# Recent Trends in AI-Based Intelligent Sensing
## 4 Practical Applications of Intelligent Sensing
### Applications of Intelligent Sensing  
The amount of data that is available on the internet and in our daily life in different forms is growing fast because of the rapid development of sensing and computing technologies. 1. Smart Agriculture--Intelligent sensing applied to this domain, to fulfill the need of farmers, faces lots of problems on a daily basis, like crop disease infestations, weed management, pesticide control, etc. [135]. The gradient descent-based technique is used in [136] to train the network on a real field dataset consisting of various tea gardens. To identify tea pests, a radial basis function network is used for classification. Ref. [137] combines expert system technology and ANN to predict the nutrition level in the crop in order to help inexpert farmers. This system is developed as an application of Android, which could be installed on a smartphone [137]. The basic methodology is feed-forward and backpropagation. The neural process and recalling patterns are done by the feed-forward algorithm, and the training is done by the backpropagation algorithm. A study carried out in [138] considers the use of ANN in various techniques to estimate evapotranspiration (ET). The methods applied include the Penman-Monteith method and Levenberg-Marquardt backpropagation. Because of the increase in the number of hidden layers, an increase in the variability of the ET estimation was observed. Figure 3 illustrates how smart sensors can be used in different areas of agriculture, i.e., soil, crop growth, disease identification, supply control, environment sensing, and bio-surveillance. Some key reasons for using sensors are real-time monitoring to  
enable remedial measures, cost savings by reducing waste, remote sensing through wireless and IoT platforms, and automated agricultural produce monitoring. 2. Intruder Detection and Surveillance--In IoT devices, attacks and threats have become more dominant as intrusion detection methodologies are hard to deploy. The most effective intrusion detection systems apply signature-matching methods for detecting vicious activities [139]. These systems have low false alarm rates and perform well in the various attacks. Another is anomaly detection; it maps ordinary behaviors to a certain baseline and detects eccentricities. For creating a baseline profile, a supervised learning algorithm is used, which uses previous data samples to train a model. In [140], the knowledge discovery in databases (KDD) is saved to the Oracle database server to extract the proper dataset for a set of classifiers. After preparing the dataset by removing the attacks, the most common experimental techniques are multilayer perception, Bayesian algorithm, and J48 trees for classification. In [141], the dataset from the 1998 DARPA intrusion detection program is pre-processed in binary TCP dump format readable by the neural network. Backpropagation is the supervised learning method used to accomplish this task. An intelligent video surveillance system (IVSS) composed of an IP camera and a human-computer interface is presented in [142]. IVSS has modules for image analysis, image understanding, video capture, and event generation. In the video capture module, input data can be accessed from different IP addresses of cameras over a LAN. Image analysis comprises image processing tasks, for example, extracting relevant information, including tracking, motion detection, etc. Image understanding includes AI techniques to understand the significance of the scene captured by a camera. The abnormal behavior is then forwarded to an event generation module, which helps the user by generating an alarm. The use of intelligent sensing in intrusion detection and remote surveillance for monitoring applications is shown in Figure 4. The use of smart sensors will greatly help improve the existing systems in terms of cost, energy, and performance. 3. Intelligent Traffic Management- AI-based techniques have been applied in this field to control road traffic.