Synthetic Fungi Datasets: A Time-Aligned Approach

TL;DR

This paper introduces a synthetic, time-aligned image dataset modeling key stages of fungal growth, supporting deep learning applications in fungal biology, disease monitoring, and industrial mycology.

Contribution

It presents a novel, scalable dataset that captures dynamic fungal morphological transformations with temporal consistency for AI research.

Findings

Supports classification of growth stages

Enables prediction of fungal development

Facilitates analysis of morphological patterns

Abstract

Fungi undergo dynamic morphological transformations throughout their lifecycle, forming intricate networks as they transition from spores to mature mycelium structures. To support the study of these time-dependent processes, we present a synthetic, time-aligned image dataset that models key stages of fungal growth. This dataset systematically captures phenomena such as spore size reduction, branching dynamics, and the emergence of complex mycelium networks. The controlled generation process ensures temporal consistency, scalability, and structural alignment, addressing the limitations of real-world fungal datasets. Optimized for deep learning (DL) applications, this dataset facilitates the development of models for classifying growth stages, predicting fungal development, and analyzing morphological patterns over time. With applications spanning agriculture, medicine, and industrial mycology, this resource provides a robust foundation for automating fungal analysis, enhancing disease monitoring, and advancing fungal biology research through artificial intelligence.