# Incorporating Artificial Intelligence Technology in Smart Greenhouses: Current State of the Art
## 4 Barriers to Incorporating Artificial Intelligence Technology in Smart Greenhouses  
Current research on AI and IoT in agriculture advances distinct claims on the potential barriers to incorporating AI technology in smart greenhouses. One school of thought claimed that the inconsistent adoption of AI could be linked to the slow commercialization of R&D innovations, barriers to technology acceptance, and information asymmetry. This school of thought was corroborated by [PERSON] [8] and [PERSON] et al. [13]. The second school of thought suggested that demographics were a major determinant. Research from Europe suggested that demographics directly affect progressiveness and technology adoption in agriculture [67; 161]. Younger generations are more highly motivated to adopt new farming technologies than older generations. In contrast to the scenario observed by [67; 161] in Europe, Asian studies highlighted the aging of the population as a bar  
Figure 8: Sunlight prediction models and lighting strategies for the predictive light control systems [11]. PPFD was measured with units of \(\upmu\)mol. m\({}^{-2}\) s\({}^{-1}\). rier [162]. Drawing from the latter studies, each continent had unique barriers and drivers for precision technology adoption in greenhouses. The issues raised by [PERSON] [8] and [PERSON] et al. [13] concerning the pace of technology adoption were discounted by emerging research on the immense benefits of augmented reality, such as the GRETA application in promoting technology adoption among hobby farmers, and those with limited knowledge about farming technologies, and even professional farmers using unique human-computer interactions [24]. The case for AR advanced by [24] corroborates [PERSON] and [PERSON]'s study [163], which noted AR would help transform rural and novice farmers into experts while providing agronomists with a real-time and holistic view of farm and crop health. The benefits afforded by AR-based systems such as GRETA suggest that poor understanding of technology should not be an impediment to the automation of greenhouse operations. The low pace of AI technology adoption in the global agricultural sector has remained unresolved. For example, the first AI-powered system for agriculture was the GOSYM cotton crop simulation model developed in 1985 to regulate water and fertilizer application, weed control, and local climate for better cotton yields [8]. The GOSYM model was key to developing the Artificial Intelligence Automated Greenhouse System (AIAGS), processing systems, and web applications [13]. On the downside, there was limited R&D in the subsequent years to build on the GOSYM model. In 1996, researchers developed an autonomous AUORA greenhouse robot to replace manual labor. The historical slow adoption of precision farming technologies documented by [8; 13] was in line with [PERSON]'s appraisal of precision farming technology integration in the US and EU [71]. Until the late 2000s, only 22% of the farmers had integrated precision farming technologies in the West [71]. On a positive note, there was a notable improvement in the pace of technology adoption post-2013. In the past two years, researchers have developed multi-application AI systems for intelligent IPM [15; 86; 128], seeding and harvesting, and weed recognition [104; 105], and digital twins for autonomous greenhouse operation using traditional sensor data, IoT, big data analytics, and AI [136]. However, the pace of R&D innovations does not match commercial adoption--the digital twin-based GHI4.0 project in the Danish horticultural industry was in the pilot phase. On a positive note, 80% of Danish farmers attempted to integrate precision farming technologies in the production of maize and barley crops [71]. Presently, it is challenging to determine the cost-benefits of technological innovation and underlying motives for the transition to AI in agriculture [18].