1Krishna Institute of Science and Technology, Krishna Vishwa Vidyapeeth “Deemed to be University”, Karad, Satara - 415 539, Maharashtra, India, E-mail: jayakarape@gmail.com
2Department of Pharmacy Practice, Krishna Institute of Pharmacy, Krishna Vishwa Vidyapeeth “Deemed to be University”, Karad, Satara - 415 539, Maharashtra, India, E-mail: kipjishaannie22@gmail.com
3Department of Plant Pathology, Institute of Agricultural Sciences, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar - 751 030, Odisha, India, E-mail: diptanudatta@soa.ac.in
4Department of Entomology, College of Agriculture, Parul University, Vadodara - 391 760, Gujarat, India, E-mail: agriculture@paruluniversity.ac.in
Department of Agriculture, Noida International University, Greater Noida, Gautam Buddha Nagar -201 310, Uttar Pradesh, India
*Corresponding authors' E-mail: fazil@niu.edu.in
Online published on 26 September, 2025.
This study provides a comprehensive overview of how variations in climate, including temperature fluctuations, altered rainfall patterns, and catastrophic events, impact insect populations. Using statistical approaches and machine learning, among other predictive modelling tools, we demonstrate a range of hypothetical spread scenarios including several pest species and global locations. Data from remote sensing, GIS systems, and citizen science initiatives forms the foundation of these models. This emphasises the need of making forecasts more accurate by using data from several sources. Predictive insights in farming will help legislators and other interested parties reduce the consequences of insect assaults on agricultural yield and food security.
Agricultural, Climate change, Data analysis, Ecological dynamics, Ecosystems, Food security, Integrated pest management, Machine learning, Monitoring, Pest outbreaks, Precipitation, Predictive modelling, Remote sensing, Resilience, Temperature
