NDLI: Detection, quantification and classification of ripened tomatoes: a comparative analysis of image processing and machine learning

Content Provider	IET Digital Library
Author	Siddiquee, Kazy Noor e Alam Islam, Md. Shabiul Dowla, Mohammad Yasin Ud Rezaul, Karim Mohammed Grout, Vic
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.
Starting Page	2442
Ending Page	2456
Page Count	15
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 11, Sep (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/11
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.0738
Journal	IET Image Processing
Publisher Date	2020-04-27
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Agriculture Camera Cascaded Object Detector Circular Hough Transformation Colour Segmentation Method Colour Thresholding Colour Transformation Computer Vision And Image Processing Technique Crop Field Crops Feature Extraction Fisheries Computing Forestry Hough Transforms Image Classification Image Colour Analysis Image Processing Image Recognition Image Segmentation Image Sonsor Industrial Application of IT Industrial Robots Integral Transforms Knowledge Engineering Technique Learning in AI Machine Learning Method Machine Learning Technique Mathematical Analysis Microcontroller Microprocessor Chips Microprocessors And Microcomputer Mobile Robots Object Detection RGB Colour Ripened Tomatoes Ripeness Checking Robot Vision Tomato Field
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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