Unmanned Aerial Vehicle Image Data Research Articles

Improving the Estimation of Rice Crop Damage from Flooding Events Using Open-Source Satellite Data and UAV Image Data

Having an additional tool for swiftly determining the extent of flood damage to crops with confidence is beneficial. This study focuses on estimating rice crop damage caused by flooding in Candaba, Pampanga, using open-source satellite data. By analyzing the correlation between Normalized Difference Vegetation Index (NDVI) measurements from unmanned aerial vehicles (UAVs) and Sentinel-2 (S2) satellite data, a cost-effective and time-efficient alternative for agricultural monitoring is explored. This study comprises two stages: establishing a correlation between clear sky observations and NDVI measurements, and employing a combination of S2 NDVI and Synthetic Aperture Radar (SAR) NDVI to estimate crop damage. The integration of SAR and optical satellite data overcomes cloud cover challenges during typhoon events. The accuracy of standing crop estimation reached up to 99.2%, while crop damage estimation reached up to 99.7%. UAVs equipped with multispectral cameras prove effective for small-scale monitoring, while satellite imagery offers a valuable alternative for larger areas. The strong correlation between UAV and satellite-derived NDVI measurements highlights the significance of open-source satellite data in accurately estimating rice crop damage, providing a swift and reliable tool for assessing flood damage in agricultural monitoring.

Three-Dimensional Reconstruction of Railway Bridges Based on Unmanned Aerial Vehicle–Terrestrial Laser Scanner Point Cloud Fusion

To address the incomplete image data collection of close-to-ground structures, such as bridge piers and local features like the suspension cables in bridges, obtained from single unmanned aerial vehicle (UAV) oblique photography and the difficulty in acquiring point cloud data for the top structures of bridges using single terrestrial laser scanners (TLSs), as well as the lack of textural information in TLS point clouds, this study aims to establish a high-precision, complete, and realistic bridge model by integrating UAV image data and TLS point cloud data. Using a particular large-scale dual-track bridge as a case study, the methodology involves aerial surveys using a DJI Phantom 4 RTK for comprehensive image capture. We obtain 564 images circling the bridge arches, 508 images for orthorectification, and 491 images of close-range side views. Subsequently, all images, POS data, and ground control point information are imported into Context Capture 2023 software for aerial triangulation and multi-view image dense matching to generate dense point clouds of the bridge. Additionally, ground LiDAR scanning, involving the placement of six scanning stations both on and beneath the bridge, was conducted and the point cloud data from each station are registered in Trimble Business Center 5.5.2 software based on identical feature points. Noise point clouds are then removed using statistical filtering techniques. The integration of UAV image point clouds with TLS point clouds is achieved using the iterative closest point (ICP) algorithm, followed by the creation of a TIN model and texture mapping using Context Capture 2023 software. The effectiveness of the integrated modeling is verified by comparing the geometric accuracy and completeness of the images with those obtained from a single UAV image-based model. The integrated model is used to generate cross-sectional profiles of the dual-track bridge, with detailed annotations of boundary dimensions. Structural inspections reveal honeycomb surfaces and seepage in the bridge piers, as well as painted rust and cracks in the arch ribs. The geometric accuracy of the integrated model in the X, Y, and Z directions is 1.2 cm, 0.8 cm, and 0.9 cm, respectively, while the overall 3D model accuracy is 1.70 cm. This method provides technical reference for the reconstruction of three-dimensional point cloud bridge models. Through 3D reconstruction, railway operators can better monitor and assess the condition of bridge structures, promptly identifying potential defects and damages, thus enabling the adoption of necessary maintenance and repair measures to ensure the structural safety of the bridges.

Improved potato AGB estimates based on UAV RGB and hyperspectral images

Crops' above-ground biomass (AGB) is a crucial indicator that reflects crop health and predicts crop yield. However, using only optical vegetation indices (VIs) can produce inaccurate AGB estimates due to differences in crop varieties, growth stages, and measurement environments. Given the advantages of unmanned aerial vehicle (UAV) RGB and hyperspectral image fusion, this study evaluated the performance of multi-source remote sensing data for estimating potato AGB at multiple growth stages. In 2019, this study conducted potato trials with different varieties, fertilization levels, and planting densities at the Xiaotangshan Experiment Base (Beijing). UAV image and AGB data of potato three main stages were obtained from ground survey work. High-frequency information of the potato canopy was extracted from RGB images using discrete wavelet transform (DWT). VIs and wavelet energy coefficients were extracted from hyperspectral images using continuous wavelet transform (CWT). The linear relationships between potato AGB with VIs, high-frequency information, and wavelet coefficients were analyzed. Potato AGB estimation models were constructed based on single and multiple types of variables using multiple stepwise regression (MSR) and random forest (RF) models, respectively. This work showed the following results: (i) High-frequency information and wavelet coefficients were more sensitive to potato multi-growth stage AGB than VIs, and the latter were the most sensitive. (ii) Using VIs, high-frequency information, or wavelet coefficients separately to estimate the potato multi-growth stage AGB resulted in higher error and lower model accuracy. (iii) Combining VIs with either high-frequency information or wavelet coefficients improved the accuracy of AGB estimation, which was further improved by combining high-frequency information with wavelet coefficients. (iv) Combining VIs with both high-frequency information and wavelet coefficients provided the highest estimation accuracy using the MSR method. This combined AGB estimation model reduced the RMSE by 27%, 21%, and 16%, respectively, relative to VIs, high-frequency information, or wavelet coefficients alone. This result shows that the complementary advantages of multi-source UAV data can solve the challenge of insufficient AGB estimation by optical remote sensing. The work in this study provides remote sensing technology support to achieve potato crop growth monitoring and improve yield predictions.

Image Processing Techniques based Feature Extraction for Insect Damage Areas

Monitoring of forests is important for the diagnosis of insect damage to vegetation. Detection and monitoring of damaged areas facilitates the control of pests for practitioners. For this purpose, Unmanned Aerial Vehicles (UAVs) have been recently used to detect damaged areas. In order to distinguish damage areas from healthy areas on UAV images, it is necessary to extract the feature parameters of the images. Therefore, feature extraction is an important step in Computer Aided Diagnosis of insect damage monitored with UAV images. By reducing the size of the UAV image data, it is possible to distinguish between damaged and healthy areas from the extracted features. The accuracy of the classification algorithm depends on the segmentation method and the extracted features. The Grey-Level Co-occurrence Matrix (GLCM) characterizes areas texture based on the number of pixel pairs with specific intensity values arranged in specific spatial relationships. In this paper, texture characteristics of insect damage areas were extracted from UAV images using with GLCM. The 3000*4000 resolution UAV images containing damaged and healthy larch trees were analyzed using Definiens Developer (e-Cognition software) for multiresolution segmentation to detect the damaged areas. In this analysis, scale parameters were applied as 500, shape 0.1, color 0.9 and compactness 0.5. As a result of segmentation, GLCM homogeneity, GLCM contrast and GLCM entropy texture parameters were calculated for each segment. When calculating the texturing parameters, neighborhoods in different angular directions (0,45,90,135) are taken into account. As a result of the calculations made by considering all directions, it was found that GLCM homogeneity values ranged between 0.08 - 0.2, GLCM contrast values ranged between 82.86 - 303.58 and GLCM entropy values ranged between 7.81 - 8.51. On the other hand, GLCM homogeneity for healthy areas varies between 0.05 - 0.08, GLCM contrast between 441.70 - 888.80 and GLCM entropy between 8.93 - 9.40. The study demonstrated that GLCM technique can be a reliable method to detection of insect damage areas from UAV imagery.

Sediment Transport of Coastal Region Using Time-Series Unmanned Aerial Vehicle Spatial Data

Continuous monitoring of the varying topographical characteristics of shorelines is important for effective coastal management. Closed-circuit television (CCTV) cameras are installed to accumulate photographic data on coastal topographical changes. The overall change in the coastal waters can be intuitively understood from the images. However, the amount of three-dimensional (3D) changes that can be grasped is limited. To address this, studies have employed aerial photogrammetry, which is the use of unmanned aerial vehicles (UAVs) to capture aerial pictures, construct 3D models of target areas, and perform analysis through scale-invariant feature transform and structure from motion technologies. Although highly efficient, this technique requires several ground-control points (GCPs), which could corrupt the overall imagery. This study designs real-time kinematics—global navigation satellite system (RTK–GNSS) UAV, which requires few GCPs. To evaluate the positional accuracy of the captured UAV orthographic images and digital surface models (DSMs) used for precise coastal terrain measurements, a virtual reference service survey was performed to determine the vertical errors. The R-squared was 0.985, which is close to 1.0. Short-term and one-year topographic changes before and after a storm were investigated using time-series UAV image data after a coastal maintenance project. Analysis of the coefficient of variation in the beach volume for one year revealed that submerged breakwater reduced erosion during high wave resistance. The submerged breakwater located in the center exhibited variability similar to the opening. Hence, this method is more suitable for periodically monitoring coastal areas.

Accuracy of UAV-based DEMs without ground control points

Unmanned aerial vehicles (UAVs) are increasingly used in various environmental research projects and other activities that require accurate topography images. The quality of elevation models derived from UAV measurements varies depending on many variables (e.g. UAV equipment used, terrain conditions, etc.). In order to improve the quality of digital models based on UAV image data, additional GNSS-RTK measurements are usually made at ground control points. The aim of this article is to evaluate the mathematical accuracy of terrain models created without ground control points. The accuracy of the models is considered in two directions: vertical and horizontal. Vertical (elevation) accuracy is calculated based on airborne laser scanning (ALS) data and horizontal (location) accuracy is calculated through comparison with high-resolution orthophotomaps. The average elevation accuracy of all created UAV-based DEMs is found to be 2.7–2.8 m (MAE), 3.1–3.3 m (RMSE), and the average horizontal accuracy is 2.1 m. Despite the low accuracy of the UAV models, the topography is reflected very well in the spatial images. This may be related to the regular and symmetrical distribution of height errors. To improve the accuracy parameters of UAV-based DEMs, it is proposed that they be rapidly georeferenced based on orthophotomaps.

Quantification of Physiological Parameters of Rice Varieties Based on Multi-Spectral Remote Sensing and Machine Learning Models

Estimating plant physiological indicators with remote sensing technology is critical for ensuring precise field management. Compared with other remote sensing platforms, low-altitude unmanned aerial vehicles (UAVs) produce images with high spatial resolution that can be used to clearly identify vegetation. However, the information of UAV image data is relatively complex and difficult to analyze, which is the main problem limiting its large-scale use at present. In order to monitor plant physiological indexes from the multi-spectral data, a new method based on machine learning is studied in this paper. Using UAV for deriving the absorption coefficients of plant canopies and whole leaf area, this paper quantifies the effects of plant physiological indicators such as the soil and plant analyzer development (SPAD) value, whole leaf area, and dry matter accumulation on the relationship between the reflectance spectra. Nine vegetation indexes were then extracted as the sensitive vegetation indexes of the rice physiological indicators. Using the SVM model to predict the SPAD value of the plant, the mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and symmetric mean absolute percentage error (SMAPE) values of the model were 1.90, 1.38, 0.13, 0.86, and 4.13, respectively. The results demonstrate that the rice plants display a considerable biochemical and spectral correlation. Using SVM to predict the SPAD value has a better effect because of a better adaptation and a higher accuracy than other models. This study suggests that the multi-spectral data acquired using UAV can quickly estimate field physiological indicators, which has potential in the pre-visual detection of SPAD value information in the field. At the same time, it can also be extended to the detection and inversion of other key variables of crops.

Fusion level of satellite and UAV image data for soil salinity inversion in the coastal area of the Yellow River Delta

ABSTRACT Rapid and accurate determination of soil salt content (SSC) and its spatial distribution are of great significance for the prevention and improvement of soil salinization. Satellite and unmanned aerial vehicle (UAV) remote sensing data have complementary advantages. The fusion of satellite and UAV multisource remote sensing data to improve the accuracy of SSC based on inversion methods has become a hot topic, and the appropriate fusion level of multisource remote sensing data needs to be explored and determined. The objective of this study was to determine the appropriate fusion level of Sentinel-2A Multispectral Instrument (Sentinel-MSI) and UAV image data for SSC inversion by comparing the fusion effect of three levels (spectral data, spectral index, and spectral model). A numerical regression method was employed to analyse the relationship between Sentinel-MSI and UAV image data (MSI-UAV), and MSI-UAV data were fused at different levels. Then, the appropriate fusion level and best inversion model were optimized to realize regional SSC inversion. The results indicate that spectral data fusion was better than spectral index fusion for enhancing the SSC spectral response, with the correlation between spectral indices and SSC increasing by 0.139–0.167 after fusion. After spectral data fusion, the model improved the SSC inversion accuracy most obviously, with a calibration R2 of 0.623, validation R2 of 0.571, and ratio of performance to deviation (RPD) of 1.821. Therefore, spectral data fusion was found to be superior in enhancing the spectral response of soil salinity and in improving the accuracy of the estimation model. This research optimized spectral data fusion as the appropriate fusion level of MSI-UAV for SSC inversion and formed a set of high-precision MSI-UAV multisource remote sensing fusion inversion approaches for SSC.

Oil palm trees detection and counting on Microsoft Bing Maps Very High Resolution (VHR) satellite imagery and Unmanned Aerial Vehicles (UAV) data using image processing thresholding approach

Palm oil is one of the highest producing vegetable oil crops globally, with production increasing rapidly over the last 40 years from 5 million tonnes in 1980 to 74.5 million tonnes in 2019. This increase in production is in line with the high demand for vegetable oils worldwide. The accurate monitoring and statistics of oil palm plantation data are essential to support effective and efficient decision-making. However, the most commonly adopted data collection still uses conventional methods, i.e., field surveys, which are highly dependent on the massive amount of human resources, cost, processing time, and difficulty reaching remote areas. Remote sensing using satellite and UAV imagery can be an alternative in data collection due to its distinct advantages with a more efficient labor force, affordable cost, shorter time updates, and covering areas that are difficult to reach. In this work, we investigate the utilization of remote sensing data from Microsoft Bing Maps Very High Resolution (VHR) satellite imagery and Unmanned Aerial Vehicle (UAV) data using the image processing thresholding method for detecting and counting oil palm trees. The combination of Hue, Saturation, and Value (HSV) conversion, the Otsu segmentation thresholding, and contours detection and counting methods are used in our approach to enhance the accuracy of captured features of oil palm trees. The detection results are further categorized into best-case, average-case, and worst-case detection to comprehend the challenging real-world situation, based on the quality of captured imageries and model prediction results. Our proposed approach achieves better and more promising results when using UAV image data. This is indicated by an average true-positive rate (TPR) of 88 to 97% in best-case data input, 70 to 81% in average-case data input, 30 to 53% in worst-case data input, and 10% in estimation error. Meanwhile, Bing image data provides an average true-positive rate (TPR) of 9 to 13% in the best-case data input, 5 to 6% in the average-case data input, and 1 to 3% in the worst-case data input, with an average estimation error up to 78%. Overall, our proposed approach can perform the oil palm trees detection and counting. We also suggest that higher resolution imagery than Microsoft Bing maps or single utilization of UAV data can be considered input for better results. Our study could further be beneficial in providing more scalable and accurate plantation statistics.

Use of RGB images from unmanned aerial vehicle to estimate lettuce growth in root-knot nematode infested soil

Lettuce (Lactuca sativa) is an important horticultural commodity all over the world, and its growth can be affected by root-knot nematodes (Meloidogyne spp.). To keep track of plant behaviors, growers are using new technologies. In this paper, aerial images were obtained using a low-cost unmanned aerial vehicle (UAV) to gather crop information in a short time giving acceptable accuracy for decision-making in the field. Evaluations were done to check the flight height interference in the image's quality for lettuce mapping, and select the best one to estimate the effect of root-knot nematode incidence on lettuce growth. In a field infested with M. incognita, lettuce seedlings were planted in plots treated with bionematicide and control plots. Aerial images were obtained using low-cost UAV in four flight heights performed for five weeks, along with field measurements. Images were processed and used to calculate vegetation indices (VI) and vegetation cover (VC). After lettuce harvesting, nematode eggs were extracted from plants' roots and quantified. Plots treated with bionematicide showed no difference from the control plots in eggs number and lettuce growth. Differences in VI values between the flight heights were not consistent, suggesting that VI values could be affected by the lack of luminosity calibration in each flight condition. VC values calculated from field data presented strong positive correlations with VI and VC values from UAV image data, indicating that RGB images obtained by UAV can be used in the detection of diseases that affect plant growth, as well as following up harvesting time.

Design Knowledge for Deep-Learning-Enabled Image-Based Decision Support Systems

With the ever-increasing societal dependence on electricity, one of the critical tasks in power supply is maintaining the power line infrastructure. In the process of making informed, cost-effective, and timely decisions, maintenance engineers must rely on human-created, heterogeneous, structured, and also largely unstructured information. The maturing research on vision-based power line inspection driven by advancements in deep learning offers first possibilities to move towards more holistic, automated, and safe decision-making. However, (current) research focuses solely on the extraction of information rather than its implementation in decision-making processes. The paper addresses this shortcoming by designing, instantiating, and evaluating a holistic deep-learning-enabled image-based decision support system artifact for power line maintenance at a German distribution system operator in southern Germany. Following the design science research paradigm, two main components of the artifact are designed: A deep-learning-based model component responsible for automatic fault detection of power line parts as well as a user-oriented interface responsible for presenting the captured information in a way that enables more informed decisions. As a basis for both components, preliminary design requirements are derived from literature and the application field. Drawing on justificatory knowledge from deep learning as well as decision support systems, tentative design principles are derived. Based on these design principles, a prototype of the artifact is implemented that allows for rigorous evaluation of the design knowledge in multiple evaluation episodes, covering different angles. Through a technical experiment the technical novelty of the artifact’s capability to capture selected faults (regarding insulators and safety pins) in unmanned aerial vehicle (UAV)-captured image data (model component) is validated. Subsequent interviews, surveys, and workshops in a natural environment confirm the usefulness of the model as well as the user interface component. The evaluation provides evidence that (1) the image processing approach manages to address the gap of power line component inspection and (2) that the proposed holistic design knowledge for image-based decision support systems enables more informed decision-making. The paper therefore contributes to research and practice in three ways. First, the technical feasibility to detect certain maintenance-intensive parts of power lines with the help of unique UAV image data is shown. Second, the distribution system operators’ specific problem is solved by supporting decisions in maintenance with the proposed image-based decision support system. Third, precise design knowledge for image-based decision support systems is formulated that can inform future system designs of a similar nature.Supplementary InformationThe online version contains supplementary material available at 10.1007/s12599-022-00745-z.

Retrieval of Crop Variables from Proximal Multispectral UAV Image Data Using PROSAIL in Maize Canopy.

Mapping crop variables at different growth stages is crucial to inform farmers and plant breeders about the crop status. For mapping purposes, inversion of canopy radiative transfer models (RTMs) is a viable alternative to parametric and non-parametric regression models, which often lack transferability in time and space. Due to the physical nature of RTMs, inversion outputs can be delivered in sound physical units that reflect the underlying processes in the canopy. In this study, we explored the capabilities of the coupled leaf–canopy RTM PROSAIL applied to high-spatial-resolution (0.015 m) multispectral unmanned aerial vehicle (UAV) data to retrieve the leaf chlorophyll content (LCC), leaf area index (LAI) and canopy chlorophyll content (CCC) of sweet and silage maize throughout one growing season. Two different retrieval methods were tested: (i) applying the RTM inversion scheme to mean reflectance data derived from single breeding plots (mean reflectance approach) and (ii) applying the same inversion scheme to an orthomosaic to separately retrieve the target variables for each pixel of the breeding plots (pixel-based approach). For LCC retrieval, soil and shaded pixels were removed by applying simple vegetation index thresholding. Retrieval of LCC from UAV data yielded promising results compared to ground measurements (sweet maize RMSE = 4.92 μg/cm2, silage maize RMSE = 3.74 μg/cm2) when using the mean reflectance approach. LAI retrieval was more challenging due to the blending of sunlit and shaded pixels present in the UAV data, but worked well at the early developmental stages (sweet maize RMSE = 0.70m2/m2, silage RMSE = 0.61m2/m2 across all dates). CCC retrieval significantly benefited from the pixel-based approach compared to the mean reflectance approach (RMSEs decreased from 45.6 to 33.1 μg/m2). We argue that high-resolution UAV imagery is well suited for LCC retrieval, as shadows and background soil can be precisely removed, leaving only green plant pixels for the analysis. As for retrieving LAI, it proved to be challenging for two distinct varieties of maize that were characterized by contrasting canopy geometry.

Normalized Cross Correlation Template Matching for Oil Palm Tree Counting from UAV image

Tree crown detection and counting from remote sensing data such as images from Unmanned Aerial Vehicle (UAV) shows significant role in this modern era for vegetation monitoring. Since the data processing would depends on raw data available and for this case the RGB data, thus a suitable method such as template matching is presented. Normalized cross correlation is widely used as an effective measure in similarity between template image and the source or testing images. This paper focuses on six (6) steps involved in the overall process which are: (1) image acquisition, (2) template optimisation, (3) normalized cross correlation, (4) sliding window, (5) matched image and counting, and (6) accuracy assessment. Normalized cross correlation and sliding window techniques proposed for this work resulted in 80% to 89% F-measure values. This result indicates that UAV image data with appropriate image processing method/s have the potential to provide vital information for oil palm tree counting. This would be beneficial in plantation management to estimate yield and productivity. However, there are still rooms for improvement to achieve better results.

Using UAV image data to monitor the effects of different nitrogen application rates on tea quality.

Accurate and efficient evaluation of the effect of nitrogen application rate on tea quality is of great significance for nitrogen management in a tea garden. However, previous methods were all through soil or leaf sampling, using biochemical methods for laboratory testing. These methods are not only less one-time detection samples, but also time-consuming, laborious and inefficient. Therefore, the development of fast, efficient and non-destructive diagnostic methods is an important goal in this field. We obtained spectral information on the tea canopy using a multispectral camera carried by an unmanned aerial vehicle (UAV), and extracted the average DN value of the experimental plot by environmental visual imagery (ENVI); we finally obtained 28 spectral parameters. By analyzing the correlation between spectral parameters and ground parameters measured synchronously, five spectral parameters with high correlation were selected. Finally, the prediction models of tea nitrogen, polyphenol and amino acid content were established by using support vector machine (SVM), partial least squares and backpropagation neural network. Through modeling comparison and coefficient verification, the results show that the ground parameters measured in the laboratory were in good agreement with the results estimated by the model. The SVM model had the best performance in predicting nitrogen and tea polyphenol content, with R2 =0.7583 and 0.7533, root mean square error of prediction (RMSEP)=0.4086 and 0.3392, and normalized RMSEP (NRMSEP)=1.23 and 1.28, respectively. The partial least squares regression model had the best performance in predicting amino acid content, with R2 =0.7597, RMSEP=0.1176 and NRMSEP=4.10. The results show that the model based on UAV image data and machine learning algorithm can effectively detect the main biochemical components of the tea plant, which provides an important basis for tea garden management. © 2021 Society of Chemical Industry.

Assessment of plant density for barley and wheat using UAV multispectral imagery for high-throughput field phenotyping

Cereal plant density is a relevant agronomic trait in agriculture and high-throughput phenotyping of plant density is important for the decision-making process in precision farming and breeding. It influences the water as well as the fertilization requirements, the intraspecific competition, and the occurrence of weeds or pathogens. Recent studies have determined plant density using machine-learning approaches and feature extraction. This requires spatially very highly resolved images (0.02 cm) because the accuracy distinctly decreased when images had lower resolution. In this study, we present an approach that uses the linear relationship between plant density manually counted in the field and fractional cover derived from a RGB and a multispectral camera equipped on an unmanned aerial vehicle (UAV). We assumed that at an early seedling stage fractional cover is closely related to the number of plants. Spring barley and spring wheat experiments, each with three genotypes and four different sowing densities, were examined. The practicability and repeatability of the methodology were evaluated with an independent experiment consisting of 42 winter wheat genotypes. This experiment mainly differed for genotypes, sowing density and season.The empirical regression models that make us of multispectral images having a GSD of 0.69 cm were able to determine plant density with a high prediction accuracy for barley and wheat (R2 > 0.91, mean absolute error (MAE) < 28 plants). In addition, prediction accuracy only slightly declines for multispectral image data having 1.4 cm GSD or RGB image data having 0.6 cm GSD (MAE < 35 plants m−2). BBCH stage 13 was identified as the ideal growth stage in which the plants were large enough to accurately determine fractional cover even from the lower resolution image data. Moreover, a developed empirical regression model was transferred to an independent experimental field verifying its robustness across different conditions. The prediction accuracy of UAV estimated plant density showed an R2 value of 0.83 and an MAE of less than 21 plants m−2. Furthermore, manual measurements of 11 randomly selected plots proved sufficient for a user-based training of the regression model (R2 = 0.83, MAE < 23 plants m−2) adapted to the independent experimental field.The method and the use of UAV image data enable high-throughput phenotyping of cereal plant density with uncertainties of less than 10 %.The practicability, repeatability and robustness of the developed approach were demonstrated in this study.

Unmanned aerial vehicle-based field phenotyping of crop biomass using growth traits retrieved from PROSAIL model

Unmanned aerial vehicle (UAV) platform has been perceived as a useful tool for high-throughput field phenotyping of crop growth traits. While interpretation of UAV image data and retrieval of reliable and accurate phenotypic information are still challengeable due to the variations in sensors, crops and environment conditions. The aim of this study, therefore, is to explore the potential of UAV-based field phenotyping with the PROSAIL model to estimate biomass of rice and oilseed rape crops. Field experiments were designed for rice and oilseed rape with different nitrogen (N) treatments, and a UAV platform mounted with a multispectral camera was used to collect multi-temporal field images. Simultaneously, field measurements of leaf chlorophyll content (Cab), leaf area index (LAI), canopy chlorophyll content (CCC) and biomass were conducted. The results showed that coupling UAV-based multispectral images at the spectral region of 604–872 nm with the PROSAIL model successfully retrieved Cab, LAI and CCC of rice with the root mean square error (RMSE) of 5.40 μg/cm2, 1.13, and 43.50 μg/cm2, respectively. Further, the Cab, LAI and CCC retrieved from the PROSAIL model achieved the satisfactory biomass estimation in rice with the RMSE of 0.32 kg/m2, 0.23 kg/m2 and 0.22 kg/m2, respectively, which was comparable or superior to those obtained from commonly used empirical models. The proposed method also presented the robust performance for rice biomass estimation at different growth stages. In addition, model validation with the oilseed rape dataset showed an acceptable accuracy of biomass estimation with the determination coefficient (r2), RMSE and relative RMSE of 0.81, 0.03 kg/m2 and 27.82%, respectively, and still outperformed the empirical models with the better estimation performance. These findings demonstrate the potential of the proposed biomass retrieval strategy for UAV-based multispectral images, which also extend the application of PROSAIL model in field phenotyping of crop growth traits.

Habitat classification using convolutional neural networks and multitemporal multispectral aerial imagery

The monitoring of threatened habitats is a key objective of European environmental policies. Due to the high cost of current field-based habitat mapping techniques, there is keen interest in proposing solutions that can reduce cost through increased levels of automation. Our study aims to propose a habitat mapping solution that benefits both from the merits of convolutional neural networks (CNNs) for image classification tasks, as well as from the high spatial, spectral, and multitemporal unmanned aerial vehicle image data, which shows great potential for accurate vegetation classification. The proposed CNN-based method uses multitemporal multispectral aerial imagery for the classification of threatened coastal habitats in the Maharees (Ireland) and shows a high level of classification accuracy.

Assessment of DSM Based on Radiometric Transformation of UAV Data.

Unmanned Aerial Vehicle (UAV) is one of the latest technologies for high spatial resolution 3D modeling of the Earth. The objectives of this study are to assess low-cost UAV data using image radiometric transformation techniques and investigate its effects on global and local accuracy of the Digital Surface Model (DSM). This research uses UAV Light Detection and Ranging (LIDAR) data from 80 m and UAV Drone data from 300 and 500 m flying height. RAW UAV images acquired from 500 m flying height are radiometrically transformed in Matrix Laboratory (MATLAB). UAV images from 300 m flying height are processed for the generation of 3D point cloud and DSM in Pix4D Mapper. UAV LIDAR data are used for the acquisition of Ground Control Points (GCP) and accuracy assessment of UAV Image data products. Accuracy of enhanced DSM with DSM generated from 300 m flight height were analyzed for point cloud number, density and distribution. Root Mean Square Error (RMSE) value of Z is enhanced from ±2.15 m to ±0.11 m. For local accuracy assessment of DSM, four different types of land covers are statistically compared with UAV LIDAR resulting in compatibility of enhancement technique with UAV LIDAR accuracy.

Haplotype analysis from unmanned aerial vehicle imagery of rice MAGIC population for the trait dissection of biomass and plant architecture.

Unmanned aerial vehicles (UAVs) are popular tools for high-throughput phenotyping of crops in the field. However, their use for evaluation of individual lines is limited in crop breeding because research on what the UAV image data represent is still developing. Here, we investigated the connection between shoot biomass of rice plants and the vegetation fraction (VF) estimated from high-resolution orthomosaic images taken by a UAV 10 m above a field during the vegetative stage. Haplotype-based genome-wide association studies of multi-parental advanced generation inter-cross (MAGIC) lines revealed four quantitative trait loci (QTLs) for VF. VF was correlated with shoot biomass, but the haplotype effect on VF was better correlated with that on shoot biomass at these QTLs. Further genetic characterization revealed the relationships between these QTLs and plant spreading habit, final shoot biomass and panicle weight. Thus, genetic analysis using high-throughput phenotyping data derived from low-altitude, high-resolution UAV images during early stages of rice growing in the field provides insights into plant growth, architecture, final biomass, and yield.

Automatic Crack Segmentation for UAV-Assisted Bridge Inspection

Bridges are a critical piece of infrastructure in the network of road and rail transport system. Many of the bridges in Norway (in Europe) are at the end of their lifespan, therefore regular inspection and maintenance are critical to ensure the safety of their operations. However, the traditional inspection procedures and resources required are so time consuming and costly that there exists a significant maintenance backlog. The central thrust of this paper is to demonstrate the significant benefits of adapting a Unmanned Aerial Vehicle (UAV)-assisted inspection to reduce the time and costs of bridge inspection and established the research needs associated with the processing of the (big) data produced by such autonomous technologies. In this regard, a methodology is proposed for analysing the bridge damage that comprises three key stages, (i) data collection and model training, where one performs experiments and trials to perfect drone flights for inspection using case study bridges to inform and provide necessary (big) data for the second key stage, (ii) 3D construction, where one built 3D models that offer a permanent record of element geometry for each bridge asset, which could be used for navigation and control purposes, (iii) damage identification and analysis, where deep learning-based data analytics and modelling are applied for processing and analysing UAV image data and to perform bridge damage performance assessment. The proposed methodology is exemplified via UAV-assisted inspection of Skodsberg bridge, a 140 m prestressed concrete bridge, in the Viken county in eastern Norway.