用多视角图像预测植物年龄和叶片数量，助力精准农业
GroMo: Plant Growth Modeling with Multiview Images
- 基于24个角度的多视角图像，构建植物生长建模方法
- 在四种作物上实现年龄预测平均误差7.74天，叶片数误差5.52片
- 适合对植物表型分析、农业智能化感兴趣的科研与工程人员
理解植物生长动态对农业与植物表型研究至关重要。我们提出植物生长建模（GroMo）挑战，旨在解决两个核心任务：植物年龄预测与叶数估计，二者均对作物监测与精准农业具有重要意义。为此，我们构建了GroMo25数据集，包含萝卜、木豆、小麦和芥菜四种作物，每种作物由多个植株（p1, p2, ..., pn）在不同日期（d1, d2, ..., dm）拍摄，共分为五个生长阶段（L1-L5）。每个植株从24个不同角度采集图像，相邻视角间隔15度。参赛者需使用这些多视角图像完成所有四类作物的两项任务。我们提出了多视角视觉变压器（MVVT）模型，并在GroMo25上评估其作物级表现：年龄预测平均绝对误差（MAE）为7.74，叶数估计MAE为5.52。该挑战旨在推动植物表型研究发展，鼓励创新性生长追踪与预测方案。项目代码已公开于GitHub：https://github.com/mriglab/GroMo-Plant-Growth-Modeling-with-Multiview-Images。
原文摘要 · Abstract (English)
Understanding plant growth dynamics is essential for applications in agriculture and plant phenotyping. We present the Growth Modelling (GroMo) challenge, which is designed for two primary tasks: (1) plant age prediction and (2) leaf count estimation, both essential for crop monitoring and precision agriculture. For this challenge, we introduce GroMo25, a dataset with images of four crops: radish, okra, wheat, and mustard. Each crop consists of multiple plants (p1, p2, ..., pn) captured over different days (d1, d2, ..., dm) and categorized into five levels (L1, L2, L3, L4, L5). Each plant is captured from 24 different angles with a 15-degree gap between images. Participants are required to perform both tasks for all four crops with these multiview images. We proposed a Multiview Vision Transformer (MVVT) model for the GroMo challenge and evaluated the crop-wise performance on GroMo25. MVVT reports an average MAE of 7.74 for age prediction and an MAE of 5.52 for leaf count. The GroMo Challenge aims to advance plant phenotyping research by encouraging innovative solutions for tracking and predicting plant growth. The GitHub repository is publicly available at https://github.com/mriglab/GroMo-Plant-Growth-Modeling-with-Multiview-Images.
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