构建番茄多角度多姿态数据集，助力精准植物表型分析
Tomato Multi-Angle Multi-Pose Dataset for Fine-Grained Phenotyping
- 基于物联网系统采集6万+张番茄图像，覆盖12种姿态、4个拍摄角度
- 包含7个区域标注与50类生长阶段标签，3616张图具像素级分割
- 模型表现媲美专家，可提升表型分析效率与一致性
传统植物表型方法受观察者偏差和不一致性影响，难以实现精细分析。为此，我们构建了基于物联网成像系统的番茄多角度多姿态（TomatoMAP）数据集，用于栽培番茄（Solanum lycopersicum）。数据集包含64,464张RGB图像，涵盖12种植物姿态及4个相机高度角。每张图像均含7个感兴趣区域（叶、花序、花簇、果簇、腋芽、茎、全株）的边界框标注，并提供基于BBCH尺度的50类细粒度生长阶段分类。此外，还包含3,616张高分辨率图像，具备像素级语义与实例分割标注。通过结合MobileNetv3分类、YOLOv11检测与MaskRCNN分割的级联深度学习框架验证，模型在五位领域专家参与的AI vs. 人类对比中，达到与专家相当的准确率与速度。Cohen's Kappa与评分者间一致性热力图证实该方法可靠性。
原文摘要 · Abstract (English)
Observer bias and inconsistencies in traditional plant phenotyping methods limit the accuracy and reproducibility 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.
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