Detection of Trees and Objects in Apple Orchard from LiDAR Point Cloud Data Using a YOLOv5 Framework
Abstract
1. Introduction
2. Related Works
- The proposed detection pipeline, including pre-processing and training, would optimize for deployment on resource-constrained platforms such as UAVs or mobile agricultural robots, making it suitable for real-time orchard monitoring.
- The model was trained and validated using real-world data collected from a commercial apple orchard, accounting for occlusion, class imbalance, and visual ambiguity, which improves robustness under field conditions.
- The study provides a performance comparison of the proposed method with other object detection models, including SSD, YOLOv3–v7, and transformer-based approaches, supporting the justification for adopting YOLOv5 in precision agriculture.
- The findings suggest economic feasibility by reducing the manual labor and processing time. Moreover, the detection framework was shown to be scalable and transferable to other fruit orchards (e.g., pear, orange, peach, and persimmon) through minimal retraining.
3. Materials and Methods
3.1. Data Collection Site
3.2. Sensor Selection and Data Collection
3.3. Data Pre-Processing Procedure
3.4. YOLOv5 Model
3.5. Model Performance Matrices
3.6. Dataset Preparation and Training Process
4. Results
4.1. Training Outputs on Key Performance Indicators
4.2. Object Detection Performance
5. Discussion
5.1. Implications for Theory
5.2. Implications for Practice
5.3. Implications for Economic Benefit
5.4. Scalability and Cross-Crop Applicability
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Parameter | Specifications |
---|---|
Channels | 16 |
Range of measurement | Up to 100 m |
Range accuracy | ±3 cm (typical) |
HFOV | 360° |
VFOV | 30° (+15.0° to −15.0°) |
Angular Resolution (vertical) | 2.0° |
Angular Resolution (horizontal) | 0.1–0.4° |
Rotation rate | 5–20 Hz |
Wavelength (laser) | 903 nm |
Power (consumption) | 8 W (typical) |
Voltage (operating) | 9–18 V |
Weight | ~830 g |
Temperature (operating) | −10 °C to +60 °C |
Temperature (storage) | −40 °C to +105 °C |
Data interface | 100 Mbps ethernet |
Data format | UDP packets with ToF, distance, rotation angles, and synchronized timestamps (µs resolution) |
Measurement rate | 300,000 points/s (single return), 600,000 points/s (dual return) |
Epochs | GPU Memory | Box Loss | Objectness_Loss | Class Loss | Instances | Size |
---|---|---|---|---|---|---|
100/100 | 12.6 GB | 0.025 | 0.028 | 0.001 | 96 | 1216 × 1216 |
Class | Images | Instances | P | R | mAP@0.5 | mAP@0.5:0.95 |
All | 300 | 949 | 0.903 | 0.871 | 0.892 | 0.478 |
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Karim, M.R.; Reza, M.N.; Ahmed, S.; Lee, K.-H.; Sung, J.; Chung, S.-O. Detection of Trees and Objects in Apple Orchard from LiDAR Point Cloud Data Using a YOLOv5 Framework. Electronics 2025, 14, 2545. https://doi.org/10.3390/electronics14132545
Karim MR, Reza MN, Ahmed S, Lee K-H, Sung J, Chung S-O. Detection of Trees and Objects in Apple Orchard from LiDAR Point Cloud Data Using a YOLOv5 Framework. Electronics. 2025; 14(13):2545. https://doi.org/10.3390/electronics14132545
Chicago/Turabian StyleKarim, Md Rejaul, Md Nasim Reza, Shahriar Ahmed, Kyu-Ho Lee, Joonjea Sung, and Sun-Ok Chung. 2025. "Detection of Trees and Objects in Apple Orchard from LiDAR Point Cloud Data Using a YOLOv5 Framework" Electronics 14, no. 13: 2545. https://doi.org/10.3390/electronics14132545
APA StyleKarim, M. R., Reza, M. N., Ahmed, S., Lee, K.-H., Sung, J., & Chung, S.-O. (2025). Detection of Trees and Objects in Apple Orchard from LiDAR Point Cloud Data Using a YOLOv5 Framework. Electronics, 14(13), 2545. https://doi.org/10.3390/electronics14132545