Detection of Trees and Objects in Apple Orchard from LiDAR Point Cloud Data Using a YOLOv5 Framework

Object detection is crucial for smart apple orchard management using agricultural machinery to avoid obstacles. The objective of this study was to detect apple trees and other objects in an apple orchard using LiDAR and the YOLOv5 algorithm. A commercial LiDAR was attached to a tripod to collect apple tree trunk data, which were then pre-processed and converted into PNG images. A pre-processed set of 1500 images was manually annotated with bounding boxes and class labels (trees, water tanks, and others) to train and validate the YOLOv5 object detection algorithm. The model, trained over 100 epochs, resulted in 90% precision, 87% recall, [email protected] of 0.89, and [email protected]:0.95 of 0.48. The accuracy reached 89% with a low classification loss of 0.001. Class-wise accuracy was high for water tanks (96%) and trees (95%), while the “others” category had lower accuracy (82%) due to inter-class similarity. Accurate object detection is challenging since the apple orchard environment is complex and unstructured. Background misclassifications highlight the need for improved dataset balance, better feature discrimination, and refinement in detecting ambiguous objects.