Image processing of an agriculture produce: Determination of size and ripeness of a banana

Mustafa, Nur Badariah Ahmad; Fuad, Nurashikin Ahmad; Ahmed, Syed Khaleel; Abidin, Aidil Azwin Zainul; Ali, Zaipatimah; Yit, Wong Bing; Sharrif, Zainul Abidin Md

doi:10.1109/itsim.2008.4631636

Cited by 53 publications

(31 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Subsequently, the selected image is converted to a binary image to distinguish the position of the markers clearly, and the centroid of each fiducial marker is determined by using the regionprops function implemented in MATLAB. 8 Finally, the distances for the x-and z-directions in images are calculated between the markers, and the longitudinal distance is also calculated between one of the markers and a boundary of the phantom. The calculated results are compared to the baseline data.…”

Section: B1 | Consistency Test For Geometric Distortionsmentioning

confidence: 99%

TomoMQA: Automated analysis program for MVCT quality assurance of helical tomotherapy

Han

Hong

Chang

et al. 2020

J Applied Clin Med Phys

View full text Add to dashboard Cite

Purpose: In this study, we developed a simple but useful computer program, called TomoMQA, to offer an automated quality assurance for mega-voltage computed tomography (MVCT) images generated via helical tomotherapy.Methods: TomoMQA is written in MATLAB and contains three steps for analysis:(a) open the DICOM dataset folder generated via helical tomotherapy (i.e., TomoTherapy ® and Radixact™), (b) call the baseline data for the consistency test and click the "Analysis" button (or click the "Analysis" button without the baseline data and export the results as the baseline data), and (c) print an analyzed report.The overall procedure for the QA analysis included in TomoMQA is referred from the TG-148 recommendation. Here, the tolerances for MVCT QA were implemented from TG-148 recommended values as default; however, it can be modified by a user manually. Results:To test the performance of the TomoMQA program, 15 MVCTs were prepared from five helical tomotherapy machines (1 of TomoTherapy ® HD, 2 of TomoTherapy ® HDA, and 2 of Radixact™) in 3 months and the QA procedures were performed using TomoMQA. From our results, the evaluation revealed that the developed program can successfully perform the MVCT QA analysis irrespective of the type of helical tomotherapy equipment. Conclusion:We successfully developed a new automated analysis program for MVCT QA of a helical tomotherapy platform, called TomoMQA. The developed program will be made freely downloadable from the TomoMQA-dedicated website.

show abstract

Section: B1 | Consistency Test For Geometric Distortionsmentioning

confidence: 99%

TomoMQA: Automated analysis program for MVCT quality assurance of helical tomotherapy

Han

Hong

Chang

et al. 2020

J Applied Clin Med Phys

View full text Add to dashboard Cite

show abstract

“…13 Skewness for undermature, mature and over-mature samples method which was proposed to be beneficial in banana post harvest processing industries in quality control. Mustafa et al (2008) suggested that the first step to determine the maturity of banana fruit is by recognizing the color parameter. This study confirmed earlier hypothesis and established that the mean color intensity value is a viable prediction feature to determine banana fruit maturity.…”

Section: Maturementioning

confidence: 99%

Assessment of banana fruit maturity by image processing technique

2013

View full text Add to dashboard Cite

Maturity stage of fresh banana fruit is an important factor that affects the fruit quality during ripening and marketability after ripening. The ability to identify maturity of fresh banana fruit will be a great support for farmers to optimize harvesting phase which helps to avoid harvesting either under-matured or over-matured banana. This study attempted to use image processing technique to detect the maturity stage of fresh banana fruit by its color and size value of their images precisely. A total of 120 images comprising 40 images from each stage such as under-mature, mature and over-mature were used for developing algorithm and accuracy prediction. The mean color intensity from histogram; area, perimeter, major axis length and minor axis length from the size values, were extracted from the calibration images. Analysis of variance between each maturity stage on these features indicated that the mean color intensity and area features were more significant in predicting the maturity of banana fruit. Hence, two classifier algorithms namely, mean color intensity algorithm and area algorithm were developed and their accuracy on maturity detection was assessed. The mean color intensity algorithm showed 99.1 % accuracy in classifying the banana fruit maturity. The area algorithm classified the under-mature fruit at 85 % accuracy. Hence the maturity assessment technique proposed in this paper could be used commercially to develop a field based complete automatic detection system to take decision on the right time of harvest by the banana growers.

show abstract

“…Mustafa et al propose a system for measuring banana perimeter and other geometrical information [66]. As it is not possible to compute real geometrical information using only camera information, they place a dollar coin on the same image of the evaluated banana.…”

Section: Related Workmentioning

confidence: 99%

A Wearable Mobile Sensor Platform to Assist Fruit Grading

Aroca

Gomes

Dantas

et al. 2013

Sensors

View full text Add to dashboard Cite

Wearable computing is a form of ubiquitous computing that offers flexible and useful tools for users. Specifically, glove-based systems have been used in the last 30 years in a variety of applications, but mostly focusing on sensing people's attributes, such as finger bending and heart rate. In contrast, we propose in this work a novel flexible and reconfigurable instrumentation platform in the form of a glove, which can be used to analyze and measure attributes of fruits by just pointing or touching them with the proposed glove. An architecture for such a platform is designed and its application for intuitive fruit grading is also presented, including experimental results for several fruits.

show abstract

Image processing of an agriculture produce: Determination of size and ripeness of a banana

Cited by 53 publications

References 2 publications

TomoMQA: Automated analysis program for MVCT quality assurance of helical tomotherapy

TomoMQA: Automated analysis program for MVCT quality assurance of helical tomotherapy

Assessment of banana fruit maturity by image processing technique

A Wearable Mobile Sensor Platform to Assist Fruit Grading

Contact Info

Product

Resources

About