of 128 × 128 pixels showed an advantage for processing time. Saturation colour plane showed more promising results compared to hue and intensity.