构建首个覆盖苹果全生长周期的立体图像数据集，支持果实检测与三维重建。
AppleGrowthVision: A large-scale stereo dataset for phenological analysis, fruit detection, and 3D reconstruction in apple orchards
- 采集9317张高分辨率立体图像，覆盖六个农业验证生长阶段。
- 含31084个果实标注，提升YOLOv8和Faster R-CNN模型性能超30%。
- 适合从事精准农业、果树监测与三维建模的研究者使用。
深度学习已推动计算机视觉在精准农业中的应用，但苹果园监测仍受限于数据集匮乏。现有数据集缺乏多样性、真实感，且难以对密集异质场景进行标注，同时忽略不同生长阶段与立体图像，而这些对果园的逼真三维建模及果实定位、产量估算、结构分析至关重要。为填补此空白，我们提出AppleGrowthVision，一个大规模数据集，包含两个子集：第一部分为来自德国勃兰登堡农场的9,317张高分辨率立体图像，覆盖一个完整生长周期的六个农业验证生长阶段；第二部分为同一勃兰登堡农场及皮尔尼茨农场的1,125张密集标注图像，共含31,084个苹果标签。该数据集提供具有农业验证生长阶段的立体图像，支持精确的物候分析与三维重建。将本数据集扩展MinneApple可使YOLOv8的F1-score提升7.69%，叠加至MinneApple与MAD则使Faster R-CNN F1-score提升31.06%。此外，利用VGG16、ResNet152、DenseNet201与MobileNetv2对六种BBCH生长阶段的预测准确率超过95%。AppleGrowthVision弥合了农业科学与计算机视觉之间的鸿沟，助力开发鲁棒的果实检测、生长建模与三维分析模型。未来工作包括改进标注质量、增强三维重建能力，并实现全生长阶段的多模态分析。
原文摘要 · Abstract (English)
Deep learning has transformed computer vision for precision agriculture, yet apple orchard monitoring remains limited by dataset constraints. The lack of diverse, realistic datasets and the difficulty of annotating dense, heterogeneous scenes. Existing datasets overlook different growth stages and stereo imagery, both essential for realistic 3D modeling of orchards and tasks like fruit localization, yield estimation, and structural analysis. To address these gaps, we present AppleGrowthVision, a large-scale dataset comprising two subsets. The first includes 9,317 high resolution stereo images collected from a farm in Brandenburg (Germany), covering six agriculturally validated growth stages over a full growth cycle. The second subset consists of 1,125 densely annotated images from the same farm in Brandenburg and one in Pillnitz (Germany), containing a total of 31,084 apple labels. AppleGrowthVision provides stereo-image data with agriculturally validated growth stages, enabling precise phenological analysis and 3D reconstructions. Extending MinneApple with our data improves YOLOv8 performance by 7.69 % in terms of F1-score, while adding it to MinneApple and MAD boosts Faster R-CNN F1-score by 31.06 %. Additionally, six BBCH stages were predicted with over 95 % accuracy using VGG16, ResNet152, DenseNet201, and MobileNetv2. AppleGrowthVision bridges the gap between agricultural science and computer vision, by enabling the development of robust models for fruit detection, growth modeling, and 3D analysis in precision agriculture. Future work includes improving annotation, enhancing 3D reconstruction, and extending multimodal analysis across all growth stages.
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