Innovative Approaches to Surveying and Monitoring Potentially Invasive Alien Plants in Agro-Ecosystems-Review

作者：Najla Sayari, Mehdia Fraj, Bochra Bejaoui, Giuseppe Brundu, Vanessa Lozano, Maryline Abert Vian, Naceur M’Hamdi, Vincent Lequart, Nicolas Joly, Patrick Martin · 发表于：American Journal of Plant Sciences · 年份：2026 · DOI：10.4236/ajps.2026.174028 · 被引用次数：1 · 研究领域：Species Distribution and Climate Change、Smart Agriculture and AI、Remote Sensing in Agriculture

Invasive Alien Plants (IAPs) pose significant threats to biodiversity, crop productivity, and ecosystem services within agroecosystems. Effective surveillance and monitoring are critical for early detection and rapid management. This review synthesizes recent advances and interdisciplinary innovations in IAP monitoring, with a focus on their application in agricultural landscapes. Traditional methods, including field surveys, herbarium records, and farmer reports, provide foundational data but suffer from limitations such as spatial bias, high labor costs, and limited scalability. In response, emerging technologies such as remote sensing, Machine Learning (ML), Deep Learning (DL), citizen science platforms, and smart-chip IoT systems are being integrated into modern monitoring frameworks. Remote sensing coupled with ML enables automated detection across large areas, while citizen science applications expand spatial coverage and public engagement. Smart sensors and AI-driven analytics offer continuous, real-time monitoring and risk assessment. Integrated systems, such as Early Detection and Rapid Response (EDRR) platforms, increasingly combine these tools to support informed decision-making. Despite these advances, challenges remain, including data quality issues, model generalizability, interoperability across platforms, and socio-political barriers. This review highlights key research gaps, including the need for standardized data protocols, federated learning, and ethical f...