DOI: 10.5061/dryad.bzkh189nw
title: Ground and aerial imagery dataset for strawberry breeding trials: Training deep learning models for runner detection and segmentation

description: Runners play a crucial role in vegetative propagation and provide a
 cost-effective method for expanding strawberry production, excessive
 growth presents significant challenges. Uncontrolled runner development
 diverts energy from the mother plant, leading to smaller, lower-quality
 fruit and ultimately reducing overall yield. Accurate and efficient
 identification of strawberry runners is essential for optimizing crop
 management and propagation. This dataset was prepared to enhance the
 automation of strawberry runner identification in field environments by
 developing a deep learning-based approach. A diverse dataset of strawberry
 images was collected, encompassing a wide range of varieties, different
 growth stages, and multiple growing seasons. Images were captured using
 three different platforms and cameras at varying altitudes to improve
 dataset diversity: 0.5 m above the ground level (AGL) for ground imaging
 (GI), 10 m AGL for aerial imaging (AI10), and 5 m AGL for aerial imaging
 (AI5). All raw images were preprocessed to segment individual plants. out
 and saved. Runners were annotated using polygonal masks to delineate their
 boundaries, with a single class designated for runner identification.
description: We collected data from strawberry breeding trials conducted at
 the University of Florida (UF) Gulf Coast Research and Education Center
 (GCREC) in Wimauma, Florida (27° 45' 40" N, 82° 13'
 49" W). Videos and images were collected from two separate trials
 conducted over two consecutive growing seasons (Table 1). Each trial
 included multiple strawberry genotypes, with replicated plantings to
 ensure experimental consistency (Table 1). Table 1.
 Number of strawberry genotypes and plants used for image collection in
 different trials across two consecutive field seasons.
 
 Field Seasons 
 Trials 
 Genotypes 
 Plants 
 2023 -2024 
 Clonal 420
 442 
 2024 - 2025 
 Density 
 16 1,549
 
   Video Collection Using Ground Imaging (GI)
 Platform 4K videos at 60 fps were collected using an
 RGB camera (24MP, full-frame, EOS R8, Canon, Japan) mounted onto a ground
 vehicle (Amiga, Farm-Ng, CA, US). The camera was mounted on a bar at
 approximately 0.5 m AGL, positioned about 0.3 m above the plant canopy,
 and angled 45 degrees down from the horizon, facing forward in alignment
 with the vehicle’s movement direction. All the videos were collected
 during the 2023-24 strawberry season and processed using the Python script
 <em>ground_video_preprocessing.py</em>, which trims the
 footage to remove background to retain a single plant in view, extracts
 high-resolution frames from the video, and saves images for
 annotation. Image Collection Using Aerial Imaging
 Platform at 10 m AGL (AI10) A quad-copter unmanned
 aerial vehicle (UAV, EVO-II, Autel Robotics, WA, US) equipped with an RGB
 camera (20MP 1-inch CMOS, Sony, Japan) was used to capture strawberry
 plant images at 10 m AGL from the same clonal trials as the GI during the
 2023-24 strawberry season, aiming to enhance data collection efficiency.
 The onboard camera was downward-facing, capturing top-view images of the
 strawberry plants. These images were then processed using a Python script
 <em>UAV_image_preprocessing.py</em> to segment out each
 individual strawberry plant for annotation. Image
 Collection Using Aerial Imaging Platform at 5 m AGL (AI5)
 To balance image resolution and data acquisition efficiency, we
 utilized another UAV (Mavic Pro 3, DJI, China) equipped with an imaging
 sensor array to capture images at 5 m AGL. A different UAV was selected
 for AI5 compared to AI10, primarily due to the superior performance of its
 main imaging sensor (20 MP, 4/3-inch CMOS, Hasselblad, Sweden). The
 onboard camera was downward-facing, capturing top-view images of the
 strawberry plants. Using this platform, we collected mid-resolution aerial
 images from density trials conducted during the 2024-25 strawberry season.
 Unlike images collected by GI and AI10, these mid-resolution aerial images
 were only collected during the early growth stage, when the number of
 runners typically reaches its peak. These images were processed the same
 way as the images from AI10. Table 2. Summary of Data
 Collection Platforms and Data Collection Date and Time
 
 Imaging Platform 
 Carrier Imaging
 Sensor Imaging
 Altitude Raw Image
 Resolution Data Collection
 Date Data Collection
 Time 
 GI Amiga,
 Farm-Ng 24 MP, full-frame, EOS
 R8, Cannon 0.5 m AGL
 3,840 × 2,160 
 11/29/2023 12/20/2023
 1/29/2024 2/19/2024 
 Between 10:00 and 13:00 
 AI10 
 EVO II Pro, Autel Robotics 
 20 MP, 1-inch CMOS, Sony 
 10 m AGL 
 5,472 × 3,648 
 11/29/2023 12/19/2023
 1/31/2024 2/20/2024 
 Between 11:00 and 13:00 
 AI5 
 Mavic Pro 3, DJI 
 20 MP, 4/3-inch CMOS, Hasselblad 
 5 m AGL 5,272 ×
 3,948 12/16/2024
 Between 12:30 and 13:00
 
   Image Annotation and Augmentation
 Runners were annotated using polygons on all the preprocessed
 video frames and images. All the training data were augmented by applying
 horizontal flipping and 90-degree rotations (none, clockwise, or
 counterclockwise) to increase dataset diversity. Table
 3. Training and Validation Data Sets Summary 
 
 Platform Training Data
 (Images of Individual Plants)  
 Validation Data (Images of Individual Plants) 
 GI 
 1,013 
 106 
 AI10  1,019
 105 
 AI5  
 1,004 99
 GI, AI10, and
 AI5  3,036 
 309 
 For ground videos, the proportions of
 the left and right soil borders varied slightly, resulting in minor
 differences in the dimensions of the cropped videos. However, the selected
 video frames were approximately 1,464 × 1,152 pixels in size. The
 segmented aerial images captured from 10 m and 5 m had resolutions of 218
 × 218 pixels and 392 × 392 pixels, respectively.
description: # Ground and aerial imagery dataset for strawberry breeding trials:
 Training deep learning models for runner detection and segmentation
 Dataset DOI: [10.5061/dryad.bzkh189nw](10.5061/dryad.bzkh189nw) **Usage
 Notes** The GI.zip contains images and corresponding annotation files for
 ground images extracted from processed ground videos. The AI5.zip contains
 images and corresponding annotation files for processed aerial images
 captured at 5 m above the ground level. The AI10.zip contains images and
 corresponding annotation files for processed aerial images captured at 10
 m above the ground level. The GI_AI5_AI10.zip combined all the images and
 corresponding annotation files from GI, AI5, and AI10. The folder
 structure is the same for GI.zip, AI5.zip, AI10.zip, and GI_AI5_AI10.zip.
 For example, this is the folder structure for GI.zip. GI/ ├── train/ │  
 ├── images/ │   │   ├── img1.jpg │   │   ├── img2.jpg │   │   └── ... │  
 ├── labels/ │   │   ├── img1.txt       # Polygon annotation file for the
 corresponding image in the images folder │   │   └── ... ├── val/ │   ├──
 images/ │   │   ├── img1.jpg │   │   ├── img2.jpg │   │   └── ... │   ├──
 labels/ │   │   ├── img1.txt # Polygon annotation file for the
 corresponding image in the images folder │   │   └── ... ├── classes
                     # Annotated classes for all the images ├──
 data.yaml                # Dataset configuration file ## Code/software You
 can find the two Python scripts here:
 [https://github.com/Xue-Zhou/Ground_UAV_Data_Preprocessing.git](https://github.com/Xue-Zhou/Ground_UAV_Data_Preprocessing.git)