CropCount3D: A Multi-Modal Dataset for Real-Time 3D Crop Counting in Densely Planted Orchards

Description CropCount3D is a publicly available dataset containing synchronized camera, LiDAR, and IMU measurements acquired in densely planted orchard environments—including both greenhouses and open fields—for the purpose of on-the-fly crop counting. Designed to facilitate research in real-time 3D crop counting under challenging agricultural conditions, the dataset captures realistic field scenarios characterized by dense foliage, occlusion, and irregular planting. It includes keyframe-wise crop count ground truth, enabling frame-level evaluation of crop counting algorithms without requiring instance-level annotations. CropCount3D serves as a benchmark for assessing the accuracy, consistency, and robustness of multi-modal, real-time crop counting systems in unstructured environments. Sensor Platform The dataset was collected using a handheld multi-sensor rig designed for real-time crop counting in densely planted settings. The platform rigidly mounts a monocular RGB camera, a 3D LiDAR, and an IMU. All data streams are temporally synchronized and spatially calibrated. Camera: FLIR Blackfly (FL3-U3-1324C-C) with Edmund Optics 86900 lens LiDAR: Livox Avia IMU: BMI088 (built-in with LiDAR) Motion Patterns The sensor rig was manually operated through the orchard environments, following three representative motion patterns designed to reflect common real-world scanning behaviors: Revisit: Back-and-forth traversal simulating revisit scenarios prone to double-counting (Collected in open-field settings) Circular: Rotational scanning around a single tree in spatially constrained environments. (Collected in greenhouse settings.) Straight: Linear forward motion typically observed in row-wise crop arrangements. (Collected in greenhouse settings.) Dataset Structure The CropCount3D dataset is organized by sequence, where each sequence represents a distinct scanning session. Each sequence contains synchronized multi-sensor data and corresponding keyframe-wise cumulative crop count ground truth. Each ROS1 bag file ({$sequence_name}.bag) in the dataset/ directory contains raw sensor measurements for a specific sequence, recorded under the following topics: /camera/image_color: RGB images, 1280×1024×3 /livox/lidar: LiDAR point clouds /livox/imu: IMU data Calibration files are provided in the calibration/ directory and include the following: camera_intrinsics.yaml: Intrinsic parameters (focal length, principal point, distortion coefficients), obtained using the ROS camera_calibration tool. lidar_camera_extrinsics.yaml: 6DoF transformation from LiDAR to camera coordinate frame, obtained using the livox_camera_calib. Crop count annotations are provided in the crop_count/ directory as follows: {$sequence_name}_crop_counts.csv: Per-keyframe cumulative ground truth crop counts for each sequence. Detection weights are provided in the weight/ directory: yolov5s_seg.pt: Pretrained YOLOv5s-segmentation model weights. Compatible with Pytorch 2.0.1. yolov5s_seg.onnx: Pretrained YOLOv5s segmentation model weights, converted to ONNX format for deployment. Compatible with ONNX Runtime GPU version 1.12.0 and CUDA 11.4. Citation Please cite the following paper when using this dataset in your work.[ANONYMIZED FOR REVIEW] License Information The CropCount3D dataset is released under a Creative Commons Attribution 4.0 International License, CC BY 4.0 Contact Information If you have any issues about the CropCount3D dataset, please contact us at [ANONYMIZED EMAIL].