# Incorporating Artificial Intelligence Technology in Smart Greenhouses: Current State of the Art
## 2 Artificial Intelligence Technologies for Smart Greenhouses
### Robotics and Algorithms for Precision Agriculture and Smart Greenhouses
In addition, the relative mean, standard error, and mean of absolute value of errors (MAE) values were within the acceptable range [109]. The performance of ELM-GA/CSA/CSO/PFA raises questions on whether bio-inspired algorithms are better than traditional ones. Such concerns were informed by the study of [PERSON] et al., which achieved comparable \(R^{2}\), RSME (Relative Standard Mean Error), and MAE values using ANN, SVM, and Gaussian process regression techniques [110]. Following the comparison of the validation and test performance, it was deduced that the choice of ANN/SVM, GPR vis-a-vis ELM-GA/CSA/CSO/PFA should consider other variables beyond \(R^{2}\), RSME, and MAE. The observations were justified by the model-specific training time and performance in the validation phase [110]. The selection of the appropriate algorithm is key to collecting reliable real-time data using sensors to help eliminate excessive waste of water, pesticides, and fertilizers. AI-based irrigation techniques offer additional benefits, including reduced risk of powdery mildew and Botrytis and fungal pathogen spores caused by excess humidity in greenhouses (due to inadequate regulation of water molecule condensation during sunrise and sunset and on cold, cloudy days) [111]. Proactive prevention of crop pests and disease directly impacts crop yields, considering powdery mildew and Botrytis limit chlorophyll formation through conidia and conidiophores on the leaf surfaces. Recent advances in AI technology for agricultural production facilitated autonomous monitoring of chlorophyll formation using the SPAD value, which is a proxy measure for wavelength absorbance [112]. However, the reliability of the measurements is contingent on the chloroplast movements and light intensity. [PERSON] et al. [111] employed long short-term memory neural network systems to effectively manage powdery mildew in greenhouse structures. The findings reported by [PERSON] et al. [111] were in line with the research of [PERSON] et al. [113] and other experimental studies, which supported using AI technology to prevent pests and diseases in greenhouses. In particular, [PERSON] et al. relied on UAV-based hyperspectral imaging and artificial intelligence to accurately diagnose the presence of powdery mildew [113] (see Figure 4). However, the case made by [PERSON] et al. [113] and [PERSON] et al. [111] was not aligned with [PERSON] et al. [114], who noted that machine learning techniques were equally effective in the management of powdery mildew. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Station/ Model**} & \multicolumn{3}{c}{**Training**} & \multicolumn{5}{c}{**Validation**} & \multicolumn{5}{c}{**Testing**} & \\ \cline{2-13}  & **R2** & **RMSE** & \begin{tabular}{c} **NRMSE** \\ **(\%)** \\ \end{tabular}  & **MAE** & **R2** & **RMSE** & \begin{tabular}{c} **NRMSE** \\ **(\%)** \\ \end{tabular}  & **MAE** & **R2** & **RMSE** & \ \begin{tabular}{c} **NRMSE** \\ **(\%)** \\ \end{tabular}  & **MAE** & **R2** & **RMSE** & \begin{tabular}{c} **NRMSE** \\ **(\%)** \\ \end{tabular}  & **MAE** \\ \hline \multicolumn{1}{c}{} & \multicolumn{3}{c}{**(mm d\({}^{-1}\))**} & \multicolumn{3}{c}{**(mm d\({}^{-1}\))**} & \multicolumn{3}{c}{**(mm d\({}^{-1}\))**} & \multicolumn{3}{c}{**(mm d\({}^{-1}\))**} & \multicolumn{3}{c}{**(mm d\({}^{-1}\))**} \\ \hline ELM & 0.995 & 0.150 & 5.020 & 0.122 & 0.994 & 0.189 & 6.332 & 0.132 & 0.988 & 0.288 & 9.532 & 0.200 \\ ELM-GA & 0.995 & 0.149 & 4.983 & 0.117 & 0.995 & 0.176 & 5.882 & 0.125 & 0.988 & 0.281 & 9.280 & 0.193 \\ ELM-ACO & 0.9 The concerns raised by [PERSON] et al. [113], [PERSON] et al. [111], and [PERSON] et al. [114] about the reliability of the algorithms for powdery mildew diagnosis are but a microcosm of the limits of image detection techniques. Future R&D is anticipated to resolve these challenges.