Fruit Ripeness Based on RGB, HSV, HSL, L*a*b* Color Feature Using SVM

Pardede, Jasman; Husada, Milda Gustiana; Hermana, Asep Nana; Rumapea, Sri Agustina

doi:10.1109/icosnikom48755.2019.9111486

Cited by 30 publications

(29 citation statements)

References 17 publications

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“…Before converting to L * a * b * colour channel, the RGB image was first transformed into XYZ colour channel, which returns as a numerical array of the same size as the input conversion. With reference to work done by Pardede et al, 23 the RGB colour channel was first converted to XYZ colour channel by multiplying a three by three matrix

((), M)

, as follows

([], \begin{matrix} X \\ Y \\ Z \end{matrix}) goodbreak= M ([], \begin{matrix} R \\ G \\ B \end{matrix})

where

M goodbreak= ([], \begin{matrix} 0.4125 & 0.3576 & 0.1804 \\ 0.2127 & 0.7152 & 0.0722 \\ 0.0193 & 0.1192 & 0.9503 \end{matrix})

…”

Section: Methodsmentioning

confidence: 99%

Prediction of the ripening stages of papayas using discriminant analysis and support vector machine algorithms

et al. 2021

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BACKGROUND: Evaluation of the quality properties of papaya becomes essential due to the acceleration of the fruit shelf-life senescence and the deterioration factor of the expected postharvest operations. In this study, the colour features in RGB, normalised RGB, HSV and L*a*b* channels were extracted and correlated with mechanical properties, moisture content (MC), total soluble solids (TSS) and pH for the prediction of quality properties at five ripening stages of papaya (R1-R5). RESULTS: The mean values of colour features in RGBwere the best estimator for predicting TSS with R 2 ≥ 0.90. All colour channels also showed satisfactory accuracies of R 2 ≥ 0.80 in predicting the bioyield force, apparent modulus and mean force. The highest average classification accuracy was obtained using linear discriminant analysis (LDA) with an average accuracy of more than 82%. The study showed that LDA, linear support vector machine, quadratic discriminant analysis and quadratic support vector machine obtained the correct classification of up to 100% for R5, whereas R1, R2, R3 and R4 gave classification accuracies in the range 83. 75-91.85%, 85.6-90.25%, 85.75-90.85% and 77.35-87.15%, respectively. This indicates that R5 colour information was obviously different from R1-R4. The mean values of the HSV channel indicated the best performance to predict the ripening stages of papaya, compared to RGB, normalised RGB and L*a*b* channels, with an average classification accuracy of more than 80%. CONCLUSION:The study has shown the versatility of a machine vision system in predicting the quality changes in papaya. The results showed that the machine vision system can be used to predict the ripening stages as well as classifying the fruits into different ripening stages of papayas.

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((), M)

, as follows

([], \begin{matrix} X \\ Y \\ Z \end{matrix}) goodbreak= M ([], \begin{matrix} R \\ G \\ B \end{matrix})

where

M goodbreak= ([], \begin{matrix} 0.4125 & 0.3576 & 0.1804 \\ 0.2127 & 0.7152 & 0.0722 \\ 0.0193 & 0.1192 & 0.9503 \end{matrix})

…”

Section: Methodsmentioning

confidence: 99%

Prediction of the ripening stages of papayas using discriminant analysis and support vector machine algorithms

et al. 2021

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“…In Indonesia, agriculture plays a vital role in the country's economy [8][9]. In Indonesia, various kinds of fruit are produced, such as mango, salak, orange, banana, watermelon, and many more [10]. However, sometimes the process of determining fruit ripeness is still done by using the manual method.…”

Section: Table Of Contentmentioning

confidence: 99%

“…The CNN model was used to classify these strawberries' ripeness and managed to achieve an accuracy rate of 91.6%. Pardede et al [10] researched to classify the ripeness of various fruits, such as apple, mango, orange, and tomato. Color features, such as RGB, HSV, HSL, and L * a * b * were used, and SVM as the classifier.…”

Section: Literature Reviewmentioning

confidence: 99%

Banana Ripeness Classification Based on Deep Learning using Convolutional Neural Network

Saragih

Emanuel

2021

2021 3rd East Indonesia Conference on Computer and Information Technology (EIConCIT)

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Fruit ripeness is an important thing in agriculture because it determines the fruit's quality. Determining the ripeness of the fruit that was done manually poses several weaknesses, such as takes a relatively long time, requires a lot of labor, and can cause inconsistencies. The agricultural sector is one of the important sectors of the economy in Indonesia. However, sometimes the process of determining fruit ripeness is still done by using the manual method. The development of computer vision and machine learning technologies can be used to classify fruit ripeness automatically. This study applies the Convolutional Neural Network to classify the ripeness of the banana. The banana's ripeness is divided into four classes: unripe/green, yellowish-green, mid-ripen, and overripe. Two pre-trained models are used, which are MobileNet V2 and NASNetMobile. The experiment was conducted using Google Colab and several libraries such as OpenCV, Tensorflow, and scikit-learn. The result shows that MobileNet V2 achieves higher accuracy and faster execution time than the NASNetMobile. The highest accuracy achieved is 96.18%.

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“…The HSV method also has an excellent ability to segment color images that have noise. HSV's accuracy is better in segmenting images based on color than RGB, HSL, and L*A*B [18]. HSV calculation can be done using (1).…”

Section: Search For Candidate Image Backgroundmentioning

confidence: 99%

“…HSV can segment images based on color by utilizing the upper and lower values of the HSV value so that objects and backgrounds are separated and can segment noisy color images. HSV accuracy is also better in segmenting color images than red, green, dan blue (RGB), HSL, and L*A*B [18]. This study aims to provide a solution in real-time parking detection using a combination of background subtraction HSV segmentation methods to determine the background image.…”

Section: Introductionmentioning

confidence: 99%

Parking detection system using background subtraction and HSV color segmentation

et al. 2021

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Manual system vehicle parking makes finding vacant parking lots difficult, so it has to check directly to the vacant space. If many people do parking, then the time needed for it is very much or requires many people to handle it. This research develops a real-time parking system to detect parking. The system is designed using the HSV color segmentation method in determining the background image. In addition, the detection process uses the background subtraction method. Applying these two methods requires image preprocessing using several methods such as grayscaling, blurring (low-pass filter). In addition, it is followed by a thresholding and filtering process to get the best image in the detection process. In the process, there is a determination of the ROI to determine the focus area of the object identified as empty parking. The parking detection process produces the best average accuracy of 95.76%. The minimum threshold value of 255 pixels is 0.4. This value is the best value from 33 test data in several criteria, such as the time of capture, composition and color of the vehicle, the shape of the shadow of the object’s environment, and the intensity of light. This parking detection system can be implemented in real-time to determine the position of an empty place.

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Fruit Ripeness Based on RGB, HSV, HSL, Lab* Color Feature Using SVM

Cited by 30 publications

References 17 publications

Prediction of the ripening stages of papayas using discriminant analysis and support vector machine algorithms

Prediction of the ripening stages of papayas using discriminant analysis and support vector machine algorithms

Banana Ripeness Classification Based on Deep Learning using Convolutional Neural Network

Parking detection system using background subtraction and HSV color segmentation

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