# IoT-Based Cotton Plant Pest Detection and Smart-Response System
## 2 Related Work
### Technologies Used in Agriculture
It is very difficult to see with the naked eye, and when it is visible, it is too late. Therefore, it is essential to use new technology to protect the crop from this pest. In this work, scientists have developed a prototype to prove their concept and planted it in the crop, and tried to monitor it from a distance around the clock so that the red palm weevil can be identified in time and, at the same time, it can be destroyed. It produced three kinds of performance parameters such as success, incomplete, and failure. The results were analyzed, and the success rate was over 90%. In [19], the researchers have proposed an IoT-based system that, in conjunction with digital image processing, will detect various plant leaf diseases. In this system, some sensors monitor the values of various parameters of the environment, while many diseases can be detected by taking pictures with the help of digital cameras and processing digital images on them. This system will work on a somewhat limited scale as the camera cannot take pictures at night while the sensors and server machine also have an energy problem. This research [20] has proposed an IoT framework that will monitor eight specific field parameters on a daily basis. A predictive model has also been developed that has been able to identify diseases and insects through machine learning. [PERSON] et al. [21] proposed a Fuzzy-Inference Crop Pest Prediction system. Various Rice and sugarcane/Millet pests and related disease were identified with the help of multiple weather parameters. Figure 2 of [21] shows the proposed layed architecture. The measured parameter includes temperature, humidity, atmospheric pressure, and rainfall. a Virtual Network Connection (VNC) is formed through the use of 5G-LTE connectivity. The proposed system predicts the occurrence of Rice pests using a fuzzy-logic-based mechanism. In [22], the authors proposed a hardware design, a network of wireless sensors, and an actuator network, which is a great application in the field of agriculture. The researchers have proven that in the development of modern technology, they have not denied the importance of remote-monitoring technology drones, and if combined with  
Figure 2: Overview of proposed Agriculture IoT platform [15]. image processing technology, it can be a great tool for agriculture. With the help of this, the crabs and diseases can be seen from afar and, at the same time, take any steps to cure them. In [23], [PERSON] et al. have suggested a sensor network used in a large field that will provide coverage through nodes spread across the field. A mobile node in the form of a drone collects data whose job is to collect data. Once a video is made, it has to send it to the data center. The researchers claim that their research is very standardized and much cheaper than the existing technology in the market. In Table 1, we have tried to give a quick overview of all the modern academic research on the subject. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Research** & **Proposed** & **Country** & **Crop** & **Pest or Disease** & **Contribution** \\ \hline \multirow{4}{*}{[PERSON] et al. [12]} & \multirow{4}{*}{ \begin{tabular}{c} Ontology-based \\ Expert System. \\ \end{tabular}  } & \multirow{4}{*}{India} & \multirow{4}{*}{Cotton} & \multirow{4}{*}{ \begin{tabular}{c} Multiple Pests \\ and Diseases. \\ \end{tabular}  } & \multirow{4}{*}{ \begin{tabular}{c} They have suggested an \\ Ontology-based expert system to \\ answer related to pests and diseases \\ using this system which uses \\ Web-based technologies. \\ \end{tabular}  } \\ \hline \multirow{4}{*}{[PERSON] et al. [13]} & Recurrent Neural & \multirow{4}{*}{China} & \multirow{4}{*}{Cotton} & \multirow{4}{*}{\ \begin{tabular}{c} Multiple Pests \\ and Diseases. \\ \end{tabular}