Phytomorphic Computing: A Bio-Inspired Framework for Sustainable Data Processing Using Plant Growth Patterns
Posted: Oct 28, 2025
Abstract
The computing industry faces unprecedented challenges in energy consumption and environmental sustainability. Current computational paradigms, largely inspired by animal nervous systems or mathematical abstractions, prioritize performance metrics that often conflict with ecological considerations. We propose a radical departure from these approaches by drawing inspiration from plant biology—specifically, the sophisticated growth patterns, resource distribution mechanisms, and environmental adaptation strategies exhibited by botanical systems. Phytomorphic computing represents a novel framework that translates plant biological processes into computational operations. While neural networks mimic animal cognition, our approach leverages the unique characteristics of plant systems: decentralized control, energy efficiency, environmental responsiveness, and graceful degradation. This research addresses the fundamental question: Can we design computing systems that grow, adapt, and process information like plants while achieving practical computational objectives? Our contributions include: (1) a formal model of phytomorphic computation based on plant physiology, (2) algorithms for phyllotactic data organization and auxin-inspired resource allocation, (3) experimental validation of energy efficiency and resilience properties, and (4) application case studies in sustainable edge computing.
Downloads: 0
Abstract Views: 1235
Rank: 307961