ORANGE RECOGNITION ON TREE USING IMAGE PROCESSING METHOD BASED ON LIGHTING DENSITY PATTERN

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

AHMADI H.R.; AMIRI PARIAN J.

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: JOURNAL OF AGRICULTURAL MACHINERY Year:2015 | Volume:5 | Issue:1 Page(s): 92-100

Download Full-Text

Persian Verion

View:

755

Download:

Cites:

Information Journal Paper

Title

ORANGE RECOGNITION ON TREE USING IMAGE PROCESSING METHOD BASED ON LIGHTING DENSITY PATTERN

Author(s)

AHMADI H.R. | AMIRI PARIAN J. | Issue Writer Certificate

Keywords

ORANGE HARVESTINGQ2

IMAGE PROCESSINGQ2

SEPARATIONQ2

HARVESTING ROBOTQ2

MACHINE VISIONQ2

REGIONAL MAXIMAQ2

Abstract

Within the last few years, a new tendency has been created towards robotic harvesting of oranges and some of citrus fruits. The first step in robotic harvesting is accurate recognition and positioning of fruits. Detection through IMAGE PROCESSING by color cameras and computer is currently the most common method. Obviously, a HARVESTING ROBOT faces with natural conditions and, therefore, detection must be done in various light conditions and environments. In this study, it was attempted to provide a suitable algorithm for recognizing the orange fruits on tree. In order to evaluate the proposed algorithm, 500 images were taken in different conditions of canopy, lighting and the distance to the tree. The algorithm included sub-routines for optimization, segmentation, size filtering, SEPARATION of fruits based on lighting density method and coordinates determination. In this study, MLP neural network (with 3 hidden layers) was used for segmentation that was found to be successful with an accuracy of 88.2% in correct detection. As there exist a high percentage of the clustered oranges in images, any algorithm aiming to detect oranges on the trees successfully should offer a solution to separate these oranges first. A new method based on the light and shade density method was applied and evaluated in this research.Finally, the accuracies for differentiation and recognition were obtained to be 89.5% and 88.2%, respectively.

Cites

No record.

References

No record.

Cite

APA: Copy

AHMADI, H.R., & AMIRI PARIAN, J.. (2015). ORANGE RECOGNITION ON TREE USING IMAGE PROCESSING METHOD BASED ON LIGHTING DENSITY PATTERN. JOURNAL OF AGRICULTURAL MACHINERY, 5(1), 92-100. SID. https://sid.ir/paper/201472/en

Vancouver: Copy

AHMADI H.R., AMIRI PARIAN J.. ORANGE RECOGNITION ON TREE USING IMAGE PROCESSING METHOD BASED ON LIGHTING DENSITY PATTERN. JOURNAL OF AGRICULTURAL MACHINERY[Internet]. 2015;5(1):92-100. Available from: https://sid.ir/paper/201472/en

IEEE: Copy

H.R. AHMADI, and J. AMIRI PARIAN, “ORANGE RECOGNITION ON TREE USING IMAGE PROCESSING METHOD BASED ON LIGHTING DENSITY PATTERN,” JOURNAL OF AGRICULTURAL MACHINERY, vol. 5, no. 1, pp. 92–100, 2015, [Online]. Available: https://sid.ir/paper/201472/en

Related Journal Papers