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Agriculture
Volume 15
Issue 11
10.3390/agriculture15111148
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Advanced 3D Depth Imaging Techniques for Morphometric Analysis of Detected On-Tree Apples Based on AI Technology

by
Eungchan Kim
1,2,3,
Sang-Yeon Kim
1,3,
Chang-Hyup Lee
1,3,
Sungjay Kim
1,
Jiwon Ryu
1,3,
Geon-Hee Kim
4,
Seul-Ki Lee
5 and
Ghiseok Kim
1,2,3,*
1
Department of Biosystems Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
2
Global Smart Farm Convergence Major, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
3
Research Institute of Agriculture and Life Sciences, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
4
Department of Mechanical Materials Convergence System Engineering, Hanbat National University, Daejeon 34158, Republic of Korea
5
Fruit Research Division, National Institute of Horticultural and Herbal Science, Wanju 55365, Republic of Korea
*
Author to whom correspondence should be addressed.
Agriculture 2025, 15(11), 1148; https://doi.org/10.3390/agriculture15111148
Submission received: 11 April 2025 / Revised: 15 May 2025 / Accepted: 26 May 2025 / Published: 27 May 2025
(This article belongs to the Special Issue Smart Agriculture Sensors and Monitoring Systems for Field Detection)
Versions Notes

Abstract

This study developed non-destructive technology for predicting apple size to determine optimal harvest timing of field-grown apples. RGBD images were collected in field environments with fluctuating light conditions, and deep learning techniques were integrated to analyze morphometric parameters. After training various models, the EfficientDet D4 and Mask R-CNN ResNet101 models demonstrated the highest detection accuracy. Morphometric metrics were measured by linking boundary box information with 3D depth information to determine horizontal and vertical diameters. Without occlusion, mean absolute percentage error (MAPE) using boundary box-based methods was 6.201% and 5.164% for horizontal and vertical diameters, respectively, while mask-based methods achieved improved accuracy with MAPE of 5.667% and 4.921%. Volume and weight predictions showed MAPE of 7.183% and 6.571%, respectively. For partially occluded apples, amodal segmentation was applied to analyze morphometric parameters according to occlusion rates. While conventional models showed increasing MAPE with higher occlusion rates, the amodal segmentation-based model maintained consistent accuracy regardless of occlusion rate, demonstrating potential for automated harvest systems where fruits are frequently partially obscured by leaves and branches.
Keywords: 3D depth imaging; artificial intelligence; morphometric analysis; object detection; instance segmentation; amodal segmentation; multivariate regression 3D depth imaging; artificial intelligence; morphometric analysis; object detection; instance segmentation; amodal segmentation; multivariate regression

Share and Cite

MDPI and ACS Style

Kim, E.; Kim, S.-Y.; Lee, C.-H.; Kim, S.; Ryu, J.; Kim, G.-H.; Lee, S.-K.; Kim, G. Advanced 3D Depth Imaging Techniques for Morphometric Analysis of Detected On-Tree Apples Based on AI Technology. Agriculture 2025, 15, 1148. https://doi.org/10.3390/agriculture15111148

AMA Style

Kim E, Kim S-Y, Lee C-H, Kim S, Ryu J, Kim G-H, Lee S-K, Kim G. Advanced 3D Depth Imaging Techniques for Morphometric Analysis of Detected On-Tree Apples Based on AI Technology. Agriculture. 2025; 15(11):1148. https://doi.org/10.3390/agriculture15111148

Chicago/Turabian Style

Kim, Eungchan, Sang-Yeon Kim, Chang-Hyup Lee, Sungjay Kim, Jiwon Ryu, Geon-Hee Kim, Seul-Ki Lee, and Ghiseok Kim. 2025. "Advanced 3D Depth Imaging Techniques for Morphometric Analysis of Detected On-Tree Apples Based on AI Technology" Agriculture 15, no. 11: 1148. https://doi.org/10.3390/agriculture15111148

APA Style

Kim, E., Kim, S.-Y., Lee, C.-H., Kim, S., Ryu, J., Kim, G.-H., Lee, S.-K., & Kim, G. (2025). Advanced 3D Depth Imaging Techniques for Morphometric Analysis of Detected On-Tree Apples Based on AI Technology. Agriculture, 15(11), 1148. https://doi.org/10.3390/agriculture15111148

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