Multiplication of V and Cb color channel using Otsu thresholding for tomato maturity clustering

Sari, Yuita Arum; Adinugroho, Sigit; Adikara, Putra Pandu; Izzah, Abidatul

doi:10.1109/siet.2017.8304136

Cited by 10 publications

(3 citation statements)

References 10 publications

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“…In addition, further developments with the same object have been tested using a combination of two types of YUV and YCbCr color spaces. From the results of this study, the results of segmentation can reduce the error of about 3% by using several different types of cameras [6].…”

Section: Introductionmentioning

confidence: 79%

Multiple Food or Non-Food Detection in Single Tray Box Image using Fraction of Pixel Segmentation for Developing Smart Nutrition Box Prototype

2020

IJITEE

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Smart Nutrition Box is a hardware prototype to predict food leftover measurement as well as the nutrition of the leftover. In the previous approach, there was a need of trained observer to conduct the analysis. Human observer may produce subjective judgement, so that algorithm which is embedded in a prototype is proposed to get rid of the bias. Black background of tray box is used, and two menus are served in this paper. The problem on raw dataset of images is reflection and it affects the result of segmentation, since it is considered to determine the leftover measurement precisely. In this paper, we focus on how to classify image of food and non-food image in each compartment of tray box by using pixel segmentation before going to further stage of prediction. Automatic cropping is applied by means of rectangle contour detection for each compartment. Combination of L of HSL and V color channel of HSV color spaces are utilized to remove glare in each compartment. The ratio of segmented pixel is a fraction of detected object and the area of compartment. There are 10 out of 12 of tray box images containing multiple food that are correctly classified as food and non-food. The accuracy reaches 95.83% in all compartments using luminosity (L) 50% of lower upper white masking and 100% of upper white masking. It is proved that fraction pixel segmentation is sufficient to be embedded as one of features in Smart Nutrition Box prototype.

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Section: Introductionmentioning

confidence: 79%

Multiple Food or Non-Food Detection in Single Tray Box Image using Fraction of Pixel Segmentation for Developing Smart Nutrition Box Prototype

2020

IJITEE

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“…Malik et al [35] presented a riped tomato detection algorithms based on HSV colour space and a Watershed segmentation method was used to "separate" the clustered fruits. Arum Sari et al [42] multiplied the Cb and V channels, from the YCbCr and YUV colour spaces, respectively, through the Otsu segmentation algorithm, when classifying tomatoes into 6 different classes. Yin et al [31] segmented riped tomatoes through K-means clustering using the colour space L*a*b* and Indriani et al [39] combined the HSV colour space with Gray Level Co-occurrence Matrix and K-Nearest Neighbour to differentiate tomatoes into 5 different classes.…”

Section: Related Workmentioning

confidence: 99%

Benchmark of Deep Learning and a Proposed HSV Colour Space Models for the Detection and Classification of Greenhouse Tomato

et al. 2022

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The harvesting operation is a recurring task in the production of any crop, thus making it an excellent candidate for automation. In protected horticulture, one of the crops with high added value is tomatoes. However, its robotic harvesting is still far from maturity. That said, the development of an accurate fruit detection system is a crucial step towards achieving fully automated robotic harvesting. Deep Learning (DL) and detection frameworks like Single Shot MultiBox Detector (SSD) or You Only Look Once (YOLO) are more robust and accurate alternatives with better response to highly complex scenarios. The use of DL can be easily used to detect tomatoes, but when their classification is intended, the task becomes harsh, demanding a huge amount of data. Therefore, this paper proposes the use of DL models (SSD MobileNet v2 and YOLOv4) to efficiently detect the tomatoes and compare those systems with a proposed histogram-based HSV colour space model to classify each tomato and determine its ripening stage, through two image datasets acquired. Regarding detection, both models obtained promising results, with the YOLOv4 model standing out with an F1-Score of 85.81%. For classification task the YOLOv4 was again the best model with an Macro F1-Score of 74.16%. The HSV colour space model outperformed the SSD MobileNet v2 model, obtaining results similar to the YOLOv4 model, with a Balanced Accuracy of 68.10%.

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“…Malik et al [49] proposed a Watershed segmentation method was utilized to separate the clustered fruits according to ripe tomato identification algorithms, which were based on the HSV color space. The Otsu segmentation algorithm was used by Arum Sari et al [50] to multiply the Cb and V channels from the YCbCr and YUV color spaces, respectively, when categorizing tomatoes into 6 separate groups.…”

Section: Related Workmentioning

confidence: 99%

Analysis and Detection of Tomatoes Quality using Machine Learning Algorithm and Image Processing

Zuo¹

2022

IJACSA

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Grading of agricultural products methods based on artificial intelligence is more important. Because these methods have the ability to learn and thus increase the flexibility of the system. In this paper, image processing systems, detection analysis methods, and artificial intelligence are used to grade tomatoes, and the success rate of grading these methods is compared with each other. However, the purpose of this study is to obtain a solution to detect appearance defects and grade and sort the tomato crop and provide an efficient system in this field. A visual dataset is created, to investigate the approach of image processing and machine learning based on a tomato image. Tomato models are placed individually under the camera and samples are classified in a lighting box away from the effects of ambient light. Data sets have been used in three types of first, second, and third quality categories. It should be noted that quality category one has the best quality and quality category two has the medium quality and category three has the worst quality, Also, each data class contains 80 samples. Using tomato appearance such as size, texture, color, shape, etc. Image processing is performed for extract features. Tomato images are pre-processed for optimization. Then, to prepare for classification, the dimensions of the images are reduced by principal component analysis (PCA). Three categories of an artificial neural network, a support vector machine, and a decision tree are compared to show the most efficient support machine. The analysis is examined in two classes and three classes. The support vector machine has the best accuracy compared to other methods so this rate is 99.9% for two classes and 99.79% for three classes.

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Multiplication of V and Cb color channel using Otsu thresholding for tomato maturity clustering

Cited by 10 publications

References 10 publications

Multiple Food or Non-Food Detection in Single Tray Box Image using Fraction of Pixel Segmentation for Developing Smart Nutrition Box Prototype

Multiple Food or Non-Food Detection in Single Tray Box Image using Fraction of Pixel Segmentation for Developing Smart Nutrition Box Prototype

Benchmark of Deep Learning and a Proposed HSV Colour Space Models for the Detection and Classification of Greenhouse Tomato

Analysis and Detection of Tomatoes Quality using Machine Learning Algorithm and Image Processing

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