DOI: 10.5281/zenodo.15893214
title: CropCount3D: A Multi-Modal Dataset for Real-Time 3D Crop Counting in Densely Planted Orchards

description: 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 frame-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 (3 sequences collected both in open-field and greenhouse settings)




Circular: Rotational scanning around a single tree in spatially constrained environments. (2 sequences collected in greenhouse settings.)




Straight: Linear forward motion, typically observed in row-wise crop arrangements. (3 sequences collected both in open-field and greenhouse settings.)



The trajectory for each sequence is provided in the corresponding image files (.png).

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:






yolov11s_seg.pt: Pretrained YOLOv11s-segmentation model weights. Compatible with Pytorch 2.0.1.

yolov11s_seg.onnx: Pretrained YOLOv11s segmentation model weights, converted to ONNX format for deployment. Compatible with ONNX Runtime GPU version 1.15.0 and CUDA 11.4.



Dataset Acquisition Time

Data acquisition times are as follows:



Revisit1: 2024-12-19 AM

Revisit2: 2024-12-19 AM

Revisit3: 2024-12-20 PM

Circular1: 2024-01-31 PM

Circular2: 2024-02-01 AM

Straight1: 2024-01-31 PM

Straight2: 2024-02-01 AM

Straight3: 2024-12-20 AM


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].