LARVA-Net: A Bio-Inspired Recursive Attention Model for IoT-Based Microclimate Prediction in Black Soldier Fly Insectariums

Yunita Sartika Sari; Kusrini Kusrini; Ema Utami; Ferry Wahyu Wibowo

doi:10.15408/jti.v19i2.54220

Authors

Yunita Sartika Sari Department of Informatics Doctorate, Universitas Amikom Yogyakarta, Indonesia
Kusrini Kusrini Department of Informatics Doctorate, Universitas Amikom Yogyakarta, Indonesia
Ema Utami Department of Informatics Doctorate, Universitas Amikom Yogyakarta, Indonesia
Ferry Wahyu Wibowo Department of Informatics Doctorate, Universitas Amikom Yogyakarta, Indonesia

DOI:

https://doi.org/10.15408/jti.v19i2.54220

Keywords:

LARVA-Net, Bio-Inspired Learning, BSF, IoT, Recursive Attention

Abstract

Microclimate prediction is critical for optimizing larval growth and productivity in Black Soldier Fly (BSF) insectariums, where temperature and humidity exhibit nonlinear and time-varying dynamics. Conventional models such as Vector Autoregression (VAR) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) often struggle to balance accuracy, efficiency, and interpretability in IoT-based environments. This study proposes LARVA-Net, a bio-inspired recursive attention framework for multivariate microclimate prediction in BSF insectariums. LARVA-Net integrates a VAR-based linear baseline with selective recursive attention and adaptive fuzzy-state learning. The bio-inspired aspect represents a computational abstraction of adaptive and energy-efficient information processing observed in larval behavior, rather than a biological simulation. By selectively updating internal states based on contextual relevance, the model suppresses error accumulation while maintaining interpretability and stable learning. The proposed method is evaluated using real-world IoT microclimate data collected from a semi-controlled BSF insectarium. Experimental results show that LARVA-Net outperforms VAR, ANFIS, and VARFIS, achieving an RMSE of 0.2440, MAE of 0.1466, MAPE of 0.4566%, and R² of 0.9911. These results demonstrate strong predictive performance and robustness under time-aware evaluation, indicating the suitability of LARVA-Net for real-time IoT-based microclimate monitoring in resource-constrained environments.

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