# IoT-Based Cotton Plant Pest Detection and Smart-Response System
## 2 Related Work
### Technologies Used in Agriculture  
A very comprehensive survey has been presented which examines the benefits of the IoT as well as the challenges and benefits associated with this technology [9; 10; 11]. Fields and orchards are generally located far from the population, making it almost impossible for the farmer to look after them all the time, and the power supply is very limited due to the remoteness of the infrastructure. This makes it very difficult to apply the technologies, and the farmer is afraid to apply such things. As a result, even simpler and more energy-efficient technologies, such as the Internet of Things, have been finding it difficult to make their mark in agriculture. [PERSON] et al. [12] have developed an expert system based on ontology to control the presence of cotton diseases and pests. In this system, the cotton farmer inputs his queries, and the system helps him. It has been proven that this system can answer very difficult questions and suggest solutions to all these difficult queries. [PERSON] et al. [13] proposed a recurrent neural network-based (RNN) system in which pests and diseases are identified due to various weather factors. It includes a huge network that saves memory for a short time. The predictive results of this proposed network are very good, indicating many diseases and pests. [PERSON] et al. [14] have also come up with a model for diagnosing diseases that is based on artificial intelligence. A Neural Network is used in this system, and they also have developed a web-based intelligent diagnostic system that will be used to control cotton diseases. The accuracy of the predictions of this system is more than 85%, and the time required is very short, i.e., 900 ms. In another study [15], [PERSON] et al. presented an agricultural framework that applied to wheat crops. Figure 2 shows the proposed IoT platform, in which they have taken pictures of different parts of the crop through the Internet of Things and drones and sent them to the cloud data center to identify the environment conducive to pests in the field due to climate change. In this framework, we see that as the crop grows, the sensors collect real-time data related to the crop. Due to the size of the fields, which are large and delivering energy to the fields is a difficult task, the researchers have installed a solar system to provide energy to the sensors, which provides constant power, and the drones flying down have taken pictures of the separate parts of the field, and the same images are uploaded to the cloud that can be processed to assess the field environment and crop with the help of spectrum analysis technology. Due to the flight mode, the flight time of the drones has been extended, and the changes in the crop due to pests or diseases are monitored in the data center with the help of pictures. It has been observed that the probability of the presence of the disease is higher due to the temperature being between 14 \({}^{\circ}\)C and 16 \({}^{\circ}\)C, and when there is a lot of rain, the chance of spreading powdery mildew disease is reduced. In another study [16], [PERSON] et al. presented a methodology in which they used Unmanned Aerial Vehicle (UAV) integrated with advanced digital hyperspectral, multispectral, and RGB sensors. He proposed a method in which ground and aerial surveys were carried out by drones and sensors. They designed a predictive model that decides different levels of Phylloxera infestation based on airborne RGB, multi and hyperspectral imagery with ground-based data at two different periods. In [17], a system for diagnosing cotton pests and diseases has been proposed. It is shown in Figure 1 of [17], a combination of two different technologies that combine case-based reasoning and physio-logic to design and implement a web-based system. In another study [18], Researchers have tried to detect Red Palm weevil and related diseases through a prototype. The weevil is a very destructive insect that is capable of destroying the entire palm crop.