Hybrid-conditional generative adversarial network framework for climate fault detection in vertical farming environments - Tejes, P. K.S.; Dasore, Abhishek; Hashim, Norhashila; Naik, Bukke Kiran; Reddy, Challa Monisha (Elsevier, 2026)
Fault diagnosis in vertical farming systems (VFS) is constrained by the lack of labeled fault data, especially under rare or complex airflow disturbance conditions. This limits the training and evaluation of machine learning (ML) models for real-time microclimate monitoring and control. This study aims to develop and assess a Hybrid Conditional Generative Adversarial Network (Hybrid-CGAN) to generate synthetic microclimate sequences for specific airflow fault scenarios using computational fluid dynamics (CFD)-based simulation data. Four airflow fault scenarios, such as, reduced airflow from the upper inlet plenum, rack short-circuit, non-functional rear exhaust fan, and high static pressure at the return-duct junction, were simulated using a steady-state CFD model of a multi-tier plant factory. Diurnal perturbations generated 24-hour temporal sequences. Synthetic datasets trained Hybrid-CGAN and were compared with baselines such as Synthetic Minority Over-sampling Technique (SMOTE), Random Oversampling (ROS), and unconditional GANs. Performance was evaluated using Dynamic Time Warping (DTW), Uniform Manifold Approximation and Projection (UMAP) visualizations, and accuracy of Gated Recurrent Unit (GRU), Light Gradient Boosting Machine (LGBM), and k-Nearest Neighbors (k-NN) models. Hybrid-CGAN achieved the lowest DTW score of 0.10 at 100 % CFD data, representing a 61.03 % improvement over standard GAN (DTW = 10.45) and 59.76 % over simulation-only baseline (DTW = 10.12). Classifiers trained on Hybrid-CGAN-augmented datasets achieved accuracies of 0.95 (GRU), 0.96 (LGBM), and 0.94 (k-NN). UMAP showed high clustering similarity between real and synthetic sequences. This study establishes a practical paradigm by integrating CFD-based airflow simulations with Hybrid-CGAN framework for fault-specific synthetic data generation in VFS.