GroMo: Plant Growth Modeling with Multiview Images

2025-03-09

AI Summary

This paper addresses the challenge of modeling plant growth dynamics for phenotypic analysis. We propose a method for plant age prediction and leaf count estimation using 24-view multispectral imagery. Our key contributions are: (1) GroMo25—the first large-scale, multi-crop (four species), multi-plant, multi-day, multi-stage (five growth stages) multiview plant growth benchmark dataset; (2) the first paradigm for collaborative multiview modeling of plant growth dynamics; and (3) the Multiview Vision Transformer (MVVT), a novel architecture designed specifically for multiview plant understanding, which fuses cross-view features and jointly regresses plant age and leaf count. On GroMo25, MVVT achieves a mean absolute error (MAE) of 7.74 days for age prediction and 5.52 leaves for leaf counting. Both the code and dataset are publicly released.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multi-instance/Multi-view Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

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.

Problem

• Predict plant age using multiview images for crop monitoring.
• Estimate leaf count from multiview images for precision agriculture.
• Advance plant phenotyping research with innovative growth tracking solutions.

Innovation

• Multiview Vision Transformer for plant growth modeling
• GroMo25 dataset with multiview images of four crops
• Age prediction and leaf count estimation using MAE metrics