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פותח על ידי קלירמאש פתרונות בע"מ -
Immature green citrus fruit detection using color and thermal images
Year:
2018
Authors :
אלחנתי, ויקטור
;
.
Volume :
152
Co-Authors:

Gan, H., Department of Agricultural and Biological Engineering, University of Florida, Gainesville, FL, United States; Lee, W.S., Department of Agricultural and Biological Engineering, University of Florida, Gainesville, FL, United States;  Ehsani, R., Department of Mechanical Engineering, University of California, Merced, Merced, CA, United States; Schueller, J.K., Department of Agricultural and Biological Engineering, University of Florida, Gainesville, FL, United States, Department of Mechanical & Aerospace Engineering, University of Florida, Gainesville, FL, United States

Facilitators :
From page:
117
To page:
125
(
Total pages:
9
)
Abstract:

Citrus fruit detection is one of the most important and challenging steps in citrus yield mapping. The distinct color differences between the ripe fruit and leaves allowed previously-described imaging-based methods to achieve good results. However, immature green citrus fruit detection, which aims to provide valuable information for citrus yield mapping at earlier stages is much more difficult because the fruit and leaf colors are very similar. This study combines color and thermal images for immature green fruit detections. Experiments identified optimal conditions for thermal imaging. A multimodal imaging platform was built to integrate color and thermal cameras. A novel image registration method was developed for combining color and thermal images and matching fruit in both images which achieved pixel-level accuracy. A new Color-Thermal Combined Probability (CTCP) algorithm was created to effectively fuse information from the color and thermal images to classify potential image regions into fruit and non-fruit classes. Algorithms were also developed to integrate image registration, information fusion and fruit classification and detection into a single step for real-time processing. An increase in recall rate from 78.1% when using only color images to 90.4% after fusing the color and thermal images was obtained at similar precision rates, and an increase in precision rate from 86.6% to 95.5% was obtained at similar recall rates. The fusion of the color and thermal images effectively improved immature green citrus fruit detection. © 2018 Elsevier B.V.

Note:
Related Files :
Citrus fruits
Classification (of information)
color
image classification
Image registration
Infrared imaging
Mapping
Yield mapping
עוד תגיות
תוכן קשור
More details
DOI :
10.1016/j.compag.2018.07.011
Article number:
0
Affiliations:
Database:
סקופוס
Publication Type:
מאמר
;
.
Language:
אנגלית
Editors' remarks:
ID:
35487
Last updated date:
02/03/2022 17:27
Creation date:
24/07/2018 13:07
Scientific Publication
Immature green citrus fruit detection using color and thermal images
152

Gan, H., Department of Agricultural and Biological Engineering, University of Florida, Gainesville, FL, United States; Lee, W.S., Department of Agricultural and Biological Engineering, University of Florida, Gainesville, FL, United States;  Ehsani, R., Department of Mechanical Engineering, University of California, Merced, Merced, CA, United States; Schueller, J.K., Department of Agricultural and Biological Engineering, University of Florida, Gainesville, FL, United States, Department of Mechanical & Aerospace Engineering, University of Florida, Gainesville, FL, United States

Immature green citrus fruit detection using color and thermal images

Citrus fruit detection is one of the most important and challenging steps in citrus yield mapping. The distinct color differences between the ripe fruit and leaves allowed previously-described imaging-based methods to achieve good results. However, immature green citrus fruit detection, which aims to provide valuable information for citrus yield mapping at earlier stages is much more difficult because the fruit and leaf colors are very similar. This study combines color and thermal images for immature green fruit detections. Experiments identified optimal conditions for thermal imaging. A multimodal imaging platform was built to integrate color and thermal cameras. A novel image registration method was developed for combining color and thermal images and matching fruit in both images which achieved pixel-level accuracy. A new Color-Thermal Combined Probability (CTCP) algorithm was created to effectively fuse information from the color and thermal images to classify potential image regions into fruit and non-fruit classes. Algorithms were also developed to integrate image registration, information fusion and fruit classification and detection into a single step for real-time processing. An increase in recall rate from 78.1% when using only color images to 90.4% after fusing the color and thermal images was obtained at similar precision rates, and an increase in precision rate from 86.6% to 95.5% was obtained at similar recall rates. The fusion of the color and thermal images effectively improved immature green citrus fruit detection. © 2018 Elsevier B.V.

Scientific Publication
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