Remote sensing and artificial intelligence: revolutionizing pest management in agriculture - Aziz, Danishta; Rafiq, Summira; Saini, Pawan; Ahad, Ishtiyaq; Gonal, Basanagouda; Rehman, Sheikh Aafreen; Rashid, Shafiya; Saini, Pooja; Rohela, Gulab Khan; Aalum, Khursheed; Singh, Gurjeet; Gnanesh, Belaghihalli N.; et al. (Frontiers in Sustainable Food Systems, 2025)
The agriculture sector is currently facing several challenges, including the growing global human population, depletion of natural resources, reduction of arable land, rapidly changing climate, and the frequent occurrence of human diseases such as Ebola, Lassa, Zika, Nipah, and most recently, the COVID-19 pandemic. These challenges pose a threat to global food and nutritional security and place pressure on the scientific community to achieve Sustainable Development Goal 2 (SDG2), which aims to eradicate hunger and malnutrition. Technological advancement plays a significant role in enhancing our understanding of the agricultural system and its interactions from the cellular level to the green field level for the benefit of humanity. The use of remote sensing (RS), artificial intelligence (AI), and machine learning (ML) approaches is highly advantageous for producing precise and accurate datasets to develop management tools and models. These technologies are beneficial for understanding soil types, efficiently managing water, optimizing nutrient application, designing forecasting and early warning models, protecting crops from plant diseases and insect pests, and detecting threats such as locusts. The application of RS, AI, and ML algorithms is a promising and transformative approach to improve the resilience of agriculture against biotic and abiotic stresses and achieve sustainability to meet the needs of the ever-growing human population. In this article covered the leveraging AI algorithms and RS data, and how these technologies enable real time monitoring, early detection, and accurate forecasting of pest outbreaks. Furthermore, discussed how these approaches allows for more precise, targeted pest control interventions, reducing the reliance on broad spectrum pesticides and minimizing environmental impact. Despite challenges in data quality and technology accessibility, the integration of AI and RS holds significant potential in revolutionizing pest management.