Remote Sensing, Geographic Information System (GIS), and Machine Learning in the Pest Status Monitoring

In the chapter titled “Remote Sensing, Geographic Information System (GIS), and Machine Learning in Pest Status Monitoring” from the book “Decision Systems in Agricultural Pest Management,” the focus is on the significance of precision agriculture in addressing global food security challenges. The chapter explores how advanced sensors and analyses play a crucial role in optimizing agricultural practices and improving crop yields. By integrating state-of-the-art technologies such as remote sensing (RS), GIS, and machine learning (ML), precision agriculture enters a new era of revolution, enabling more precise and adaptable management of agricultural systems. Remote sensing, especially when combined with Artificial Inteligence and ML, has shown to be effective in monitoring agricultural health and making informed decisions in integrated pest management (IPM). The utilization of RS technology provides rapid and detailed data on pest populations, aiding in decision-making processes. Furthermore, the integration of GIS, GPS, and RS technologies offers valuable spatial data for evaluating population changes and monitoring pest occurrences over large areas or over time. This chapter highlights the importance of leveraging high-tech solutions in agricultural pest management for sustainable and efficient practices. Within this chapter, fundamental and practical concepts related to RS, GIS, various algorithms in ML, and their potential applications in IPM are explored. Through practical case studies, we aim to envision the near future for using these tools in decision-making processes related to pest management.