Knowledge Economy Studies

Promoting Environmental Sustainability through Artificial Intelligence: A Systematic Review of Detection, Prevention, and Treatment Applications in Houseplants and Home Greenhouses

Articles in Press

Document Type : Original Article

Authors

Sara Mazaheri 1
Neda Abdolvand 2

1 Department of Management, Faculty of Social Sciences and Economics, Alzahra University, Tehran, Iran

2 Department of Management, Faculty of Social Sciences and Economics, Alzahra University

https://doi.org/10.22034/kes.2026.2093445.1123
Abstract
Ornamental houseplants and home greenhouses, as part of urban agriculture and indoor cultivation, are playing a growing role in environmental sustainability, household food security, indoor air quality, and urban well-being. This review asks how far the existing literature has actually moved beyond reactive disease detection toward a complete cycle of intelligent and sustainable plant protection — one that also covers early prediction, treatment decision support, and deployment in users' real homes. Following PRISMA 2020, we analyzed 55 primary studies published between 2015 and 2025. Thematic coding produced 23 codes across 9 categories and 4 dimensions: disease detection (64%), prediction and early warning (18%), treatment support (9%), and home/greenhouse deployment (7%). Reported accuracy reaches 95–99% in laboratory settings but falls to 70–85% once models are tested in real environments. We identified five structural gaps: a strong bias toward reactive over preventive approaches, a persistent laboratory-to-reality accuracy gap, an almost complete absence of data for indoor ornamental species, no use whatsoever of large language models for treatment guidance, and a weak theoretical connection to sustainable agriculture and Integrated Pest Management (IPM) principles. Future work should prioritize lightweight, generalizable models that integrate with environmental sensing under an IPM framework, thereby translating technical accuracy into measurable sustainability outcomes.

Keywords

houseplants
home greenhouse
plant disease detection
deep learning
sustainable agriculture
environmental sustainability
Subjects
Knowledge Economy Studies

Articles in Press, Accepted Manuscript
Available Online from 26 July 2026

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