# 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
#### 2.4.1 Role of AI Technologies and Biological Pesticide Compounds in IPM
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Pesticides and Active Ingredients (AI)**} & \multirow{2}{*}{\begin{tabular}{c} **Field** \\ **Recommended** \\ **Rates** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Content of Active** \\ **Ingredient,** \\ **mcg/mL** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Original Number** \\ **Experiences (N0)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Cumulative** \\ **Dead** \\ **Mites (N1)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Mortality Rate** \\ **(Xep \(\pm\) St), \%** \\ \end{tabular} } \\ \hline \multicolumn{6}{c}{Pesticides used in greenhouses} \\ \multicolumn{6}{c}{Fitovermcm EC} & 1.0 & 20 & 146 & 9 & 93.7 \(\pm\) 6.7 \\ \multicolumn{6}{c}{(avermectin C, 2 g/L)} & 0.2 & 10 & 208 & 68 & 33.1 \(\pm\) 8.8 \\ \multicolumn{6}{c}{} & 0.1 & 5 & 154 & 27 & 16.3 \(\pm\) 3.2 \\ \multicolumn{6}{c}{} & Extra, & 0.015 & 105 & 102 & 23 & 21.7 \(\pm\) 4.0 \\ \multicolumn{6}{c}{} & WDP & 0.0075 & 525 & 89 & 16 & 18.2 \(\pm\) 3.7 \\ \multicolumn{6}{c}{} & EC & 0.2 & 1000 & 217 & 86 & 39.5 \(\pm\) 4.7 \\ \multicolumn{6}{c}{} & 0.1 & 500 & 260 & 65 & 24.8 \(\pm\) 5.4 \\ \multicolumn{6}{c}{} & WE & 0.15 & 660 & 66 & 49 & 42.1 \(\pm\) 7.3 \\ \multicolumn{6}{c}{} & 0.075 & 330 & 172 & 38 & 20.7 \(\pm\) 11.5 \\ \multicolumn{6}{c}{} & Promising pesticides for use in greenhouses} \\ \multicolumn{6}{c}{} & SC & 0.2 & 40 & 104 & 7 & 6.9 \(\pm\) 3.1 \\ \multicolumn{6}{c}{} & 20 & 0.1 & 20 & 106 & 7 & 4.4 \(\pm\) 0.8 \\ \multicolumn{6}{c}{} & 0.05 & 10 & 79 & 4 & 5.4 \(\pm\) 3.8 \\ \multicolumn{6}{c}{} & SC & 0.1 & 100 & 404 & 86 & 23.0 \(\pm\) 8.3 \\ \multicolumn{6}{c}{} & (pyridyl), 100 g/L) & 0.05 & 50 & 319 & 48 & 16.9 \(\pm\) 8.2 \\ \multicolumn{6}{c}{} & WSP & 0.2 & 100 & 158 & 111 & 70.5 \(\pm\) 3.1 \\ \multicolumn{6}{c}{} & 0.1 & 50 & 151 & 62 & 40.7 \(\pm\) 4.0 \\ \multicolumn{6}{c}{} & 0.05 & 25 & 68 & 7 & 9.3 \(\pm\) 1.4 \\ \multicolumn{6}{c}{} & SC & 0.05 & 120 & 136 & 55 & 42.2 \(\pm\) 9.8 \\ \multicolumn{6}{c}{} & 240 & 0.025 & 60 & 74 & 18 & 25.9 \(\pm\) 9.2 \\ \multicolumn{6}{c}{} & \multicolumn{6}{c}{} & - & 352 & 20 & 6.3 \(\pm\) 5.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Potency of different classes of pesticides against mites [117]. mildew and _Aphis gossypii_, among other pests and diseases, using AI and other smart technologies might yield enormous benefits to farmers. Complementary techniques for disease detection are needed, considering that the research of [PERSON] et al. [113], [PERSON] et al. [111], and [PERSON] et al. [114] confirmed that AI-based disease detection systems had critical shortcomings. For example, the detection of powdery mildew was impacted by the UV wavelength, the camera properties, and the location of the infected leaves in relation to the non-infected leaves. The shortcomings of AI-based disease detection systems in greenhouse and open-field agriculture raise pertinent questions about sustainable IPM practices. Two theories were advanced in the literature. On the one hand, proponents of the current status quo supported the continued use of pesticides to manage aggressive pests and diseases, including _Orrius laevisatus_, _Nesidiocoris tenuis_, _Tutta absoluta_, and _Amblysieius swirskii_[61]. However, persistent pesticide use increased the risk of crop pesticide resistance [61,113]. Moreover, the concentration of pesticides diminished over time, contributing to the resurgence in the population of _Nesidiocoris tenuis_ (see Figure 6); this phenomenon informed the need for frequent pesticide applications. On the other hand, the proponents of smart farming advocated using AI and IoT-based solutions in pest management. The high detection accuracy validated the case for AI in pest management. The UAV-based hyperspectral imaging and artificial intelligence system had 82% and 99% classification accuracy for the asymptomatic and late development of powdery mildew [113,127].