An AI-Enabled Vertical Hybrid Farming System Integrating IOT, Renewable Energy, and Machine Learning for Sustainable Agriculture

DOI

https://www.doi.org/10.59256/indjece.20260302004

Published In

Year 2026 · Volume 3 · Issue 2

Published Online

May-August 2026

Pages

32-39

Article Type

Original Article

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Rapid urbanization, diminishing arable land, and increasing demand for sustainable food production have accelerated the adoption of vertical farming as an alternative agricultural approach. However, maintaining optimal environmental conditions while minimizing resource consumption remains a significant challenge. This paper presents an AI-driven vertical hybrid farming system that integrates hydroponics, aeroponics, and aquaponics within a multilayer cultivation framework to improve crop productivity and resource efficiency. The proposed system combines Internet of Things (IoT) sensors, artificial intelligence, and automation to continuously monitor and regulate key environmental parameters, including temperature, humidity, light intensity, nutrient concentration, water quality, and fish health. Machine learning techniques are employed to support predictive decision-making, while automated control mechanisms optimize irrigation, nutrient delivery, and climate regulation with minimal human intervention. In addition, the system incorporates renewable energy through a solar-powered Maximum Power Point Tracking (MPPT) unit to improve energy efficiency and support sustainable operation. A deep learning-based plant disease detection module further enhances crop health monitoring by enabling early identification of diseases and timely corrective actions. The integrated framework promotes efficient water recycling, reduced pesticide usage, lower operational costs, and improved crop quality compared with conventional farming methods. The proposed approach demonstrates the potential of combining artificial intelligence, automation, and renewable energy to develop scalable and environmentally sustainable farming systems suitable for urban and resource-constrained environments.