A Grading Method for Orange Based on Computer Vision

Wu, Dong; Xiong, Qingrong; Kong, Fang; Pan, Zhi Bin; Deng, Xing

doi:10.4028/www.scientific.net/amm.195-196.1158

Cited by 2 publications

(2 citation statements)

References 4 publications

(3 reference statements)

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“…Traditional computer vision applications for agriculture have required optical sensors to capture images and then have them processed on an external server. Fruit grading systems have achieved high accuracy in real-world situations (Wu et al, 2012). However, these systems require multiple servers to compute images fast enough to perform real-time grading.…”

Section: Literature Review and Related Studiesmentioning

confidence: 99%

The Use of Computer Vision to Combat Losses from Disease in Grapevines

2023

CEIS

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The use of computer vision to support and automate agriculture and viticulture is increasing. Therefore, it is important to continuously test new technologies and equipment. Management of pests and diseases in viticulture is a labour-intensive task. This study aims to investigate current technologies in computer vision that could be applied to disease and pest detection in viticulture and the application of transfer learning on segmentation networks. This study also implements a case study and applies computer vision for disease and pest detection. Observation of limitations in the network's performance on testing images, after training on the limited data set, suggests that careful control is needed over lighting conditions in the image capture environment. Although initial results are positive, a larger training dataset is recommended to achieve a greater level of accuracy.

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Section: Literature Review and Related Studiesmentioning

confidence: 99%

The Use of Computer Vision to Combat Losses from Disease in Grapevines

2023

CEIS

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“…For example, the HSI togram of H level of an unripe orange usually has only one peak. Meanwhile that of a ripe one has at least two peaks [6]. Thus the standard deviation of H value indicates the mature degree.…”

Section: Applied Mechanics and Materials Vols 303-306mentioning

confidence: 99%

Computer Vision Based Orange Grading Using SVM

Pan

Wei

2013

AMM

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Fruit grading is very important for promoting its additional value. We graded oranges based on its images. Four photos were taken from different view angles for each orange. Both RGB and HSI color model were utilized. We extracted a 28-dimensional feature which can describe the size and color of them. Then support vector machine was used to grade these oranges into four levels. Experimental result shows SVM has promising performance for orange grading.

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A Grading Method for Orange Based on Computer Vision

Cited by 2 publications

References 4 publications

The Use of Computer Vision to Combat Losses from Disease in Grapevines

The Use of Computer Vision to Combat Losses from Disease in Grapevines

Computer Vision Based Orange Grading Using SVM

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