# Incorporating Artificial Intelligence Technology in Smart Greenhouses: Current State of the Art
## 4 Barriers to Incorporating Artificial Intelligence Technology in Smart Greenhouses
### Impact of Demographics and Socioeconomic Factors on AI Technology Adoption in Agriculture
For example, the aging of the farmers was a major issue in Europe [67; 161]. In light of the observations made by critics and proponents of AI in smart greenhouses and precision agriculture, the following fundamental issues emerge. First, AI technology critics negate smart agriculture's immense contribution to farming. Second, the proponents of AI technology disregard the enormous capital requirements and the barriers to technology transfer in developing countries. On a positive note, there were successful cases in the developing countries in the global south. For example, Ethiopian scientists had developed an ERNN system to monitor humidity and temperature variations in greenhouse structures; the system had additional capabilities for wind velocity and carbon dioxide concentration measurement [170]. Proper temperature regulation and monitoring were necessary, considering a majority of the greenhouse crops; tomatoes were particularly susceptible to temperature variations [171]; the effect of chilling stress on greenhouse tomatoes in China was a case in point [171]. In practice, incorporating artificial intelligence technology in smart greenhouses should be supported by prior data on actual farm applications and short and long-term benefits vis-a-vis the costs and potentially detrimental environmental impacts. The dynamic natural and physical conditions (including atmospheric, weather, soil, and humidity) and biological interactions between plants, pests, and diseases in green \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **ANN Testing** & **ANN Training** & **ANN CV** & **GEP Testing** & **GEP Training** & **GEP CV** & \\ \hline RMSE & 0.070 & 0.068 & 0.070 & 0.095 & 0.091 & 0.095 & \\ r-RMSE & 13.07\% & 12.67\% & 14.25\% & 17.87\% & 17.13\% & 19.42\% & \\ MAE & 0.052 & 0.051 & 0.052 & 0.070 & 0.066 & 0.068 & \\ Stage 2 & 0.933 & 0.969 & 0.968 & 0.970 & 0.930 & 0.930 & 0.943 \\ RMSE & 0.050 & 0.053 & 0.049 & 0.077 & 0.078 & 0.069 & \\ r-RMSE & 3.33\% & 3.47\% & 3.26\% & 5.06\% & 5.10\% & 4.57\% & \\ MAE & 0.039 & 0.040 & 0.038 & 0.058 & 0.058 & 0.054 & \\ Stage 3 & 0.840 & 0.910 & 0.912 & 0.922 & 0.848 & 0.853 & 0.857 \\ RMSE & 0.087 & 0.086 & 0.081 & 0.114 & 0.113 & 0.113 & \\ r-RMSE & 3.44\% & 3.43\% & 3.18\% & 4.51\% & 4.48\% & 4.41\% & \\ MAE & 0.067 & 0.067 & 0.063 & 0.088 & 0.086 & 0.089 & \\ Stage 4 & 0.849 & 0.911 & 0.908 & 0.911 & 0.860 & 0.856 & 0.843 \\ RMSE & 0.086 & 0.091 & 0.083 & 0.109 & 0.113 & 0.110 & \\ r-RMSE & 2.44\% & 2.58\% & 2.38\% & 3.09\% & 3.23\% & 3.17\% & \\ MAE & 0.063 & 0.065 & 0.061 & 0.087 & 0.090 & 0.091 & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Gene expression programming (GEP), ANN RSME, and MAE [85]. houses and open-field agriculture increase the complexity of the decision-making processes [6; 15; 57]. The sophisticated relationship between these variables would predict the utility of AI in agriculture and agricultural sustainability. On a positive note, emerging research on bio-inspired algorithms and selected applications of AI systems in agriculture confirmed that the benefits outweighed the risks [14; 108; 121]. Moreover, the ongoing R&D projects would resolve most of the shortcomings.