Unveiling the potential of sustainable agriculture: A comprehensive survey on the advancement of AI and sensory data for smart greenhouses
Rabia Al-Qudah, Mrouj Almuhajri, Ching Y. Suen
Abstract
The intersection of Artificial Intelligence (AI) and the Internet of Things (IoT) has propelled the agricultural industry into a new era of efficiency and sustainability. Among the diverse applications of AI and IoT in agriculture, Smart Greenhouses (SGHs) are particularly notable for their transformative potential in revolutionizing crop cultivation practices. Moreover, the adoption of SGH technologies has significant implications for agricultural sustainability and environmental conservation. By minimizing resource waste and reducing reliance on chemical inputs, SGHs mitigate the environmental impact of traditional farming practices. The aim of this comprehensive survey is evaluating the state-of-the-art literature on SGH development using AI and sensory data. In addition, this survey is one of the first to bridge the gap between academic research and industrial applications of AI-powered SGHs, offering a holistic view of the field’s progress and future prospects. This work also critically examines the technical level of the surveyed works and their alignment with the current AI trends. This comprehensive survey follows a well-defined review protocol and inclusion criteria. A total of 88 studies, industrial projects, related datasets from different research sources, namely, IEEE, SpringerLink and Science Direct were included in the review. The survey critically assesses both academic and industrial SGH projects, identifying key research gaps and the lag in adopting recent AI innovations. • The scarcity of multimodal and synthetic datasets is a significant research gap in the field of smart greenhouses. • A novel research direction, namely Cognitive Smart Greenhouses (CSGH), is introduced. • A critical analysis of the surveyed literature reveals key challenges, including a notable gap between academic research and industrial practices. • The smart greenhouse literature lacks focus on secure decentralized AI training methods.