Bioscience Methods 2024, Vol.15, No.4, 196-206 http://bioscipublisher.com/index.php/bm 2 01 Remote sensing involves the measurement and analysis of electromagnetic radiation reflected from crop fields, which can be used to detect physiological changes in plants caused by pest infestations. This technology allows for early detection and precise monitoring of pest outbreaks, which is crucial for timely intervention and minimizing crop losses (Filho et al., 2019; El-Ghany et al., 2020; Zhao et al., 2023). Drones, or unmanned aerial vehicles (UAVs), equipped with advanced imaging technologies such as multispectral and hyperspectral sensors, can capture detailed images of crop fields. These images can be processed to identify pest hotspots and generate prescription maps for targeted pesticide application. This method not only improves the efficiency of pest control but also reduces the environmental impact by minimizing the use of chemical pesticides (Vanegas et al., 2018; Filho et al., 2019; Zhao et al., 2023). Additionally, drones can be used for precision spraying, where they deliver pesticides or natural enemies directly to the affected areas, further enhancing the sustainability of pest management practices (Filho et al., 2019; Azfar et al., 2023a; Azfar et al., 2023b). 5.2 Data-driven decision making in pest management Data-driven decision making is a cornerstone of modern pest management strategies. The use of Internet of Things (IoT) devices, such as motion detection sensors and environmental sensors, enables real-time monitoring of pest activity and environmental conditions in cotton fields. These sensors collect vast amounts of data, which can be analyzed using advanced algorithms to predict pest outbreaks and inform management decisions (Chen et al., 2020; Azfar et al., 2023a; Azfar et al., 2023b). Artificial intelligence (AI) and machine learning techniques play a significant role in processing and interpreting the data collected from various sources. For instance, AI algorithms can classify and segment images of pests, extract relevant features, and predict pest occurrences based on historical data and environmental conditions. This predictive capability allows farmers to implement proactive pest management measures, reducing the likelihood of severe infestations and crop damage (Chen et al., 2020; Filho et al., 2022; Toscano-Miranda et al., 2022). The integration of AI with IoT and remote sensing technologies creates a comprehensive system for efficient and effective pest management in cotton crops (Chen et al., 2020; Toscano-Miranda et al., 2022; Azfar et al., 2023a). 5.3 Mobile applications for pest identification and management Mobile applications have emerged as valuable tools for pest identification and management in cotton crops. These applications leverage AI and image recognition technologies to provide farmers with real-time information about pest presence and severity. By simply capturing images of affected plants, farmers can receive instant feedback on the type of pest and recommended control measures (Chen et al., 2020; Toscano-Miranda et al., 2022). Moreover, mobile applications can integrate data from various sources, including remote sensing, IoT sensors, and weather stations, to provide a holistic view of the pest situation in the field. This integration enables farmers to make informed decisions about pest control, such as the optimal timing and location for pesticide application. Additionally, mobile applications can offer educational resources and best practices for pest management, empowering farmers with the knowledge needed to protect their crops effectively (Chen et al., 2020; Toscano-Miranda et al., 2022). In summary, the advancements in precision agriculture and digital tools, including remote sensing, drones, data-driven decision making, and mobile applications, have significantly enhanced pest management techniques for cotton crops. These technologies enable early detection, precise intervention, and informed decision-making, ultimately leading to more sustainable and efficient pest control practices. 6 Case Study 6.1 Successful implementation of IPM in a cotton-producing region The Australian cotton industry provides a compelling example of the successful implementation of Integrated Pest Management (IPM). Faced with escalating insecticide resistance, the industry adopted a systems IPM approach that integrated pest ecology, biology, and insecticide resistance management into a flexible, year-round strategy. This approach emphasized both strategic and tactical elements to reduce pest abundance and rationalize pest
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