A Novel Dataset for Fine-Grained Phenotyping
Observer bias and inconsistencies in traditional plant phenotyping methods limit the accuracy and re- producibility of fine-grained plant analysis. To overcome these challenges, we developed TomatoMAP, a comprehensive dataset for Solanum lycopersicum using an Internet of Things (IoT) based imaging system with standardized data acquisition protocols. Our dataset contains 64,464 RGB images that capture 12 different plant poses from four camera elevation angles. Each image includes manually annotated bounding boxes for seven regions of interest (ROIs), including leaves, panicle, batch of flowers, batch of fruits, axillary shoot, shoot and whole plant area, along with 50 fine-grained growth stage classifications based on the BBCH scale. Additionally, we provide 3,616 high-resolution image subset with pixel-wise semantic and instance segmentation annotations for fine-grained phenotyping. We validated our dataset using a cascading model deep learning framework combining MobileNetv3 for classification, YOLOv11 for object detection, and MaskRCNN for segmentation. Through AI vs. Human analysis involving five domain experts, we demonstrate that the models trained on our dataset achieve accuracy and speed comparable to the experts. Cohen's Kappa and inter-rater agreement heatmap confirm the reliability of automated fine-grained phenotyping using our approach.
Keywords: phenotyping, fine-grained, Solanum lycopersicum, Internet of Things, cascading model, AI vs. Human
Multi-pose protocol with 12 different poses
Multi-angle protocol with 4 camera angles
3D topology potiential for 3D rendering
IoT in Greenhouse
Depth prediction on Drone
Fine-grained phenotyping
The imaging station comprised four OV5647 color CMOS 5-megapixel image sensors: three equipped with 90° lenses and one equipped with a 170° fisheye lens. These cameras were mounted at vertical inclination angles of 45°, 90°， 135°, and 180°, each featuring an adjustable focal length and an aperture of F/2.2, thereby enabling comprehensive coverage of the entire plant structure at close range.
@misc{zhang2025tomatomap,
title={Tomato Multi-Angle Multi-Pose Dataset for Fine-Grained Phenotyping},
author={Yujie Zhang and Sabine Struckmeyer and Andreas Kolb and Sven Reichardt},
year={2026},
journal={Sci Data},
doi={10.1038/s41597-026-06926-9},
}
@dataset{tomatomap,
title={TomatoMAP: Tomato Multi-Angle Multi-Pose Dataset for Fine-Grained Phenotyping},
author={Yujie Zhang and Sabine Struckmeyer and Andreas Kolb and Sven Reichardt},
journal={e!DAL-Plant Genomics and Phenomics Research Data Repository (PGP)},
year={2025},
doi={10.5447/ipk/2025/14}
}