Field surveys conducted by experts employ systematic sampling techniques, such as quadrats, transects, and plot-based methods, to document the presence, abundance, and spatial distribution of plant species within agroecosystems [12][13]. These methodologies provide high-resolution data, enabling precise species identification and the collection of detailed trait-based metrics, such as biomass, seed mass, and specific leaf area. A notable example is a recent study in the oasis agroecosystems of Xinjiang (China) where researchers deployed over 600 plots using a combination of 20 × 20 m and 1 m quadrats to catalog 62 alien plant species. The study successfully linked plant functional traits to invasion risk, achieving high predictive performance (AUC = 0.981) [14]. The key advantage of such surveys lies in their accuracy and depth of ecological insight, particularly for rare or morphologically similar species that may evade automated detection methods. However, these surveys are highly labor- and cost-intensive. Comparative analyses, such as those performed for garlic mustard (Alliaria petiolata (M. Bieb.) Cavara & Grande), have shown that while random sampling provides comprehensive ecological data, simpler roadside transects can detect similar prevalence levels using only a quarter of the required person-hours [15]. Thus, while expert-led field surveys remain a gold standard for ecological assessments, their scalability and efficiency continue to pose significant operational challenges.

Specimens collected, pressed, and stored in herbaria provide historical and taxonomic context for IAP occurrence, allowing frequency and range analyses over time [16]. While invaluable for retrospective studies, they suffer from spatial bias; most collections cluster near accessible areas and do not facilitate real-time monitoring.

Farmer-based observations and local surveys feed into early detection systems, especially in resource-limited contexts. Integrated methods like mixed surveys, ecological, household, and focus groups form a large part (17%) of IAP studies examining human-plant interactions [17]. These approaches facilitate stakeholder engagement and wider coverage, but may lack taxonomic precision and consistency.

Given major threats represented by biological invasions, both in man-made and natural ecosystems, efforts are made to avoid invasions, eradicate, and/or control established invaders. It has long been recognized that Geographical Information Systems (GIS) could contribute to this through mapping actual invader spread or areas at risk of invasion. GIS could also be used as a synthesizing tool for the undertaken IAP management actions [18]. GIS is primarily a spatial analysis and visualization framework, while modern technologies such as UAVs, hyperspectral sensors, and machine learning provide new data inputs that enhance GIS-based mapping and decision-making. GIS mapping, while powerful, has limitations related to data accuracy, complexity, cost, and expertise required for processing high-resolution imagery. Furthermore, GIS limitations in invasive plant mapping are particularly due to challenges in accurately identifying and differentiating species, especially when they are hidden or mixed within vegetation canopies [19][20].

A US Forest Service review emphasizes that inventory methods must align with explicit land-use goals; misalignment leads to underused or irrelevant data [12]. Further, remote sensing studies note that coarse spatial and spectral resolutions hinder early detection, particularly for understory or low-density invaders [21]. Table 1 summarizes the advantages and limitations of the commonly used methods to survey and monitor IAP. Despite their limitations, traditional methods (Table 1) form the baseline for new approaches: they supply ground-truth data crucial for calibrating remote sensing models and validating citizen science observations.

Remote sensing platforms, satellite, Unmanned Aerial Vehicles (UAVs, mainly drones), aerial, and Light Detection And Ranging (LiDAR), combined with Machine Learning (ML), are transforming IAP monitoring in agro-ecosystems. A comprehensive review by [1] confirmed the effectiveness of supervised classifiers, e.g., Random Forest (RF) or Support Vector Machine (SVM), and weakly supervised methods in detecting invasive plant species from multispectral and hyperspectral data [2]. Satellite sensors (e.g., Sentinel 2) using vegetation indices, e.g., Normalized Difference Vegetation Index (NDVI) or red-edge bands, yielded detection accuracies ranging from 76% - 92% when distinguishing IAP from native vegetation [1]. UAV-based hyperspectral and LiDAR systems enable centimeter-level resolution, which is especially valuable for identifying early-stage infestations across fragmented agricultural landscapes [1]. However, challenges remain, such as cloud cover, spectral similarity among co-occurring species, and the need for extensive labeled training datasets, which limit broader deployment. Advancements like semi-supervised learning, active learning, and automated labeling are being pursued to address these obstacles and reduce dependency on ground truthing.

Table 1. Traditional IAP surveying and monitoring methods.

Machine learning and Deep Learning (DL) are essential for processing large-scale remote sensing data efficiently. A 2024 review highlighted the use of RF, SVM, Classification And Regression Trees (CART), XGBoost, and Convolutional Neural Networks (CNNs) in classifying IAPs, achieving up to >95% accuracy for some species [2]. Deep learning models, especially CNNs, excel in feature extraction from hyperspectral imagery, further enhancing classification capabilities across complex plant communities. Furthermore, DL-powered approaches have enabled real-time species detection using action-cameras mounted on vehicles or UAVs, with accuracies around 89% for one of Japan’s 100 worst invasive species Solidago altissima L. These findings suggest that effective identification models can be prepared using inexpensive cameras, and this may open possibilities for citizen science in this field [4].

Citizen science has emerged as a powerful, low-cost complement to technological monitoring. Platforms such as iNaturalist, European Alien Species Information Network (EASIN), and regional apps have demonstrated success in detecting IAPs through public engagement [31]. For example, European LIFE medCLIFFS volunteers recorded thousands of invasive plant sightings with minimal training [32]. Australia’s Atlas of Living Australia developed a Biosecurity Alerts service that delivered 99% of alerts via citizen-submitted observations [33]. Despite challenges in data validation, new quality-control tools, such as expert-curated validation and algorithmic anomaly detection, are enhancing reliability [34].

Emerging Internet-of-Things (IoT) systems integrate AI-powered smart chips and environmental sensors for continuous, in-situ monitoring. A new 2025 review described IoT-based systems capable of detecting IAP presence, phenology, and physiological stress via environmental variables, allowing real-time alerts and predictive management responses [35]. While still experimental, these systems promise high precision, decreased herbicide use, and less collateral damage, though cost, environmental stability, and policy barriers must be addressed [36].

Projects like Biodiversa+’s CamAlien exemplify cross-disciplinary integration, mounting cameras on vehicles to collect thousands of roadside images processed using Pl@ntNet’s deep learning apps. This approach supports harmonized, large-scale monitoring across Europe; expert review of low-confidence images enhances quality under FAIR data protocols [39]-[41]. Similarly, smart chip IoT platforms embed micro sensors in the field to track environmental conditions (e.g., soil, humidity, phenology), alerting on IAP presence and physiological stress. These systems integrate AI edge-processing for real-time analytics and decision automation.

Integrated systems now frame Early Detection & Rapid Response (EDRR) workflows: 1) detection via RS + IoT + public reports; 2) validation through expert assessments; 3) rapid management via mobile/AI tools. They align with national regulatory priorities, as documented in U.S. federal EDRR capacity reviews [42]. Advanced AI modules like convolutional neural networks are merging data streams, satellite, UAV, and in-situ sensors to automatically generate risk maps and trigger alerts. Frameworks used in pest detection (e.g., locust, bark beetle) are now being adapted for plant invasions in agroecosystems.

Successful integration requires strong stakeholder networks: scientists, farmers, extension agents, citizens, and policymakers. Platforms such as eBird, iNaturalist, and EASIN exemplify effective data-sharing ecosystems [43]. Governance is aided by standardized data pipelines (image, sensor, report), centralizing datasets in FAIR-aligned archives like Electronic Research Data Archive (ERDA) and Global Biodiversity Information Facility (GBIF). EU projects stress protocol harmonization across borders and long-term institutional collaboration [39][40].

Interdisciplinary and integrated monitoring systems for IAPs offer a transformative approach by combining technologies such as remote sensing, IoT devices, citizen science platforms, and artificial intelligence into cohesive Early Detection and Rapid Response (EDRR) frameworks (Figure 1). These systems provide multiple benefits: they enhance comprehensiveness by integrating data from various platforms which provide latency reduction in IAP detection; ensure scalability, as citizen science and IoT tools can function across diverse geographical and ecological contexts; increase resilience which enable swift coordinated responses to new invasions; and offer flexibility, as such systems can often be applied retrospectively, even without prior planning [44]. Furthermore, Proactive risk management involves identifying potential risks before they occur and taking preventive actions to minimize their impact. It is a forward-looking strategy that focuses on preventing risks rather than reacting to them after they have happened [45]. However, several challenges hinder their implementation. Technical interoperability remains a major barrier, as aligning datasets from satellites, sensors, camera networks, and crowd-sourced observations requires robust integration frameworks [46]. Cost and scalability concerns arise from the infrastructure needed for large-scale IoT and camera deployments, which require long-term investment and maintenance. Policy and governance obstacles, including data privacy regulations and coordination across sectors, often delay or constrain system rollout. Moreover, a lack of standardized protocols for data collection, validation, storage, and sharing limits the comparability and usability of monitoring outputs [42][47][48]. Several case studies illustrate the potential of these systems in action: the CamAlien project in Europe uses highway-mounted cameras and Pl@ntNet-based processing to detect IAPs in real-time, applying consistent protocols across multiple countries [41]; IoT smart chip arrays in agricultural settings monitor phenological and stress markers to identify early invasion cues [35][49] and EDRR frameworks modeled after U.S. federal programs integrate detection, verification, and intervention via mobile platforms and AI, setting a precedent for rapid and adaptive IAP management.

Figure 1 illustrates the interconnected components of an integrated monitoring system designed for the detection and management of potentially IAPs in agroecosystems. Each node represents a core method or technology, while dashed lines depict the bidirectional flow of data and insights among components. Remote sensing supplies large-scale imagery that feeds into machine learning algorithms, enabling automated identification of invasive species across broad areas. Field surveys provide critical ground-truth data that validate and refine these models, improving their reliability and ecological accuracy. Citizen science platforms contribute near-real-time, crowd-sourced observations that can enhance species distribution mapping and trigger early alerts. Meanwhile, IoT smart chips capture continuous environmental data, including early plant stress signals, which can be analyzed using AI-driven decision-support systems. The integration of these components creates a synergistic, adaptive, and scalable monitoring network. This framework not only improves early detection and response times but also supports more effective, data-informed strategies for the long-term ecological management of IAPs in dynamic agricultural landscapes.

Figure 1. Diagram of integrated monitoring system for potentially invasive alien plants (IAPs) in agro-ecosystems.