# Incorporating Artificial Intelligence Technology in Smart Greenhouses: Current State of the Art
## 1 Introduction  
The AI systems of interest include bio-inspired ecological and swarm intelligence algorithms, unmanned UAVs (Unmanned aerial vehicles) for pesticide application, and robotic systems for harvesting, irrigation, soil treatment, fertilizer application, and seeding [1, 2]. In addition, the study critiques the role of swarm intelligence (genetic optimization algorithms and related algorithms) in agricultural parameter optimization, machinery path planning, robotic flight, resolution of scheduling problems, system identification, power systems, and image and signal processing [1, 2]. The multidimensional view of AI (artificial intelligence) technologies in smart greenhouses would provide a nuanced understanding of the recent milestones in research and development and their implications for commercial and smallholder agriculture in the developed global north (North America, Western Europe, Mediterranean region, China, Japan, and South Korea) and underdeveloped global south (Africa, Latin America, and South Asia) [3]. Emerging technology companies, such as Blue River Technology, Harvest CROO Robotics, Trace Genomics, and Autonomous Tractor Corp were leading in the commercial deployment of self-driving agricultural tractors, AI-based weed control systems, pest diagnostics, soil analysis, and crop health monitoring [4; 5]. In line with [4; 5], [PERSON] et al. [6] acknowledged the immense role played by Blue River Technology in the advancement of robotics and AI systems in agriculture in California and the US in general. These emerging trends of AI and machine learning (ML) in agriculture need to be summarized to provide a comprehensive overview of the state of the art (i.e., maturity levels of various AI applications in agriculture, including barriers and facilitators) and insights into future developments [7]. Many factors dictate the need to review the maturity levels of various AI applications in agriculture. First, there is increasing demand for technological solutions to address global food insecurities, especially in the Global South. Currently, at least 810 million people are food insecure. The actual ratio would increase over time in line with the global population growth. By 2050, the global population will surpass 9 billion [8; 9]. The higher population growth within a generation would strain the already fragile agricultural systems and ecosystems that are being gradually destroyed to create additional land for farming. [PERSON] et al. argued that controlled environment agriculture (CEA) using greenhouses might help to offset the pressing concerns about agricultural sustainability [10]. The case for greenhouse-based CEA was validated by the fact that greenhouse crop cultivation improves yields and reduces the need for excess pesticides, water, land, and transportation of crops over extended distances. The case for CEA advanced by [10] was in tandem with [PERSON] et al. [11], who supported the intensification of agricultural technologies. The need for a paradigm shift in agricultural cultivation was premised on the increasing risk of global starvation [12]. The AI-mediated improvements in crop yields may help avert the forecasted global food crises. Second, a large body of knowledge, including the evidence presented by [PERSON] et al. [13] and [PERSON] et al. [12], suggested that the satisfaction of future food needs would be achieved by optimizing the existing crop production technologies, given there was limited arable land for agricultural expansion [14]. In line with [14], [PERSON] and [PERSON] [15] estimated that smart technology in agriculture may help boost yields by 60% before 2030. Conservative estimates indicate the EU market for precision agriculture systems will surpass 11.8 billion by 2025 [16]. The projected market growth and higher yields catalyzed the current greenhouse boom. As of 2020, there were 3.6 million hectares of greenhouse crops globally [17; 18; 19; 20; 21; 22].