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All Journal Jurnal Nasional Teknik Elektro dan Teknologi Informasi
Nana Marliza
Electrical Engineering Department, Faculty of Engineering, Universitas Cendekia Abditama, Tangerang, Banten, 15811, Indonesia
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Edge AI–Based Threshold-Free Control for Mushroom Farming Using K-NN on ESP32 Antonius Irianto Sukowati; Nana Marliza; Debyo Saptono
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 15 No 3: Agustus 2026 (dalam proses)
Publisher : This journal is published by the Department of Electrical and Information Engineering, Faculty of Engineering, Universitas Gadjah Mada.

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Abstract

Precise environmental control is critical for the successful cultivation of paddy straw mushrooms (Volvariella volvacea), which requires tightly regulated temperature, humidity, substrate moisture, and air quality. Conventional automation systems typically rely on fixed threshold logic (e.g., ‘if humidity < 80%, activate mist maker’) using isolated parameter triggers and lack contextual awareness of multivariate environmental dynamics. This study aimed to overcome this limitation by developing a threshold-free, context-aware control system that replaced rule-based triggers with multidimensional environmental state classification. To achieve this, a novel edge-artificial intelligence (AI) system that deployed a lightweight k-nearest neighbor (K-NN) classifier directly on an ESP32 microcontroller for real-time, closed-loop actuator control was proposed. The cultivation environment was modeled as four-dimensional discrete state space, yielding 81 expert-defined combinatorial classes that captured nuanced interactions among substrate moisture, air temperature, humidity, and CO₂ levels. Each class was mapped to a specific actuator vector (pump, heater, mist maker, fan), effectively encoding agronomic best practices into a machine-interpretable decision framework. The system achieved 98% classification accuracy, operated with only a 15 KB memory footprint, and completed inference in 12.3 ms, demonstrating that lightweight machine learning could replace rigid rule-based logic in resource-constrained agricultural settings. Beyond improving environmental stability and actuator reliability, this work establishes a new paradigm for knowledge-preserving, edge-intelligent automation in smart agriculture.
Co-Authors Antonius Irianto Sukowati Debyo Saptono