Topsoil organic matter content (OMC), particularly within the upper 30 cm, is critical for soil fertility, ecosystem functioning, and resilience. In Mediterranean environments, OMC is highly vulnerable to soil water erosion (SWE), a process intensified by increasingly frequent and intense rainfall. While SWE research has largely focused on mineral soil loss, its effects on OMC, especially the balance between depletion in source areas and accumulation in depositional zones, remain poorly quantified at regional scales. This study applies advanced analytical approaches, including multilevel meta-analytic models, to examine the spatial dynamics of OMC redistribution under SWE and to evaluate how scale and methodology influence reported outcomes. A meta-analysis was conducted using data from 71 studies across Mediterranean landscapes. Due to the limited availability of land-use-specific studies, the analysis adopts a broad regional perspective rather than stratification by management type. Results reveal extremely high heterogeneity, driven primarily by methodological differences, spatial scale, and timing. Mean effects varied by study type: field experiments showed a weakly negative association between SWE and OMC (beta = -0.0421, k = 68), rainfall-based studies showed a positive relationship (beta = 0.7209, k = 66), and simulation studies indicated a mild positive trend (beta = 0.0835, k = 14), all with high heterogeneity (I-2 > 90 %). Overall, the findings demonstrate that SWE does not uniformly result in OMC loss but often reflects spatial translocation. These results highlight the need for improved methodological consistency and ecologically grounded frameworks to interpret OMC dynamics in Mediterranean erosion-prone landscapes.

Late blight (LB), caused by Phytophthora infestans, remains one of the most destructive diseases in potato (Solanum tuberosum) production. Early detection, before the emergence of visual symptoms, is critical for timely intervention and sustainable disease management. However, previous research has highlighted the challenges of consistent LB detection under variable field conditions, growth stages, and resistance levels. This study investigates the potential of machine learning models, specifically a backpropagation neural network (BPNN), for the early detection of LB across diverse conditions. Spectral data (400–1650 nm) were collected using a handheld spectrometer from two potato cultivars with differing LB resistance levels (low vs. moderate) at two distinct growth stages (establishment vs. vegetative). The BPNN model achieved high accuracy rates (>95%) from the first day post-inoculation, enabling LB prediction up to four days before visual symptom onset. When data were pooled across cultivars and growth stages, a BPNN model trained on time-series data maintained an overall accuracy of 94%, highlighting the importance of temporal data in enhancing detection robustness. Notably, the most informative wavelengths were identified in the short-wave infrared (1100–1650 nm) range. However, the use of the full spectral range proved necessary for reliable detection across varied conditions, as no single wavelength subset consistently provided optimal results. These findings support the integration of BPNN-based spectral tools for precise, pre-symptomatic LB detection, offering a promising pathway for precision agriculture to reduce fungicide application and secure potato yields.

Accurate differentiation between crops and weeds is a critical requirement for site-specific weed management (SSWM), yet spectral similarity between plant species and variability in field imaging conditions continue to limit classification accuracy. In this study, we evaluated the potential of shellac-based Pickering emulsions as crop signaling compounds for enhancing spectral separability between crops and weeds in remote sensing applications. Pickering emulsions stabilized by shellac-based nanoparticles were prepared using either paraffin oil (PO) or medium-chain triglyceride (MCT) oil at different concentrations. Preliminary experiments demonstrated that MCT-based emulsions produced stronger and more persistent spectral angle (SA) deviations than PO-based emulsions, particularly at 20% and 40% oil concentrations. In the second stage, hyperspectral imaging was used to evaluate tomato and pepper plants treated with selected MCT emulsions against eight weed species over a 14-day period. Both formulations enhanced spectral differentiation between crops and weeds; however, pepper plants combined substantial spectral responses with minimal biomass reduction, whereas tomato plants exhibited phytotoxic effects, particularly under the 40% treatment. Wettability analyses revealed distinct leaf wetting behaviors between the crop species, with tomato leaves showing more continuous emulsion spreading patterns that may have contributed to the observed phytotoxicity. Overall, the results demonstrate the potential of shellac-based Pickering emulsions as practical crop signaling compounds for improving crop-weed spectral separability in SSWM. The favorable performance observed in pepper plants supports further development and future field-scale evaluation of this approach for precision agriculture applications.

Accurate differentiation between crops and weeds is a critical requirement for site-specific weed management (SSWM), yet spectral similarity between plant species and variability in field imaging conditions continue to limit classification accuracy. In this study, we evaluated the potential of shellac-based Pickering emulsions as crop signaling compounds for enhancing spectral separability between crops and weeds in remote sensing applications. Pickering emulsions stabilized by shellac-based nanoparticles were prepared using either paraffin oil (PO) or medium-chain triglyceride (MCT) oil at different concentrations. Preliminary experiments demonstrated that MCT-based emulsions produced stronger and more persistent spectral angle (SA) deviations than PO-based emulsions, particularly at 20% and 40% oil concentrations. In the second stage, hyperspectral imaging was used to evaluate tomato and pepper plants treated with selected MCT emulsions against eight weed species over a 14-day period. Both formulations enhanced spectral differentiation between crops and weeds; however, pepper plants combined substantial spectral responses with minimal biomass reduction, whereas tomato plants exhibited phytotoxic effects, particularly under the 40% treatment. Wettability analyses revealed distinct leaf wetting behaviors between the crop species, with tomato leaves showing more continuous emulsion spreading patterns that may have contributed to the observed phytotoxicity. Overall, the results demonstrate the potential of shellac-based Pickering emulsions as practical crop signaling compounds for improving crop-weed spectral separability in SSWM. The favorable performance observed in pepper plants supports further development and future field-scale evaluation of this approach for precision agriculture applications.

The Mediterranean Sea has a substantial volume of maritime traffic, including many tankers ferrying oil from eastern sources to western refineries. This critical maritime front, vital for trade and connectivity, also poses a significant risk of oil spills due to these busy shipping routes. The conventional methods for early oil spill detection have encountered numerous challenges, primarily due to the complex and variable nature of spill events. This study promotes an anomaly-based approach, treating oil spills as environmental outliers, and utilizes baseline water parameter comparisons to detect and monitor sea oil spills effectively. This approach leverages satellite data, employing a combination of remote sensing techniques and advanced machine learning technologies. The end goal is providing a platform for monitoring and detecting oil spills, to empower users worldwide to conduct regular assessments, contributing to the proactive prevention of future environmental damage.

Non-Destructive Testing (NDT) plays a crucial role in the aerospace industry by ensuring aircraft structural integrity and safety, while also supporting operational efficiency. Aircraft components are subject to extreme conditions and stresses, making routine inspections vital to prevent failures. Traditional NDT techniques—such as ultrasonic testing, radiography, and eddy current testing—have long been relied upon in aerospace maintenance, providing valuable insights into material integrity. However, these methods have limitations. Ultrasonic testing effectively detects internal flaws but depends on material acoustic properties and requires coupling media, limiting its applications. Radiography offers high-resolution imaging yet involves ionizing radiation and is constrained by material thickness. Eddy current testing is efficient for surface flaws but is limited by the material's conductivity and permeability. Recent advancements in NDT, including thermography and shearography, have introduced enhanced capabilities. Thermography leverages infrared imaging to identify surface temperature variations indicative of subsurface defects like delamination and corrosion, allowing for rapid, non-contact inspection of large areas in real-time. However, it is sensitive to environmental conditions and material emissivity. Shearography, an optical technique that assesses surface deformation under stress, reveals defects by analyzing interference patterns from laser light, particularly useful for detecting cracks and voids. Despite its sensitivity to stress-induced anomalies in composites, it requires a stable environment due to susceptibility to vibrations. This study explores signal-based data fusion, integrating traditional and advanced NDT techniques to enhance defect detection and characterization in aerospace materials. By combining multiple signals, data fusion and deep learning improve NDT reliability, offering a comprehensive understanding of material integrity. This approach enables detection of various defect types, assists inspectors by reducing noise and false detections, and enhances precision through combined signals. Our findings highlight that data fusion markedly improves NDT effectiveness in complex, multi-layered composite aerospace materials, raising industry standards in both efficiency and accuracy.

Halophila stipulacea, a small-leaved, fast-spreading seagrass, dominates subtidal meadows in the northern Gulf of Aqaba (GoA), where its distribution is affected by seasonal flash floods and climate-related stressors. Accurate monitoring of such meadows remains challenging due to their fine-scale structure and growth in optically complex, turbid waters. Traditional field-based mapping is logistically limited in scope, while many remote sensing approaches underperform in deeper or noisy marine environments. In this study, we present an AI-powered, reproducible workflow for subtidal seagrass mapping, integrating multi-source satellite reflectance data (VENµS and Sentinel-2) with field-validated machine learning models. Five regression algorithms (RT, RF, GBRT, SVR, and XGBR) were trained and tested using in situ data and satellite-derived spectral inputs, including raw bands and vegetation indices. XGBR models trained on VENµS imagery outperformed all others (R2 = 0.97; RMSE = 0.21), demonstrating strong predictive performance even in dynamic coastal zones. We further examined the influence of episodic disturbances such as floods on spatial patterns of vegetation loss and regrowth. Beyond performance benchmarking, the workflow contributes to ecological informatics by producing spatially explicit, scalable predictions designed with transparency and interoperability in mind. The pipeline supports standardized data ingestion, flexible ML configuration, and modular visualization of outputs, enabling integration into digital libraries, semantic search tools, and spatial decision-support systems. This work illustrates how combining remote sensing, structured ecological data, and AI-based inference can improve knowledge synthesis in marine ecology. It offers a transferable methodology for monitoring invasive species, supporting conservation planning, and evaluating ecosystem resilience under climate-driven pressures.

Sunflower broomrape (Orobanche cumana) poses a severe threat to sunflower crops, parasitizing their roots and hindering plant growth. Current control methods, which typically rely on uniform herbicide applications, are economically inefficient and environmentally damaging. This study investigates the use of unmanned aerial vehicle (UAV)-based multispectral imaging to detect broomrape-infected sunflowers by analyzing temporal patterns in spectral vegetation indices (VIs). Over four imaging campaigns conducted during early subsoil parasitic stages, multispectral data were collected and processed to compute ten VIs. These VIs, reflecting changes in canopy reflectance over time, were then analyzed using various machine learning models, including a pattern recognition neural network (PRNN). Results showed that the PRNN model, trained on time-series data, achieved an overall accuracy of 84.8% and a true positive rate of 80.4% in detecting broomrape infection, emphasizing the strength of utilizing temporal data for enhancing detection accuracies. Pixel-level reconstruction maps revealed varying spectral responses within infected canopies, highlighting the importance of accounting for this heterogeneity. This study demonstrates the potential of UAV-based multispectral imaging combined with advanced machine learning (ML) techniques for early detection of broomrape infestations in sunflower crops, offering insights for managing similar infestations in other crops.

This study integrates remote sensing and ground-based soil analysis to assess long-term land restoration in Mediterranean ecosystems. By combining top-down satellite-derived indicators with bottom-up soil health metrics, the research provides a robust framework for monitoring restoration dynamics. Sentinel-2 data were employed to track vegetation and soil changes over time, while detailed field analyses—including chemical, physical, and spectroscopic tests—offered site-specific insights into soil health. The integration of these approaches enables the development of a predictive model, linking remotely sensed data with soil parameters to support continuous monitoring and adaptive management. The findings highlight the potential for scalable, accessible tools to assist land managers and farmers in evaluating restoration progress and guiding sustainable practices.

Rodent damage significantly affects agriculture around the world. Rodenticides can sometimes control pests, but they are costly, may cause secondary poisoning to nontarget wildlife, and can become less efficient over time due to bait shyness and resistance. Using wildlife as biological pest control agents, particularly barn owls (Tyto spp.), has been suggested as an alternative. Barn owl nest boxes and hunting perches have been added to increase predator pressure, yet few studies have examined their effectiveness. We conducted a field study in forty-five 10 × 10 m2 plots to compare three treatments (biological pest control by adding hunting perches, 1080 rodenticide, and control) on rodent (vole) activity and crop health (alfalfa, Medicago sativa) using unmanned aerial system (UAS) remote sensing and ground surveys. Additionally, we used 24/7 video cameras and a machine learning (YOLOv5) object detection algorithm to determine whether hunting perches increase the presence of diurnal and nocturnal raptors. Rodent activity increased during the study and did not vary among the treatments across all three treatment groups, indicating that neither the biological pest control nor the rodenticides prevented the rodent population from increasing. Moreover, the vegetation indices clearly showed that the alfalfa has become increasingly damaged over time, due to the rising damage caused by rodents. There were significantly more raptors in plots with hunting perches than in control plots and those treated with rodenticides. Specifically, barn owls and diurnal raptors (mainly black-shouldered kites) spent 97.92% more time on hunting perch plots than rodenticide plots and 97.61% more time on hunting perch plots than control plots. The number of barn owls was positively related to vole activity, indicating a bottom-up process, while the number of black-shouldered kites was unrelated to vole activity. Even though hunting perches effectively increased the presence and activity of diurnal and nocturnal raptors, rodent populations increased. Future research should investigate whether hunting perches can increase raptor populations and improve crop health in crops beyond alfalfa, which is known to be particularly challenging to control for voles.

The Mediterranean region faces increasing land degradation, desertification, and climate change threats. Traditional assessment methods for land restoration are often time-consuming and subjective. This study leverages satellite-based remote sensing, specifically Sentinel-2 and Sentinel-1 data, to provide a comprehensive and objective approach to monitoring restoration initiatives in food forest in Beit Lehem of the Galilee, northern Israel. Sentinel-2 high-resolution multispectral imagery enables detailed tracking of vegetation health through indices like SAVI, PSRI, and NDWI. Additionally, Sentinel-1 SAR data offers insights into microtopography changes and soil moisture monitoring. Our comparative analysis of restored and non-restored areas reveals significant improvements in vegetation health, soil moisture, and microtopographic stability in restored sites. By utilizing remote sensing technologies and a comparative approach, this study offers a detailed assessment of long-term restoration processes in the Mediterranean region, contributing to sustainable land management and ecosystem restoration practices. This work provides valuable insights for policymakers and land managers seeking to implement effective restoration strategies in the Mediterranean region.

During the 5th and 6th centuries, the economic hinterland of the city of Gaza encompassed a network of settlements that supplied the city with agricultural produce. Water was collected in underground cisterns from rooftops and courtyards. While almost nothing remained of the above-ground structures since the settlements collapsed, soil erosion exposed these cisterns, which now serve as well-preserved indicators of the location and levels of the houses - that supplied the water. The study examines to what extent the land abandonment in the late Byzantine - early Islamic period resulted in denudation and soil property changes. We hypothesize that the abandonment of settlements and agriculture intensified soil erosion and amplified gully development. Approximately 140 cisterns were mapped in the study area, mainly north of the ephemeral Nahal Gerar stream. The height of the cisterns above the ground was used to calculate the denudation rate (DR) since abandonment. Findings indicate that over relatively flat terrains (1-5 %), cisterns protrude 0.5-1.2 m above the surface. Considering abandonment in the 6th or 11th century, DR was calculated as 0.35-0.85 mm/yr or 0.5-1.2 mm/yr, respectively. Over steeper slopes (10-12 %), along river banks and incised gullies, extensive bank erosion occurred, leading to the exposure of cisterns up to 2.5 m; DR = 1.8 to 2.5 mm/yr, depending on the abandonment time. The settlements' distribution and the surface topography directly correlate: in settled areas, Terrain Roughness Index (TRI) values are higher compared to other areas with the same lithology and rainfall amount. Following abandonment, decaying houses resulted in the complete disintegration of mud bricks, increasing the proportion of fine soil fractions. Cisterns acted as sedimentation basins, trapping surface-derived sediments and debris, including degraded brick material. This process influenced the mechanical composition of soils, affecting soil erosion and land degradation.

The Mediterranean region's arid and semi-arid ecosystems are increasingly threatened by land degradation, desertification, and climate change. This study uses remote sensing indicators from Sentinel-2 satellite data to assess long-term land restoration efforts. Focusing on two case studies-the Heraklion Forest restoration and the transformation of agricultural land in Beit Lehem of Galilee into a food forest model-we employed six indicators: Normalized Difference Soil Index (NDSI), Soil-Adjusted Vegetation Index (SAVI), Plant Senescence Reflectance Index (PSRI), Structure-Insensitive Pigment Index (SIPI), Normalized Difference Infrared Index (NDII), and Normalized Difference Water Index (NDWI). Our comparative analysis between restored areas and nearby non-restored control areas revealed significant improvements in vegetation health and water content in the restored areas. These findings underscore the effectiveness of restoration activities in enhancing ecosystem resilience to environmental stressors. This study demonstrates the critical role of remote sensing technologies in monitoring and managing land restoration, providing valuable data for sustainable land management in the Mediterranean region. Continuous monitoring and adaptive management, supported by robust scientific evidence, are essential for achieving long-term restoration success and resilience against climate change.

Chickpea (Cicer arietinum L.) is a key legume crop grown in many semi-arid areas. Traditionally, chickpea is a rainfed spring crop, but in certain countries it has become an irrigated crop. The main objective of this study was to evaluate the ability of Unmanned Aerial Systems (UAS) imaging platform with an integrated RGB camera to provide estimations of leaf area index (LAI), biomass, and yield for chickpea during the irrigation period. Two field trials were conducted in 2019 and 2020, in which chickpea plants were subjected to five and six irrigation regimes, respectively. Eight vegetation indexes (VIs) and three morphological parameters were estimated from the RGB images. In parallel, biomass was determined, LAI was measured manually, and yield was determined at full maturity. In total, 294 plant samples were acquired and analyzed over the two years. Firstly, each of the VIs and morphological parameters were correlated separately against the two biophysical parameters and yield. Then, all the VIs and morphological parameters were analyzed together, and two statistical models, partial least squares regression (PLS-R) and support vector machine (SVM); were used to predict biomass and LAI. The yield was predicted using multi-linear regression (MLR). When each index or morphological parameter was analyzed separately, plant height and some of the VIs provided adequate predictions of the biophysical parameters in 2019 (R2 values >= 0.50) but failed (R2 values <= 0.25) in 2020. The integration of the VIs with the morphological parameters and the use of PLS-R and SVM models increased the accuracy level for both biophysical parameters (R2 ranged from 0.31 to 0.96) and mitigated the lack of consistency between the years. The SVM model was superior to the PLS-R model in both biophysical parameters. The R2 values for the combined 2019 and 2020 biomass model increased, at the model-testing stage, from 0.62 to 0.96 and the RMSE values dropped from 1778 to 490 kg ha-1. The ability of the SVM model to estimate chickpea biomass and LAI can provide convenient support for different management decisions, including timing and amount of irrigation and harvest date.

The evolution of UAS and the potential of applying these types of equipment and technologies offer the possibility to use them in various fields of society, including monitoring environmental processes. The information provided by the UAS helps us gain a complete and complex understanding of the phenomena that take place in nature. Latest developments in UAS platforms and sensor technology facilitate cost-effective data collection through simultaneous multimodal data collection at a very high spatial resolution for environmental studies. In this context, one challenge is represented by harmonizing and providing protocols for data collection applicable across a broad range of environments and conditions. In this context, a network of scientists is cooperating to develop and promote harmonized mapping strategies and provide operational guidance to ensure best practices for data collection and interpretation. The culmination of these efforts is summarized in the present chapter to describe the latest and the most efficient approach to UAS applications today. This synthesis identifies many interdependencies of each step in the data collection and processing chain and outlines approaches to formalize and ensure a successful workflow toward product development. Given the number of environmental conditions, constraints, and variables that could be explored by UAS platforms, it is impractical to provide protocols that are universally applied under all scenarios. However, it is possible to collate and systematically order the fragmented knowledge on UAS data collection and analysis to identify the best practices that can ensure the streamlined and rigorous development of both scientific and applied UAS practices.

A series of urban wildfires that engulfed the Mediterranean in recent decades raised the problem of introducing urban vegetation into fire management. While the Wildland-Urban Interface (WUI) concept includes urban areas in the fire risk analysis exists today, the common approach used in WUI for cities as fire-resistant territories proved to be a failure in practice since wildfire not only penetrates the urban areas but also crosses them by jumping from one green patch to another. Understanding urban areas as flammable demands evaluating the ability to predict the wildfire spread in the city. Among the existing risk assessment approaches only fire behaviour modelling describes the physical processes that occur in an urban wildfire case: the rapid fire spread in the city by ember attacks can be simulated by the spotting analysis. Implementation of the mentioned method requires information about the fuel-related areas within the urban landscape. The suggested method for identifying urban fire-supporting areas based on the vegetation proportion threshold allows a highly accurate prediction of fire spread distance for a certain time step. The proposed approach serves the fire behaviour simulation in large (10-20 ha) urban areas, which previously was challenging due to the high computational load. The outcomes of the presented study can support fire management in an urban landscape both by producing fire risk maps and assessing the spatial distribution of fire-supporting patches for better fuel treatment planning.

Salt weathering is one of the most detrimental agents in the deterioration of historic wall paintings. Unfortunately, it is most noticeable once it reaches the surface, and the damage has already been done. Its elusive nature and its integration into the historic structures make it difficult to accurately detect soluble salts at the sub surfaces of wall painting non-destructively. Detailed detection in a delicate artifact such as wall paintings requires custom methodology. The study's case study is the wall painting in Herodium. The wall paintings were severely damaged by salt weathering. Although great efforts were invested in restoring and protecting the paintings their state is still unstable. This study offers a new perspective on the matter utilizing the adaption of industrial protocols based on active Infrared thermography (IRT) with data processing via thermographic signal reconstruction (TSR). The study's primary goal was to optimize the inspection methodology and assist the conservation process with knowledge of soluble salt hazards hidden at the subsurface.

Smart vehicles with embedded Autonomous Vehicle (AV) technologies are currently equipped with different types of mounted sensors, aiming to ensure safe movement for both passengers and other road users. The sensors’ ability to capture and gather data to be synchronically interpreted by neural networks for a clear understanding of the surroundings is influenced by lighting conditions, such as natural lighting levels, artificial lighting effects, time of day, and various weather conditions, such as rain, fog, haze, and extreme temperatures. Such changing environmental conditions are also known as complex environments. In addition, the appearance of other road users is varied and relative to the vehicle’s perspective; thus, the identification of features in a complex background is still a challenge. This paper presents a pre-processing method using multi-sensorial RGB and thermal camera data. The aim is to handle issues arising from the combined inputs of multiple sensors, such as data registration and value unification. Foreground refinement, followed by a novel statistical anomaly-based feature extraction prior to image fusion, is presented. The results met the AV challenges in CNN’s classification. The reduction of the collected data and its variation level was achieved. The unified physical value contributed to the robustness of input data, providing a better perception of the surroundings under varied environmental conditions in mixed datasets for day and night images. The method presented uses fused images, robustly enriched with texture and feature depth and reduced dependency on lighting or environmental conditions, as an input for a CNN. The CNN was capable of extracting and classifying dynamic objects as vehicles and pedestrians from the complex background in both daylight and nightlight images.

Fire risk assessment on the wildland–urban interface (WUI) and adjoined urban areas is crucial to prevent human losses and structural damages. One of many interacting and dynamic factors influencing the structure and function of fire-prone ecosystems is vegetation ignitability, which plays a significant role in spreading fire. This study sought to identify areas with a high-level probability of ignition from time series multispectral images by designing a pattern recognition neural network (PRNN). The temporal behavior of six vegetation indices (VIs) before the considered wildfire event provided the input data for the PRNN. In total, we tested eight combinations of inputs for PRNN: the temporal behavior of each chosen VI, the temporal behavior of all indices together, and the values of VIs at specific dates selected based on factor analysis. The reference output data for training was a map of areas ignited in the wildfire. Among the considered inputs, the MSAVI dataset, which reflects changes in vegetation biomass and canopy cover, showed the best performance. The precision of the presented PRNN (RMSE = 0.85) in identification areas with a high potential of ignitability gives ground for the application of the proposed method in risk assessment and fuel treatment planning on WUI and adjoined urban areas.

Spectral vegetation indices (VIs) are a well-known and widely used method for crop state estimation. These technologies have great importance for plant state monitoring, especially for agriculture. The main aim is to assess the performance level of the selected VIs calculated from space-borne multispectral imagery and point-based field spectroscopy in application to crop state estimation. The results obtained indicate that space-borne VIs react on phenology. This feature makes it an appropriate data source for monitoring crop development, crop water needs and yield prediction. Field spectrometer VIs were sensitive for estimating pigment concentration and photosynthesis rate. Yet, a hypersensitivity of field spectral measures might lead to a very high variability of the calculated values. The results obtained in the second part of the presented study were reported on crop state estimated by 17 VIs known as sensitive to plant drought. An alternative approach for identification early stress by VIs proposed in this study is Principal Component Analysis (PCA). The results show that PCA has identified the degree of similarity of the different states and together with reference stress states from the control plot clearly estimated stress in the actual irrigated field, which was hard to detect by VIs values only.