Predictive Pattern Recognition Techniques Towards Spatiotemporal Representation of Plant Growth in Simulated and Controlled Environments: A Comprehensive Review

2024-12-13

AI Summary

To address the challenge of modeling dynamic plant growth in controlled environments, this paper systematically reviews and innovatively integrates deterministic, probabilistic, and generative modeling paradigms into a domain-informed, data-driven framework for forecasting growth under dynamic uncertainty. Methodologically, it unifies spatiotemporal trait evolution with environmental interaction mechanisms, combining regression models, deep neural networks, functional–structural plant models (FSPMs), and conditional generative models (GANs/VAEs), tailored to 2D/3D structured phenotypic data. For the first time, it rigorously characterizes the performance boundaries and applicability domains of each approach. The resulting predictive system achieves interpretability, robustness, and environment-responsive adaptability—overcoming limitations of static experimental paradigms. This work establishes a methodological foundation for high-throughput phenotyping and intelligent, real-world agricultural control.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

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.

Problem

• Plant Growth Prediction
• Environmental Factors
• Control Environment

Innovation

• Predictive Pattern Recognition
• Plant Growth Modeling
• Environmental Impact Analysis