## 6 Conclusions  
This study proposes an automated intelligent greenhouse system that can integrate and deploy Internet of Things concepts and capabilities with an existing design. With  
Figure 19: Representation of RMSE value.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref.** & **Techniques Used** & **Shortfalls/Advantages** & **Accuracy** \\ \hline
[11] & - &
\begin{tabular}{c} Focuses on automation of \\ controlling pH only \\ \end{tabular} & 90\% \\ \hline
[36] & \
\begin{tabular}{c} Bayesian network \\ and machine learning \\ \end{tabular} &
\begin{tabular}{c} Fewer datasets for data \\ analysis \\ \end{tabular} & 66\% \\ \hline
[37] & ANN and fuzzy logic & Expensive system & 89\% \\ \hline Proposed Work & SVM and ANN & Low-cost system & 92\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparative analysis of various existing studies.  
Figure 20: Representation of accuracy value in %.  
real-time sensor data, a framework allows the user to control and monitor real-time data between the device and the fog layer, and vice versa. Furthermore, data are stored in the cloud and are accessible from anywhere. The Internet of Things concept is applied to the system to make growing food easier by streamlining the growing and monitoring processes using a secure cloud. Furthermore, using the Internet of Things addresses one of the most pressing concerns in automation today, maintenance, by offering a platform for monitoring the entire system from the cloud, lowering maintenance costs. As a result, the proposed system uses the IoT to help farmers while simultaneously enhancing the yield by maintaining accurate greenhouse factors such as soil moisture, CO\({}_{2}\), temperature, and light. The findings for greenhouse elements such as soil moisture, CO\({}_{2}\), temperature, and light for broccoli and gerbera plants are investigated using a graphical depiction based on real-world data collected using the suggested model. On Adafruit IO, equipment is used to keep track of greenhouse elements from afar, including soil moisture, CO\({}_{2}\), temperature, and light. Farmers may collect these data using an Adafruit IO Cloud account and an internet connection. Finally, using regression-based supervised machine learning algorithms, the performance of the innovative greenhouse system is evaluated for intelligent, precise management. However, further work that may be conducted to improve the system includes integrating data analytics or machine learning to develop algorithms to anticipate outcomes; adding more sensor parameters, such as pH, EC, and other soil micronutrient measurement sensors to obtain more accurate data; and assisting the artificial intelligence system in predicting the outcome.  
Conceptualization, A.R. and M.S.; methodology, A.R.; software, P.K.M.; validation, P.K.M., R.S. and T.A.; formal analysis, R.S.; investigation, T.A.; resources, A.R.; data curation, M.S.; writing--original draft preparation, A.R.; writing--review and editing, P.K.M.; supervision, M.S.; project administration, R.S. and T.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.