The Faculty of Engineering

West Indian Journal of Engineering

Volume 44 Number 2 Jan 2022

Investigation into the Use of a Fourier Based Edge Detection Image Processing Approach for Assessing Cocoa Pod Stem Cut Quality

by Renique J. Murray, Kathryn Maharaj, Robert A. Birch and Cilla Pemberton

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Abstract: As the cocoa industry continues to grow, there is an increasing need for greater efficiency and higher levels of quality in all areas. The objective assessment of pod stem cut quality is one such critical area, as it not only directly impacts productivity but wider industry economics. Despite this, and the significance of cut quality in other agricultural applications, there is little done in the area of developing an objective and reliable assessment method. This work proposes, develops and tests a Fourier based image processing approach for assessing cut quality. The proposed Fourier Peak Index (FPI) method is implemented in MATLAB 2013 via a series of algorithms. Further, a windowed FPI (WFPI) is also developed and implemented in the same environment. Both methods are tested using a set of 40 images, comprising of 10 reference images, 15 poor cut images and 15 good cut images. The results obtained showed that the FPI method had a 93% accuracy in categorising good cuts, 60% accuracy in categorising poor cuts and an overall accuracy of approximately 77%. It was particularly noted that poor cuts with long, smooth excess bark material attached to the stems, were poorly categorised by the FPI method. Additionally, the method’s effectiveness was found to be significantly influenced by image lighting, as this determined the amount of data loss during the image binarisation step. Notwithstanding, the WFPI method was found to be effective in categorising the images that were incorrectly categorised by the FPI method. The combined efforts of both methods had the potential to increase detection and categorisation accuracy to a maximum of 97%

Keywords: Cocoa; cut quality; Fourier; Image analysis

https://doi.org/10.47412/ZULR6423