**1. Introduction**

Cotton is a very important plant throughout the world. The amount of cotton production in the world is nearly 25 million tons of cotton. Leading cotton-producing countries worldwide in 2022/2023 are announced as China, India, United States, and Brazil. It is estimated that world cotton production will reach 28 million tons with an annual increase of 1.5% until 2030 [1, 2]. While cotton production has steadily increased over the past few years, many issues have also started to emerge with the increase in cotton farming areas because current cotton production methods are not environmentally sustainable. Intensive and incorrect use of technological inputs such as tillage, fertilizers, irrigation, pesticides, and herbicides in cotton agriculture

has significantly caused soil and environmental degradation as well as reduced crop profitability [3].

One of the new ways that modern agriculture could potentially maintain or enhance crop yields by minimizing environmental pollution is site-specific application of inputs according to the needs of the crop, which is defined as Precision agriculture (PA) [4]. PA is an umbrella term for using modern data-driven technologies to optimize crop management and improve productivity, efficiency, and sustainability in agricultural production. Therefore, PA can be defined as the application of modern information technologies such as GPS, sensors, drones, Internet of Things (IoT), artificial intelligence (AI), and data analytics in the management of crop production [5]. It is seen that studies on PA have gained importance in recent years. The fact that Internet of Things (IoT), artificial intelligence (AI), remote sensing, and image processing (ImP) techniques have been actively used in agriculture by integrating with geographic information systems (GIS) and geographic position systems (GPS) has brought about important developments in the use of precision agriculture technologies (PAT) in agricultural production [6–8]. Kırkaya [6] stated that in the future, PAT will be widespread used in crop management practices such as sowing, fertilization, irrigation, and weed control. The author emphasized that PAT had the ability to protect crop health, soil, and the environment by effective and optimized application of inputs.

PAT reduces not only production costs and increases income for the producer but also reduces the negative environmental impact of agricultural chemicals by adjusting input application rates to crop requirements because it can help farmers monitor and control various aspects of their fields, such as soil conditions, crop growth, pest infestation, and water use [9, 10]. However, PA also has some limitations and challenges such as high initial investment and maintenance costs because it needs expensive and complex equipment, such as sensors, drones, satellites, computers, and software, to collect and analyze data and control the farming operations [11–13]. Therefore, it seems that the PA is not applicable especially in developing countries due to the presence of poor farmers, subsistence farming systems, small farmlands, lack of technical and software knowledge among farmers, and the high cost of application of its technologies.

In cotton agriculture, the adoption of PA technology has been very different than in grain production since cotton needs intensive management processes such as multiple fertilizer applications, multiple plant growth regulator applications, multiple irrigation potential applications, and multiple pesticide applications. The availability of cotton yield sensors later than grain yield sensors affected cotton producers' adaptation to precision agriculture.

In this chapter, a review of PA techniques and practices used in cotton production is presented along with several considerations and challenges. The advantages and disadvantages of PA, as well as some of the current and future trends and opportunities for the usage of PAT in crop management practices in cotton production are explored.
