Title: Machine learning and IoT in improving productivity of smart farming: a review

Authors: Jaishree Srivastava; Manish Madhava Tripathi

Addresses: Department of Computer Science and Engineering, Integral University, Lucknow, India ' Department of Computer Science and Engineering, Integral University, Lucknow, India

Abstract: Soil is essential to plant life, however in the machine learning and IoT era, hydroponic and aeroponic methods can create plants without soil. Research in this sector has grown rapidly. IoT and machine learning are utilised in hydroponics and aeroponics to monitor plant growth, estimate planting time, and most importantly, environmental conditions. These two methods make planting easier. Another novel method is aquaponics, an eco-friendly agricultural method that mixes hydroponics and aquaculture. Fish and plants work together in aquaculture, which raises fish in a closed space, and hydroponics, which grows plants without soil. This article examines how data analytics, sensors, and automation can improve hydroponic aeroponics and aquaponics systems and smart farming. These tools assist farmers in maximising plant development and reduce resource loss by monitoring and managing in real-time. These systems can adapt to changing environmental circumstances by integrating IoT devices, ML, and AI. This paper has high resilience to outliers, with 94.26% prediction accuracy and low error rates compared to support vectors, random forests, etc.

Keywords: hydroponic; aeroponic; aquaponic; machine learning; real-time monitoring; internet of things; IoT; smart farming; SVM.

DOI: 10.1504/IJAITG.2026.153497

International Journal of Agriculture Innovation, Technology and Globalisation, 2026 Vol.5 No.2, pp.162 - 181

Received: 25 Sep 2024
Accepted: 03 Apr 2025
Published online: 11 May 2026 *

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