Syed Ahmad Shah Bukhari1, *, Ali Raza Mushtaq1, *, Maryam Hameed Butt1, Muhammad Shahid Siddique1 and Muhammad Urbaz Ul Hassan Abbas1
1Department of Entomology, Faculty of Agriculture and Environment, the Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan
*Corresponding author: alirazach404@gmail.com; sahmads297@gmail.com
Insect pest dynamics are significantly influenced by climate change and the threats thus pose severe threats to the global agricultural systems. Pest increased survival, redistribution and resistance as consequence of rising temperatures, pattern alteration of precipitation, and altered seasonal cycle, needing adaptive management. In this paper, artificial intelligence (AI) is used for the integration of machine learning (ML), deep learning (DL) and remote sensing technologies in order to increase precision pest prediction and control. Pest can be monitored and identified in real time using the artificial Intelligence driven models like convolutional and recurrent neural networks to predict risk, predicting when something is going to happen so that one can act anytime the risk seems high. Despite these limitations in accessing data, generalization of the model, and computational constraints, AI has great potential in controlling the climate induced pest outbreaks. This study highlights critical necessity of durable and resilient agriculture to which is needed interdisciplinary collaboration, better data quality and incorporation of AI in ecological modeling.