# IoT-Based Cotton Plant Pest Detection and Smart-Response System
## 1 Introduction  
With the increase in population, the demand for food and crops is also increasing. However, due to water scarcity and climate change and the area of fertile land is decreasing. At the same time, pests and diseases remain the enemies of the crop and its quality [1, 2]. Cotton is one of the most important crops, whether we talk about its commercial importance or mankind's need for it. Today, a large part of every country's economy depends on its crops, and cotton is indeed a very widely grown crop. According to the International Cotton Advisory Committee's report, Pakistan is the fifth largest producer of cotton after India, China, the USA, and Brazil [1, 3]. However, cotton is also the favorite crop of insects, so wherever it is planted, there is a high risk of damage by insects. Diseases also affect its growth and production as well as its quality. Therefore, it is our social and state responsibility to make effective plans for the control of cotton pests and diseases so that we can attain good quality cotton in great quantities [4, 5, 6]. In recent times we have seen that the Internet of Things (IoT) has become a very fast-spreading technology. The Internet of Things has quickly made inroads into various areas of life, such as health, communications, manufacturing, and energy, as well as agriculture. IoT has seen improvements in all these areas, and the quality of its products and services is increasing. [7, 8] We also know that the future of technology is very bright in agriculture. The features of IT sensors, LORA communication modules, and drones, which we can use in farming or agriculture, could not be more impressive in any other field. Due to these features, the combination of IoT and farming is indeed becoming very popular. Pest identification and its prevention is a field of work in which IoT is indeed very useful. However, many farmers are still reluctant to adopt this technology despite its usefulness. There are many reasons for this; for example, Figure 1 below shows some reasons. These barriers cannot be categorized as either big or small because they are all equally important. However, one of the most important apprehensions in adopting this technology is that it will add trouble and hassle to traditional farmers dealing with the new technology. Therefore, we believe that there is a need to study further the use of IoT-based architectures and prototypes for pest management which will contribute to both enhancing food quality and its production. Due to rapid enhancement in technology, there is a dire need for a predictive system that will enable farmers to increase their crop field as well as improve their quality and profit. In this paper, we propose an IoT framework in which sensors and drones will work together to protect cotton crops from flying insects and cotton bolworms. It can be used for real-time monitoring of agricultural fields. In our previous work, we proposed a locally assembled sensor prototype to detect cotton bolworm pests and validated it through real-time implementation both in the field and in the lab. In the study in this paper, we propose an IoT-based cotton plant pest detection and smart response system. This will be very useful for farmers in terms of minimizing losses as well as improving the quality of the crops. The novelty of the proposed system is that it is a complete system. It covers the entire process of detecting cotton pests and response, i.e., destroying them through pesticides with the help of drones. We have also proposed an algorithm for the complete flight movement of drones based on decision-making theory. The proposed system is general and can be applied to other crop pests by selecting suitable sensors. The rest of the paper is organized as follows. Section 2 discusses the related work of technologies used in smart agriculture and IoT-based models for crop pests in the literature. Our proposed system is described in Section 3 in detail, and the drone flight predictive algorithm is included as an appendix.