Predictive modeling, pattern recognition, and spatiotemporal representations of plant growth in simulated and controlled environments: A comprehensive review.

Abstract

Accurate predictions and representations of plant growth patterns in simulated and controlled environments are important for addressing various challenges in plant phenomics research. This review explores various works on state-of-the-art predictive pattern recognition techniques, focusing on the spatiotemporal modeling of plant traits and the integration of dynamic environmental interactions. We provide a comprehensive examination of deterministic, probabilistic, and generative modeling approaches, emphasizing their applications in high-throughput phenotyping and simulation-based plant growth forecasting. Key topics include regressions and neural network-based representation models for the task of forecasting, limitations of existing experiment-based deterministic approaches, and the need for dynamic frameworks that incorporate uncertainty and evolving environmental feedback. This review surveys advances in 2D and 3D structured data representations through functional-structural plant models and conditional generative models. We offer a perspective on opportunities for future works, emphasizing the integration of domain-specific knowledge to data-driven methods, improvements to available datasets, and the implementation of these techniques toward real-world applications.

Plant phenotyping relevance

植物フェノタイピングにおける成長形質の予測モデル、パターン認識、時空間表現を中心に扱う包括的レビューであり、方法論が主題です。

Code and data availability

This is a review article with no original plant-phenotyping measurements, datasets, images, or analysis code of its own. The only public URLs in the text are the CC license, two BioRender schematic figure links, a cited textbook chapter, and a cited BMVC workshop paper — none constitute paper-specific phenotyping data,

No evidence-backed public reproduction asset is currently recorded.