# Smart Irrigation Systems from Cyber-Physical Perspective: State of Art and Future Directions
## 3 Problem Space
### IoT Layer Dimension
#### 3.1.3 Application Layer
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Name**} & \multirow{2}{*}{**Type**} & \multirow{2}{*}{**IoT Protocol**} & **Open** & **Mobile App** & **Device** & \multirow{2}{*}{**Security**} & **Paper ID** \\  & & & **Source** & **Support** & & **Management** & \\ \hline \multirow{2}{*}{ThingSpeak} & \multirow{2}{*}{Cloud Platform} & HTTP and & Yes & No & No & TLS & [42; 58; 64] \\  & & MQTT & & & & & & \\ \hline \multirow{2}{*}{Blynk} & \multirow{2}{*}{Cloud Platform} & HTTP and & & & & & & \\  & & MQTT & & & & & \\ \hline \multirow{2}{*}{Chirpstack} & Standalone & & & & & & \\  & (LoRaWAN & & & & & \\  & Network Server) & & & & & & \\ \hline Amazon EC2 & Cloud Platform & MQTT & No & Yes & Yes & AWS Secure & [33] \\ \hline \multirow{2}{*}{Website} & Custom & HTTP and & & & & & \\  & Development & MQTT & Yes & No & No & None & [38; 49; 55; 56; 60] \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Platform of IoT irrigation system. Figure 6: CoAP protocol. the model's accuracy but have the potential to uncover latent trends inherent within the data itself, as highlighted by [78]. Numerous machine learning algorithms have been proposed using data collected from IoT sensors. For instance, [PERSON] et al. [79] introduced the utilization of a support vector machine (SVM) to rectify biased recursive data. [PERSON] et al. [80] combined SVM, Decision Tree, and K-Nearest Neighbour (KNN) algorithms for data classification. Similarly, [PERSON] et al. [81] employed binary SVM to ascertain the optimal timing for irrigation. These algorithms empower accurate predictions, thereby mitigating errors associated with arbitrary guessing. Deep learning represents the recent and growing development of machine learning that leverages principles from artificial neural networks [82; 83]. Unlike conventional machine learning, deep learning automates a significant portion of feature extraction, thereby reducing the necessity for extensive manual human intervention, as elucidated by [PERSON] [84]. By identifying patterns within data, deep learning models proficiently group inputs, enhancing their ability to comprehend intricate relationships [76]. [PERSON] et al. [85] propounded an innovative approach termed \"DLiSA\", featuring a long short-term memory network (LSTM) to prognosticate volumetric soil moisture content for the forthcoming day. This model incorporates predictions regarding irrigation periods and spatial water distribution. Simulation results indicate that DLISA outperforms contemporary models in prudent water resource utilization within experimental farming areas. [PERSON] and [PERSON] [86] introduced a deep reinforcement learning-based approach to improve the intelligence of irrigation systems, deploying neural networks to learn optimal control policies. These networks decode \"raw\" observations to articulate irrigation decisions for subsequent days, delineating a forward-looking approach to irrigation management. [PERSON] et al. [87] employed Recurrent Neural Networks (RNNs), Gated Recurrent Units (GRUs), and LSTM architectures to forecast approximate temperature and moisture levels.