Abstract
This article presents the current state-of-the-art research on applying artificial intelligence (AI) technology in smart greenhouses to optimize crop yields, water, and fertilizer use efficiency, to reduce pest and disease, and to enhance agricultural sustainability. The key technologies of interest were robotic systems for pesticide application, irrigation, harvesting, bio-inspired algorithms for the automation of greenhouse processes, energy management, machine path planning and operation of UAVs (unmanned aerial vehicles), resolution of scheduling problems, and image signal processing for pest and disease diagnosis. Additionally, the review investigated the cost benefits of various energy-management and AI-based energy-saving technologies, the integration of photovoltaics and dynamic pricing based on real-time and time-of-use metrics, and the cost benefits of LoRa, Wi-Fi, Bluetooth, ZigBee, mobile, and RFID (radiofrequency identification) technologies. The review established that commercially viable AI technologies for agriculture had increased exponentially. For example, AI-based irrigation and soil fertilizer application enabled farmers to realize higher returns on investment on fertilizer application and gross returns above the fertilizer cost, higher yields, and resource use efficiency. Similarly, AI image detection techniques led to the early diagnosis of powdery mildew. The precise operation of agricultural robots was supported by the integration of light imaging, detection, and ranging (LIDAR) optical and electro-optical cameras in place of the traditional GPS (geographic positioning systems) technologies, which are prone to errors. However, critical challenges remained unresolved, including cost, disparities between research and development (R&D) innovations and technology commercialization, energy use, the tradeoff between accuracy and computational speeds, and technology gaps between the Global North and South. In general, the value of this review is that it surveys the literature on the maturity level of various AI technologies in smart greenhouses and offers a state-of-the-art picture of how far the technologies have successfully been applied in agriculture and what can be done to optimize their usability.
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