Abstract
Insect pests are responsible for 20%–40% annual agricultural production losses globally, leading to an over-reliance on pesticides in farming practices. This has resulted in the overuse of pesticides which adversely affect the environment, human health, and natural resources. Integrated Pest Management has been utilized to enhance insect pest control, decrease the excessive use of pesticides, and enhance the output and quality of crops. The integration of remote sensing in pest management presents an alternative and cost effective tool to enhance insect pest monitoring and targeted management. This study provides a systematic review of remote sensing technologies for insect pest monitoring. The study analyzed 103 studies published between 2014 and 2024 indexed in Scopus and Web of Science databases. The results showed that insect pest monitoring studies using remote sensing increased annually in the past decade. Furthermore, findings revealed that MODerate resolution Imaging Spectroradiometer (MODIS), Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI) and The Thermal Infrared Sensor (TIRS) are mainly used sensors to detect and monitor the impact of insect pests on vegetation. Most studies reported that insect pests have been detected in forests and croplands, with newer sensors such as Sentinel-2 MultiSpectral Instrument and PlanetScope holding potential for systematic assessments in the future. United States of America and China are leading with insect pest monitoring research contributions. However, the analysis highlighted the lack of research contributions in South America and African countries, which highlight the need for increased research efforts on insects pest monitoring, particularly as they are increasingly impacting on food security and biodiversity in sub-Saharan Africa, where food insecurities are rife and biodiversity threatened by myriad of factors. Overall, recent advances in remote sensing emphasizes the need for more research incorporating new sensors and predictive modelling in monitoring and assessment of insect pest such as the notorious Brown Locust in South Africa.