Prediction and prospects of pest occurrence in agricultural landscapes based on artificial intelligence

Author of the article:YANG Lu-Jia** MEN Xing-Yuan***

Author's Workplace：Shandong Key Laboratory for Green Prevention and Control of Agricultural Pests, Institute of Plant Protection, Shandong Academy of Agricultural Science, Jinan 250100, China

Key Words：agricultural landscape; pest prediction; artificial intelligence; space-time modeling; deep learning

Abstract： The occurrence, spread and damage of pests within agricultural landscapes lead to reduced crop yields, lower quality of agricultural products, and impacts on the stability and sustainability of agroecosystems. As a key component of agricultural management, pest population monitoring and occurrence prediction contributes to early warning and rational prevention and control decisions and plays an important role in ensuring food security and promoting ecological agriculture. Traditional pest prediction methods mainly use population surveys, environmental monitoring, landscape remote sensing, and statistical modeling based on historical datasets, while they still face significant limitations in responding to the change of global climate and agricultural landscapes, improving prediction accuracy, and achieving dynamic monitoring. With the advancements of artificial intelligence (AI) technology, pest prediction systems based on machine learning and deep learning in agricultural landscapes have become a hot research topic. This study provides a systematic overviews of the evolutionary trajectory of pest prediction technologies and the theoretical foundations and applications of intelligent early warning mechanisms in agricultural landscapes, focusing on the potential application of AI-driven pest prediction in agricultural landscapes based on AI, the practical application of deep learning models for intelligent pest recognition, and the potential of multimodal data fusion technologies in pest-natural enemy interactions monitoring. This study also analyzes in detail the current technical bottlenecks and challenges that AI models may face in pest prediction, and foresees that by strengthening interdisciplinary cooperation and technical integration, the in-deep application of AI for pest prediction in agricultural landscapes will further optimize agro-ecological management strategies, and improve the precision and timeliness of pest prevention and control.