Citation

Islam, Md. Shabiul and Siddiquee, Kazy Noor e Alam and Ud Dowla, Mohammad Yasin and Rezaul, Karim Mohammed and Grout, Vic (2020) Detection, quantification and classification of ripened tomatoes: a comparative analysis of image processing and machine learning. IET Digital Library, 14 (1). pp. 2442-2456. ISSN 1751-9667

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Abstract

In this study, specifically for the detection of ripe/unripe tomatoes with/without defects in the crop field, two distinct methods are described and compared from captured images by a camera mounted on a mobile robot. One is a machine learning approach, known as ‘Cascaded Object Detector’ (COD) and the other is a composition of traditional customised methods, individually known as ‘Colour Transformation’: ‘Colour Segmentation’ and ‘Circular Hough Transformation’. The (Viola-Jones) COD generates ‘histogram of oriented gradient’ (HOG) features to detect tomatoes. For ripeness checking, the RGB mean is calculated with a set of rules. However, for traditional methods, colour thresholding is applied to detect tomatoes either from natural or solid background and RGB colour is adjusted to identify ripened tomatoes. This algorithm is shown to be optimally feasible for any micro-controller based miniature electronic devices in terms of its run time complexity of O(n 3) for a traditional method in best and average cases. Comparisons show that the accuracy of the machine learning method is 95%, better than that of the Colour Segmentation Method using MATLAB.

Item Type:	Article
Uncontrolled Keywords:	Image segmentation
Subjects:	T Technology > TA Engineering (General). Civil engineering (General) > TA1501-1820 Applied optics. Photonics
Divisions:	Faculty of Engineering (FOE)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	07 Oct 2021 07:37
Last Modified:	07 Oct 2021 07:37
URII:	http://shdl.mmu.edu.my/id/eprint/8219

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