Quantitative Remote Sensing at Ultra-High Resolution with UAV Spectroscopy: A Review of Sensor Technology, Measurement Procedures, and Data Correction Workflows

Spatio-temporal envolutional graph neural network for traffic flow prediction in UAV-based urban traffic monitoring system

With the advancement of modern UAV technology, UAVs have become integral to creating traffic management monitoring systems. Additionally, UAV-based traffic monitoring systems can predict traffic flow by integrating machine learning methods. Specifically, traffic flow data contains both spatial and temporal information, which can be effectively processed by graph neural networks (GNNs). However, GNNs often face the challenge of oversmoothing, which hinders their ability to capture complex structures in the data. The Spatio-Temporal Graph Ordinary Differential Equations (STGODE) model addresses this issue by introducing Neural Ordinary Differential Equations (NODEs) to construct deeper GNNs. Despite this, STGODE relies on initially predefined semantic neighborhood matrices, which do not adapt well to the dynamic nature of traffic information. To overcome this limitation, we propose an evolutionary graph neural network for traffic prediction, capable of continuously updating the semantic adjacency matrix throughout the training process. This dynamic evolution of the semantic adjacency matrix allows it to adapt to the features and semantic relations of the current data, enhancing its ability to capture the complexity and variability of traffic patterns. We validate our approach through experiments on several real-world datasets, demonstrating that our method outperforms state-of-the-art benchmarks.

Methods to optimize optical sensing of biotic plant stress - combined effects of hyperspectral imaging at night and spatial binning

In spatio-temporal plant monitoring, optical sensing (including hyperspectral imaging), is being deployed to, non-invasively, detect and diagnose plant responses to abiotic and biotic stressors. Early and accurate detection and diagnosis of stressors are key objectives. Level of radiometric repeatability of optical sensing data and ability to accurately detect and diagnose biotic stress are inversely correlated. Accordingly, it may be argued that one of the most significant frontiers and challenges regarding widespread adoption of optical sensing in plant research and crop production hinges on methods to maximize radiometric repeatability. In this study, we acquired hyperspectral optical sensing data at noon and midnight from soybean (Glycine max) and coleus wizard velvet red (Solenostemon scutellarioides) plants with/without experimentally infestation of two-spotted spider mites (Tetranychus urticae). We addressed three questions related to optimization of radiometric repeatability: (1) are reflectance-based plant responses affected by time of optical sensing? (2) if so, are plant responses to two-spotted spider mite infestations (biotic stressor) more pronounced at midnight versus at noon? (3) Is detection of biotic stress enhanced by spatial binning (smoothing) of hyperspectral imaging data? Results from this study provide insight into calculations of radiometric repeatability. Results strongly support claims that acquisition of optical sensing data to detect and characterize stress responses by plants to detect biotic stressors should be performed at night. Moreover, the combination of midnight imaging and spatial binning increased classification accuracies with 29% and 31% for soybean and coleus, respectively. Practical implications of these findings are discussed. Study results are relevant to virtually all applications of optical sensing to detect and diagnose abiotic and biotic stress responses by plants in both controlled environments and in outdoor crop production systems.

Pixel to practice: multi-scale image data for calibrating remote-sensing-based winter wheat monitoring methods

Site-specific crop management in heterogeneous fields has emerged as a promising avenue towards increasing agricultural productivity whilst safeguarding the environment. However, successful implementation is hampered by insufficient availability of accurate spatial information on crop growth, vigor, and health status at large scales. Challenges persist particularly in interpreting remote sensing signals within commercial crop production due to the variability in canopy appearance resulting from diverse factors. Recently, high-resolution imagery captured from unmanned aerial vehicles has shown significant potential for calibrating and validating methods for remote sensing signal interpretation. We present a comprehensive multi-scale image dataset encompassing 35,000 high-resolution aerial RGB images, ground-based imagery, and Sentinel-2 satellite data from nine on-farm wheat fields in Switzerland. We provide geo-referenced orthomosaics, digital elevation models, and shapefiles, enabling detailed analysis of field characteristics across the growing season. In combination with rich meta data such as detailed records of crop husbandry, crop phenology, and yield maps, this data set enables key challenges in remote sensing-based trait estimation and precision agriculture to be addressed.

Drone-Based Localization of Hazardous Chemicals by Passive Smart Dust

The distribution of tiny sensors over a specific area was first proposed in the late 1990s as a concept known as smart dust. Several efforts focused primarily on computing and networking capabilities, but quickly ran into problems related to power supply, cost, data transmission, and environmental pollution. To overcome these limitations, we propose using paper-based (confetti-like) chemosensors that exploit the inherent selectivity of chemical reagents, such as colorimetric indicators. In this work, cheap and biodegradable passive sensors made from cellulose could successfully indicate the presence of hazardous chemicals, e.g., strong acids, by a significant color change. A conventional color digital camera attached to a drone could easily detect this from a safe distance. The collected data were processed to define the hazardous area. Our work presents a combination of the smart dust concept, chemosensing, paper-based sensor technology, and low-cost drones for flexible, sensitive, economical, and rapid detection of hazardous chemicals in high-risk scenarios.

Establishing a knowledge structure for yield prediction in cereal crops using unmanned aerial vehicles

Recently, a rapid advancement in using unmanned aerial vehicles (UAVs) for yield prediction (YP) has led to many YP research findings. This study aims to visualize the intellectual background, research progress, knowledge structure, and main research frontiers of the entire YP domain for main cereal crops using VOSviewer and a comprehensive literature review. To develop visualization networks of UAVs related knowledge for YP of wheat, maize, rice, and soybean (WMRS) crops, the original research articles published between January 2001 and August 2023 were retrieved from the web of science core collection (WOSCC) database. Significant contributors have been observed to the growth of YP-related research, including the most active countries, prolific publications, productive writers and authors, the top contributing institutions, influential journals, papers, and keywords. Furthermore, the study observed the primary contributions of YP for WMRS crops using UAVs at the micro, meso, and macro levels and the degree of collaboration and information sources for YP. Moreover, the policy assistance from the People's Republic of China, the United States of America, Germany, and Australia considerably advances the knowledge of UAVs connected to YP of WMRS crops, revealed under investigation of grants and collaborating nations. Lastly, the findings of WMRS crops for YP are presented regarding the data type, algorithms, results, and study location. The remote sensing community can significantly benefit from this study by being able to discriminate between the most critical sub-domains of the YP literature for WMRS crops utilizing UAVs and to recommend new research frontiers for concentrating on the essential directions for subsequent studies.

Nitrogen addition changes the canopy biological characteristics of dominant tree species in an evergreen broad-leaved forest

Many studies have focused on the impact of nitrogen deposition on plants, but due to technical limitations, research on the responses of forest canopy to manipulated nitrogen deposition is relatively scarce. Based on a canopy nitrogen addition (CN) platform, this study used laboratory analysis and unmanned aerial vehicle (UAV) observations to assess the impact of CN on the canopy traits of dominant tree species (Engelhardia roxburghiana, Schima superba, and Castanea henryi) in an evergreen broad-leaved forest in China. The results showed that nitrogen application at 25 kg N ha-1 y-1 (CN25) and 50 kg N ha-1 y-1 (CN50) significantly increased the actual net photosynthetic rate (An) of all the three tree species. CN25 significantly increased superoxide dismutase (SOD), catalase (CAT), and ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco) activities in C. henryi. CN50 significantly increased the leaf area of all the three tree species and significantly reduced the leaf thickness of C. henryi, and significantly increased the POD and Rubisco activities in S. superba and C. henryi. CN significantly changed the number of forest gaps, but did not significantly change the area of forest gaps within the sample plots. CN25 significantly decreased the vertical projection area but increased the canopy flowering coverage of S. superba in dominant directions. CN25 and CN50 significantly increased the flowering coverage of C. henryi in favorable directions. It is found that under long-term (10-year) nitrogen addition, the balance between carbon fixation and antioxidant defense functions of E. roxburghiana may be broken down, but the carbon assimilation, antioxidant capacity and reproduction potential of S. superba and C. henryi may be well coordinated, which will have a potential impact on the species composition and ecological functions of the evergreen broad-leaved forest. This study may also provide scientific basis for forest management in the context of enhanced atmospheric nitrogen deposition.

Advancements of remote data acquisition and processing in unmanned vehicle technologies for water quality monitoring: An extensive review

Regular water quality monitoring is becoming desirable due to the increase in water pollution caused by both climate change and the generation of industrial chemicals. Unmanned vehicles have emerged as key technologies for remote data acquisition, providing fast and accurate methods for water quality monitoring. However, current research on unmanned vehicles has not systematically examined their features and limitations, which are crucial for identifying future research directions and applications of unmanned vehicle technologies. Therefore, this study extensively reviews the advancements in remote data acquisition and processing using unmanned vehicle technologies for water quality monitoring to provide valuable insights for future research. First, the types of unmanned vehicles and their application ranges for water quality monitoring are summarized. Among the unmanned vehicle technologies, unmanned aerial vehicles are considered primary platforms for water quality monitoring due to their wide data acquisition range and their ability to accommodate diverse sensors and samplers. Also, the types of samplers and sensors mounted on the unmanned vehicles are analyzed based on their characteristics. It is concluded that spectral sensors offer the most cost-effective approach for acquiring real-time water quality data. Furthermore, algorithms that convert image data into water quality data are examined, focusing on data preprocessing, analysis, and validation. The findings reveal a close relationship between the analysis of spectral characteristics of each water quality parameter and the wavelength ranges of red and red-edge. Lastly, future research directions for unmanned vehicle technologies are further suggested based on the summarized technological limitations.

Data Augmentation Techniques for Machine Learning Applied to Optical Spectroscopy Datasets in Agrifood Applications: A Comprehensive Review

Machine learning (ML) and deep learning (DL) have achieved great success in different tasks. These include computer vision, image segmentation, natural language processing, predicting classification, evaluating time series, and predicting values based on a series of variables. As artificial intelligence progresses, new techniques are being applied to areas like optical spectroscopy and its uses in specific fields, such as the agrifood industry. The performance of ML and DL techniques generally improves with the amount of data available. However, it is not always possible to obtain all the necessary data for creating a robust dataset. In the particular case of agrifood applications, dataset collection is generally constrained to specific periods. Weather conditions can also reduce the possibility to cover the entire range of classifications with the consequent generation of imbalanced datasets. To address this issue, data augmentation (DA) techniques are employed to expand the dataset by adding slightly modified copies of existing data. This leads to a dataset that includes values from laboratory tests, as well as a collection of synthetic data based on the real data. This review work will present the application of DA techniques to optical spectroscopy datasets obtained from real agrifood industry applications. The reviewed methods will describe the use of simple DA techniques, such as duplicating samples with slight changes, as well as the utilization of more complex algorithms based on deep learning generative adversarial networks (GANs), and semi-supervised generative adversarial networks (SGANs).

In vivo spectroscopy and machine learning for the early detection and classification of different stresses in apple trees

The use of in vivo spectroscopy to detect plant stress in its early stages has the potential to enhance food safety and reduce the need for plant protection products. However, differentiating between various stress types before symptoms appear remains poorly studied. In this study, we investigated the potential of Vis-NIR spectroscopy to differentiate between stress types in apple trees (Malus x domestica Borkh.) exposed to apple scab, waterlogging, and herbicides in a greenhouse. Using a spectroradiometer, we collected spectral signatures of leaves still attached to the tree and utilized machine learning techniques to develop predictive models for detecting stress presence and classifying stress type as early as 1-5 days after exposure. Our findings suggest that changes in spectral reflectance at multiple regions accurately differentiate various types of plant stress on apple trees. Our models were highly accurate (accuracies between 0.94 and 1) when detecting the general presence of stress at an early stage. The wavelengths important for classification relate to photosynthesis via pigment functioning (684 nm) and leaf water (~ 1800-1900 nm), which may be associated with altered gas exchange as a short-term stress response. Overall, our study demonstrates the potential of spectral technology and machine learning for early diagnosis of plant stress, which could lead to reduced environmental burden through optimizing resource utilization in agriculture.

UAV hyperspectral remote sensor images for mango plant disease and pest identification using MD-FCM and XCS-RBFNN

To diminish disease transmission together with promoting effective management techniques, it is crucial to monitor plant health and detect pathogens earlier. The initial part in reducing losses sourced from plant diseases is to make an accurate and earlier identification. Thus, the usage of unmanned aerial vehicle (UAV) hyperspectral imaging (HSI) sensors for surveying and assessing crops, orchards, and forests has rapidly elevated over the last decade, particularly for the stress management like water, diseases, nutrition deficits, and pests. Using Minkowski Distance-based Fuzzy C Means (MD-FCM) clustering and Xavier initialization-adapted Cosine Similarity-induced Radial Bias Function Neural Network (XCS-RBFNN) techniques, a UAV HS imaging remote sensor for Spatial and Temporal Resolution (STR) of mango plant disease and pest identification is proposed in this scheme. Collecting the input UAV source (image or video) is eventuated initially along with the Region of Interest (ROI) calculated which is followed by preprocessing. Leaf segmentation is eventuated using Logistic U-net after preprocessing. Next, MD-FCM performs clustering to cluster the diseased leaves and pests individually. The disease and pest characteristics are then retrieved separately and classified further. The requisite features are then chosen from the retrieved features utilizing the Levy Flight Distribution-produced Butterfly Optimization Algorithm (LFD-BOA). Finally, the XCS-RBFNN classifier is utilized to categorize the various diseases together with pests found in the UAV input source using the chosen features. The proposed framework's experimental findings are then compared to some prevailing schemes, with the results revealing that the proposed work outperforms other benchmark techniques.

The impact of variable illumination on vegetation indices and evaluation of illumination correction methods on chlorophyll content estimation using UAV imagery

BACKGROUND

The advancements in unmanned aerial vehicle (UAV) technology have recently emerged as an effective, cost-efficient, and versatile solution for monitoring crop growth with high spatial and temporal precision. This monitoring is usually achieved through the computation of vegetation indices (VIs) from agricultural lands. The VIs are based on the incoming radiance to the camera, which is affected when there is a change in the scene illumination. Such a change will cause a change in the VIs and subsequent measures, e.g., the VI-based chlorophyll-content estimation. In an ideal situation, the results from VIs should be free from the impact of scene illumination and should reflect the true state of the crop's condition. In this paper, we evaluate the performance of various VIs computed on images taken under sunny, overcast and partially cloudy days. To improve the invariance to the scene illumination, we furthermore evaluated the use of the empirical line method (ELM), which calibrates the drone images using reference panels, and the multi-scale Retinex algorithm, which performs an online calibration based on color constancy. For the assessment, we used the VIs to predict leaf chlorophyll content, which we then compared to field measurements.

RESULTS

The results show that the ELM worked well when the imaging conditions during the flight were stable but its performance degraded under variable illumination on a partially cloudy day. For leaf chlorophyll content estimation, The [Formula: see text] of the multivariant linear model built by VIs were 0.6 and 0.56 for sunny and overcast illumination conditions, respectively. The performance of the ELM-corrected model maintained stability and increased repeatability compared to non-corrected data. The Retinex algorithm effectively dealt with the variable illumination, outperforming the other methods in the estimation of chlorophyll content. The [Formula: see text] of the multivariable linear model based on illumination-corrected consistent VIs was 0.61 under the variable illumination condition.

CONCLUSIONS

Our work indicated the significance of illumination correction in improving the performance of VIs and VI-based estimation of chlorophyll content, particularly in the presence of fluctuating illumination conditions.

Signal-to-Noise Ratio Analysis for the Voltage-Mode Read-Out of Quartz Tuning Forks in QEPAS Applications

Quartz tuning forks (QTFs) are employed as sensitive elements for gas sensing applications implementing quartz-enhanced photoacoustic spectroscopy. Therefore, proper design of the QTF read-out electronics is required to optimize the signal-to-noise ratio (SNR), and in turn, the minimum detection limit of the gas concentration. In this work, we present a theoretical study of the SNR trend in a voltage-mode read-out of QTFs, mainly focusing on the effects of (i) the noise contributions of both the QTF-equivalent resistor and the input bias resistor R_L of the preamplifier, (ii) the operating frequency, and (iii) the bandwidth (BW) of the lock-in amplifier low-pass filter. A MATLAB model for the main noise contributions was retrieved and then validated by means of SPICE simulations. When the bandwidth of the lock-in filter is sufficiently narrow (BW = 0.5 Hz), the SNR values do not strongly depend on both the operating frequency and R_L values. On the other hand, when a wider low-pass filter bandwidth is employed (BW = 5 Hz), a sharp SNR peak close to the QTF parallel-resonant frequency is found for large values of R_L (R_L > 2 MΩ), whereas for small values of R_L (R_L < 2 MΩ), the SNR exhibits a peak around the QTF series-resonant frequency.

Automatic Crop Canopy Temperature Measurement Using a Low-Cost Image-Based Thermal Sensor: Application in a Pomegranate Orchard under a Permanent Shade Net House

Water scarcity in arid and semi-arid areas has led to the development of regulated deficit irrigation (RDI) strategies on most species of fruit trees in order to improve water productivity. For a successful implementation, these strategies require continuous feedback of the soil and crop water status. This feedback is provided by physical indicators from the soil-plant-atmosphere continuum, as is the case of the crop canopy temperature, which can be used for the indirect estimation of crop water stress. Infrared Radiometers (IRs) are considered as the reference tool for temperature-based water status monitoring in crops. Alternatively, in this paper, we assess the performance of a low-cost thermal sensor based on thermographic imaging technology for the same purpose. The thermal sensor was tested in field conditions by performing continuous measurements on pomegranate trees (Punica granatum L. 'Wonderful') and was compared with a commercial IR. A strong correlation (R² = 0.976) between the two sensors was obtained, demonstrating the suitability of the experimental thermal sensor to monitor the crop canopy temperature for irrigation management.

Calibration to maximize temporal radiometric repeatability of airborne hyperspectral imaging data

Many studies provide insight into calibration of airborne remote sensing data but very few specifically address the issue of temporal radiometric repeatability. In this study, we acquired airborne hyperspectral optical sensing data from experimental objects (white Teflon and colored panels) during 52 flight missions on three separate days. Data sets were subjected to four radiometric calibration methods: no radiometric calibration (radiance data), empirical line method calibration based on white calibration boards (ELM calibration), and two atmospheric radiative transfer model calibrations: 1) radiometric calibration with irradiance data acquired with a drone-mounted down-welling sensor (ARTM), and 2) modeled sun parameters and weather variables in combination with irradiance data from drone-mounted down-welling sensor (ARTM+). Spectral bands from 900-970 nm were found to be associated with disproportionally lower temporal radiometric repeatability than spectral bands from 416-900 nm. ELM calibration was found to be highly sensitive to time of flight missions (which is directly linked to sun parameters and weather conditions). Both ARTM calibrations outperformed ELM calibration, especially ARTM2+. Importantly, ARTM+ calibration markedly attenuated loss of radiometric repeatability in spectral bands beyond 900 nm and therefore improved possible contributions of these spectral bands to classification functions. We conclude that a minimum of 5% radiometric error (radiometric repeatability<95%), and probably considerably more error, should be expected when airborne remote sensing data are acquired at multiple time points across days. Consequently, objects being classified should be in classes that are at least 5% different in terms of average optical traits for classification functions to perform with high degree of accuracy and consistency. This study provides strong support for the claim that airborne remote sensing studies should include repeated data acquisitions from same objects at multiple time points. Such temporal replication is essential for classification functions to capture variation and stochastic noise caused by imaging equipment, and abiotic and environmental variables.

Marine environmental monitoring with unmanned vehicle platforms: Present applications and future prospects

Basic monitoring of the marine environment is crucial for the early warning and assessment of marine hydrometeorological conditions, climate change, and ecosystem disasters. In recent years, many marine environmental monitoring platforms have been established, such as offshore platforms, ships, or sensors placed on specially designed buoys or submerged marine structures. These platforms typically use a variety of sensors to provide high-quality observations, while they are limited by low spatial resolution and high cost during data acquisition. Satellite remote sensing allows monitoring over a larger ocean area; however, it is susceptible to cloud contamination and atmospheric effects that subject the results to large uncertainties. Unmanned vehicles have become more widely used as platforms in marine science and ocean engineering in recent years due to their ease of deployment, mobility, and the low cost involved in data acquisition. Researchers can acquire data according to their schedules and convenience, offering significant improvements over those obtained by traditional platforms. This study presents the state-of-the-art research on available unmanned vehicle observation platforms, including unmanned aerial vehicles (UAVs), underwater gliders (UGs), unmanned surface vehicles (USVs), and unmanned ships (USs), for marine environmental monitoring, and compares them with satellite remote sensing. The recent applications in marine environments have focused on marine biochemical and ecosystem features, marine physical features, marine pollution, and marine aerosols monitoring, and their integration with other products are also analysed. Additionally, the prospects of future ocean observation systems combining unmanned vehicle platforms (UVPs), global and regional autonomous platform networks, and remote sensing data are discussed.

Spatial response resampling (SR2): Accounting for the spatial point spread function in hyperspectral image resampling

With the increased availability of hyperspectral imaging (HSI) data at various scales (0.03-30 m), the role of simulation is becoming increasingly important in data analysis and applications. There are few commercially available tools to spatially degrade imagery based on the spatial response of a coarser resolution sensor. Instead, HSI data are typically spatially degraded using nearest neighbor, pixel aggregate or cubic convolution approaches. Without accounting for the spatial response of the simulated sensor, these approaches yield unrealistically sharp images. This article describes the spatial response resampling (SR²) workflow, a novel approach to degrade georeferenced raster HSI data based on the spatial response of a coarser resolution sensor. The workflow is open source and widely available for personal, academic or commercial use with no restrictions. The importance of the SR² workflow is shown with three practical applications (data cross-validation, flight planning and data fusion of separate VNIR and SWIR images).•The SR² workflow derives the point spread function of a specified HSI sensor based on nominal data acquisition parameters (e.g., integration time, altitude, speed), convolving it with a finer resolution HSI dataset for data simulation.•To make the workflow approachable for end users, we provide a MATLAB function that implements the SR² methodology.

Proximal and remote sensing in plant phenomics: 20 years of progress, challenges, and perspectives

Plant phenomics (PP) has been recognized as a bottleneck in studying the interactions of genomics and environment on plants, limiting the progress of smart breeding and precise cultivation. High-throughput plant phenotyping is challenging owing to the spatio-temporal dynamics of traits. Proximal and remote sensing (PRS) techniques are increasingly used for plant phenotyping because of their advantages in multi-dimensional data acquisition and analysis. Substantial progress of PRS applications in PP has been observed over the last two decades and is analyzed here from an interdisciplinary perspective based on 2972 publications. This progress covers most aspects of PRS application in PP, including patterns of global spatial distribution and temporal dynamics, specific PRS technologies, phenotypic research fields, working environments, species, and traits. Subsequently, we demonstrate how to link PRS to multi-omics studies, including how to achieve multi-dimensional PRS data acquisition and processing, how to systematically integrate all kinds of phenotypic information and derive phenotypic knowledge with biological significance, and how to link PP to multi-omics association analysis. Finally, we identify three future perspectives for PRS-based PP: (1) strengthening the spatial and temporal consistency of PRS data, (2) exploring novel phenotypic traits, and (3) facilitating multi-omics communication.

Multi-sensor spectral synergies for crop stress detection and monitoring in the optical domain: A review

Remote detection and monitoring of the vegetation responses to stress became relevant for sustainable agriculture. Ongoing developments in optical remote sensing technologies have provided tools to increase our understanding of stress-related physiological processes. Therefore, this study aimed to provide an overview of the main spectral technologies and retrieval approaches for detecting crop stress in agriculture. Firstly, we present integrated views on: i) biotic and abiotic stress factors, the phases of stress, and respective plant responses, and ii) the affected traits, appropriate spectral domains and corresponding methods for measuring traits remotely. Secondly, representative results of a systematic literature analysis are highlighted, identifying the current status and possible future trends in stress detection and monitoring. Distinct plant responses occurring under shortterm, medium-term or severe chronic stress exposure can be captured with remote sensing due to specific light interaction processes, such as absorption and scattering manifested in the reflected radiance, i.e. visible (VIS), near infrared (NIR), shortwave infrared, and emitted radiance, i.e. solar-induced fluorescence and thermal infrared (TIR). From the analysis of 96 research papers, the following trends can be observed: increasing usage of satellite and unmanned aerial vehicle data in parallel with a shift in methods from simpler parametric approaches towards more advanced physically-based and hybrid models. Most study designs were largely driven by sensor availability and practical economic reasons, leading to the common usage of VIS-NIR-TIR sensor combinations. The majority of reviewed studies compared stress proxies calculated from single-source sensor domains rather than using data in a synergistic way. We identified new ways forward as guidance for improved synergistic usage of spectral domains for stress detection: (1) combined acquisition of data from multiple sensors for analysing multiple stress responses simultaneously (holistic view); (2) simultaneous retrieval of plant traits combining multi-domain radiative transfer models and machine learning methods; (3) assimilation of estimated plant traits from distinct spectral domains into integrated crop growth models. As a future outlook, we recommend combining multiple remote sensing data streams into crop model assimilation schemes to build up Digital Twins of agroecosystems, which may provide the most efficient way to detect the diversity of environmental and biotic stresses and thus enable respective management decisions.

Inversion of chlorophyll content under the stress of leaf mite for jujube based on model PSO-ELM method

During the growth season, jujube trees are susceptible to infestation by the leaf mite, which reduces the fruit quality and productivity. Traditional monitoring techniques for mites are time-consuming, difficult, subjective, and result in a time lag. In this study, the method based on a particle swarm optimization (PSO) algorithm extreme learning machine for estimation of leaf chlorophyll content (SPAD) under leaf mite infestation in jujube was proposed. Initially, image data and SPAD values for jujube orchards under four severities of leaf mite infestation were collected for analysis. Six vegetation indices and SPAD value were chosen for correlation analysis to establish the estimation model for SPAD and the vegetation indices. To address the influence of colinearity between spectral bands, the feature band with the highest correlation coefficient was retrieved first using the successive projection algorithm. In the modeling process, the PSO correlation coefficient was initialized with the convergent optimal approximation of the fitness function value; the root mean square error (RMSE) of the predicted and measured values was derived as an indicator of PSO goodness-of-fit to solve the problems of ELM model weights, threshold randomness, and uncertainty of network parameters; and finally, an iterative update method was used to determine the particle fitness value to optimize the minimum error or iteration number. The results reflected that significant differences were observed in the spectral reflectance of the jujube canopy corresponding with the severity of leaf mite infestation, and the infestation severity was negatively correlated with the SPAD value of jujube leaves. The selected vegetation indices NDVI, RVI, PhRI, and MCARI were positively correlated with SPAD, whereas TCARI and GI were negatively correlated with SPAD. The accuracy of the optimized PSO-ELM model (R ² = 0.856, RMSE = 0.796) was superior to that of the ELM model alone (R ² = 0.748, RMSE = 1.689). The PSO-ELM model for remote sensing estimation of relative leaf chlorophyll content of jujube shows high fault tolerance and improved data-processing efficiency. The results provide a reference for the utility of UAV remote sensing for monitoring leaf mite infestation of jujube.

UAV-Based Hyperspectral Monitoring Using Push-Broom and Snapshot Sensors: A Multisite Assessment for Precision Viticulture Applications

Hyperspectral aerial imagery is becoming increasingly available due to both technology evolution and a somewhat affordable price tag. However, selecting a proper UAV + hyperspectral sensor combo to use in specific contexts is still challenging and lacks proper documental support. While selecting an UAV is more straightforward as it mostly relates with sensor compatibility, autonomy, reliability and cost, a hyperspectral sensor has much more to be considered. This note provides an assessment of two hyperspectral sensors (push-broom and snapshot) regarding practicality and suitability, within a precision viticulture context. The aim is to provide researchers, agronomists, winegrowers and UAV pilots with dependable data collection protocols and methods, enabling them to achieve faster processing techniques and helping to integrate multiple data sources. Furthermore, both the benefits and drawbacks of using each technology within a precision viticulture context are also highlighted. Hyperspectral sensors, UAVs, flight operations, and the processing methodology for each imaging type' datasets are presented through a qualitative and quantitative analysis. For this purpose, four vineyards in two countries were selected as case studies. This supports the extrapolation of both advantages and issues related with the two types of hyperspectral sensors used, in different contexts. Sensors' performance was compared through the evaluation of field operations complexity, processing time and qualitative accuracy of the results, namely the quality of the generated hyperspectral mosaics. The results shown an overall excellent geometrical quality, with no distortions or overlapping faults for both technologies, using the proposed mosaicking process and reconstruction. By resorting to the multi-site assessment, the qualitative and quantitative exchange of information throughout the UAV hyperspectral community is facilitated. In addition, all the major benefits and drawbacks of each hyperspectral sensor regarding its operation and data features are identified. Lastly, the operational complexity in the context of precision agriculture is also presented.

Close-range remote sensing-based detection and identification of macroplastics on water assisted by artificial intelligence: A review

Detection and identification of macroplastic debris in aquatic environments is crucial to understand and counter the growing emergence and current developments in distribution and deposition of macroplastics. In this context, close-range remote sensing approaches revealing spatial and spectral properties of macroplastics are very beneficial. To date, field surveys and visual census approaches are broadly acknowledged methods to acquire information, but since 2018 techniques based on remote sensing and artificial intelligence are advancing. Despite their proven efficiency, speed and wide applicability, there are still obstacles to overcome, especially when looking at the availability and accessibility of data. Thus, our review summarizes state-of-the-art research about the visual recognition and identification of different sorts of macroplastics. The focus is on both data acquisition techniques and evaluation methods, including Machine Learning and Deep Learning, but resulting products and published data will also be taken into account. Our aim is to provide a critical overview and outlook in a time where this research direction is thriving fast. This study shows that most Machine Learning and Deep Learning approaches are still in an infancy state regarding accuracy and detail when compared to visual monitoring, even though their results look very promising.

Estimating Community-Level Plant Functional Traits in a Species-Rich Alpine Meadow Using UAV Image Spectroscopy

Plant functional traits at the community level (plant community traits hereafter) are commonly used in trait-based ecology for the study of vegetation–environment relationships. Previous studies have shown that a variety of plant functional traits at the species or community level can be successfully retrieved by airborne or spaceborne imaging spectrometer in homogeneous, species-poor ecosystems. However, findings from these studies may not apply to heterogeneous, species-rich ecosystems. Here, we aim to determine whether unmanned aerial vehicle (UAV)-based hyperspectral imaging could adequately estimate plant community traits in a species-rich alpine meadow ecosystem on the Qinghai–Tibet Plateau. To achieve this, we compared the performance of four non-parametric regression models, i.e., partial least square regression (PLSR), the generic algorithm integrated with the PLSR (GA-PLSR), random forest (RF) and extreme gradient boosting (XGBoost) for the retrieval of 10 plant community traits using visible and near-infrared (450–950 nm) UAV hyperspectral imaging. Our results show that chlorophyll a, chlorophyll b, carotenoid content, starch content, specific leaf area and leaf thickness were estimated with good accuracies, with the highest R2 values between 0.64 (nRMSE = 0.16) and 0.83 (nRMSE = 0.11). Meanwhile, the estimation accuracies for nitrogen content, phosphorus content, plant height and leaf dry matter content were relatively low, with the highest R2 varying from 0.3 (nRMSE = 0.24) to 0.54 (nRMSE = 0.20). Among the four tested algorithms, the GA-PLSR produced the highest accuracy, followed by PLSR and XGBoost, and RF showed the poorest performance. Overall, our study demonstrates that UAV-based visible and near-infrared hyperspectral imaging has the potential to accurately estimate multiple plant community traits for the natural grassland ecosystem at a fine scale.

UAV-Based Forest Health Monitoring: A Systematic Review

In recent years, technological advances have led to the increasing use of unmanned aerial vehicles (UAVs) for forestry applications. One emerging field for drone application is forest health monitoring (FHM). Common approaches for FHM involve small-scale resource-extensive fieldwork combined with traditional remote sensing platforms. However, the highly dynamic nature of forests requires timely and repetitive data acquisition, often at very high spatial resolution, where conventional remote sensing techniques reach the limits of feasibility. UAVs have shown that they can meet the demands of flexible operation and high spatial resolution. This is also reflected in a rapidly growing number of publications using drones to study forest health. Only a few reviews exist which do not cover the whole research history of UAV-based FHM. Since a comprehensive review is becoming critical to identify research gaps, trends, and drawbacks, we offer a systematic analysis of 99 papers covering the last ten years of research related to UAV-based monitoring of forests threatened by biotic and abiotic stressors. Advances in drone technology are being rapidly adopted and put into practice, further improving the economical use of UAVs. Despite the many advantages of UAVs, such as their flexibility, relatively low costs, and the possibility to fly below cloud cover, we also identified some shortcomings: (1) multitemporal and long-term monitoring of forests is clearly underrepresented; (2) the rare use of hyperspectral and LiDAR sensors must drastically increase; (3) complementary data from other RS sources are not sufficiently being exploited; (4) a lack of standardized workflows poses a problem to ensure data uniformity; (5) complex machine learning algorithms and workflows obscure interpretability and hinders widespread adoption; (6) the data pipeline from acquisition to final analysis often relies on commercial software at the expense of open-source tools.

Herbage Mass, N Concentration, and N Uptake of Temperate Grasslands Can Adequately Be Estimated from UAV-Based Image Data Using Machine Learning

Precise and timely information on biomass yield and nitrogen uptake in intensively managed grasslands are essential for sustainable management decisions. Imaging sensors mounted on unmanned aerial vehicles (UAVs) along with photogrammetric structure-from-motion processing can provide timely data on crop traits rapidly and non-destructively with a high spatial resolution. The aim of this multi-temporal field study is to estimate aboveground dry matter yield (DMY), nitrogen concentration (N%) and uptake (Nup) of temperate grasslands from UAV-based image data using machine learning (ML) algorithms. The study is based on a two-year dataset from an experimental grassland trial. The experimental setup regarding climate conditions, N fertilizer treatments and slope yielded substantial variations in the dataset, covering a considerable amount of naturally occurring differences in the biomass and N status of grasslands in temperate regions with similar management strategies. Linear regression models and three ML algorithms, namely, random forest (RF), support vector machine (SVM), and partial least squares (PLS) regression were compared with and without a combination of both structural (sward height; SH) and spectral (vegetation indices and single bands) features. Prediction accuracy was quantified using a 10-fold 5-repeat cross-validation (CV) procedure. The results show a significant improvement of prediction accuracy when all structural and spectral features are combined, regardless of the algorithm. The PLS models were outperformed by their respective RF and SVM counterparts. At best, DMY was predicted with a median RMSECV of 197 kg ha−1, N% with a median RMSECV of 0.32%, and Nup with a median RMSECV of 7 kg ha−1. Furthermore, computationally less expensive models incorporating, e.g., only the single multispectral camera bands and SH metrics, or selected features based on variable importance achieved comparable results to the overall best models.

Optimization and Evaluation of Sensor Angles for Precise Assessment of Architectural Traits in Peach Trees

Fruit industries play a significant role in many aspects of global food security. They provide recognized vitamins, antioxidants, and other nutritional supplements packed in fresh fruits and other processed commodities such as juices, jams, pies, and other products. However, many fruit crops including peaches (Prunus persica (L.) Batsch) are perennial trees requiring dedicated orchard management. The architectural and morphological traits of peach trees, notably tree height, canopy area, and canopy crown volume, help to determine yield potential and precise orchard management. Thus, the use of unmanned aerial vehicles (UAVs) coupled with RGB sensors can play an important role in the high-throughput acquisition of data for evaluating architectural traits. One of the main factors that define data quality are sensor imaging angles, which are important for extracting architectural characteristics from the trees. In this study, the goal was to optimize the sensor imaging angles to extract the precise architectural trait information by evaluating the integration of nadir and oblique images. A UAV integrated with an RGB imaging sensor at three different angles (90°, 65°, and 45°) and a 3D light detection and ranging (LiDAR) system was used to acquire images of peach trees located at the Washington State University’s Tukey Horticultural Orchard, Pullman, WA, USA. A total of four approaches, comprising the use of 2D data (from UAV) and 3D point cloud (from UAV and LiDAR), were utilized to segment and measure the individual tree height and canopy crown volume. Overall, the features extracted from the images acquired at 45° and integrated nadir and oblique images showed a strong correlation with the ground reference tree height data, while the latter was highly correlated with canopy crown volume. Thus, selection of the sensor angle during UAV flight is critical for improving the accuracy of extracting architectural traits and may be useful for further precision orchard management.

Hyperspectral Identification of Ginseng Growth Years and Spectral Importance Analysis Based on Random Forest

The growth year of ginseng is very important as it affects its economic value and even defines if ginseng can be used as medicine or food. In the case of large-scale developments in the ginseng industry, a set of non-destructive, fast, and nonprofessional operations related to the growth year identification method is needed. The characteristics of ginseng reflectance spectral data were analyzed, and the growth year recognition model was constructed by a decision-tree-based random forest machine learning method. After independent verification, the accuracy of distinguishing ginseng food and medicine can reach 92.9%, with 6-year growth as the boundary, and 100%, with 5-year growth as the boundary. The research results show that the spectral change of ginseng is the most obvious in the fifth year, which provides a reference for the key research years based on chemical analyses and other methods. For the application of growth year recognition, the NIR band (1000–2500 nm) had little contribution to the recognition of ginseng growth years, and the band with the largest contribution was 400–650 nm. The recognition model based on machine learning provides a non-destructive, fast, and simple scheme with high accuracy for ginseng year recognition, and the spectral importance analysis conclusion of ginseng growth years provides a design reference for the development of special lightweight spectral equipment for year recognition.

Estimating Grass Sward Quality and Quantity Parameters Using Drone Remote Sensing with Deep Neural Networks

The objective of this study is to investigate the potential of novel neural network architectures for measuring the quality and quantity parameters of silage grass swards, using drone RGB and hyperspectral images (HSI), and compare the results with the random forest (RF) method and handcrafted features. The parameters included fresh and dry biomass (FY, DMY), the digestibility of organic matter in dry matter (D-value), neutral detergent fiber (NDF), indigestible neutral detergent fiber (iNDF), water-soluble carbohydrates (WSC), nitrogen concentration (Ncont) and nitrogen uptake (NU); datasets from spring and summer growth were used. Deep pre-trained neural network architectures, the VGG16 and the Vision Transformer (ViT), and simple 2D and 3D convolutional neural networks (CNN) were studied. In most cases, the neural networks outperformed RF. The normalized root-mean-square errors (NRMSE) of the best models were for FY 19% (2104 kg/ha), DMY 21% (512 kg DM/ha), D-value 1.2% (8.6 g/kg DM), iNDF 12% (5.1 g/kg DM), NDF 1.1.% (6.2 g/kg DM), WSC 10% (10.5 g/kg DM), Ncont 9% (2 g N/kg DM), and NU 22% (11.9 N kg/ha) using independent test dataset. The RGB data provided good results, particularly for the FY, DMY, WSC and NU. The HSI datasets provided advantages for some parameters. The ViT and VGG provided the best results with the RGB data, whereas the simple 3D-CNN was the most consistent with the HSI data.

Experimental data manipulations to assess performance of hyperspectral classification models of crop seeds and other objects

BACKGROUND

Optical sensing solutions are being developed and adopted to classify a wide range of biological objects, including crop seeds. Performance assessment of optical classification models remains both a priority and a challenge.

METHODS

As training data, we acquired hyperspectral imaging data from 3646 individual tomato seeds (germination yes/no) from two tomato varieties. We performed three experimental data manipulations: (1) Object assignment error: effect of individual object in the training data being assigned to the wrong class. (2) Spectral repeatability: effect of introducing known ranges (0-10%) of stochastic noise to individual reflectance values. (3) Size of training data set: effect of reducing numbers of observations in training data. Effects of each of these experimental data manipulations were characterized and quantified based on classifications with two functions [linear discriminant analysis (LDA) and support vector machine (SVM)].

RESULTS

For both classification functions, accuracy decreased linearly in response to introduction of object assignment error and to experimental reduction of spectral repeatability. We also demonstrated that experimental reduction of training data by 20% had negligible effect on classification accuracy. LDA and SVM classification algorithms were applied to independent validation seed samples. LDA-based classifications predicted seed germination with RMSE = 10.56 (variety 1) and 26.15 (variety 2), and SVM-based classifications predicted seed germination with RMSE = 10.44 (variety 1) and 12.58 (variety 2).

CONCLUSION

We believe this study represents the first, in which optical seed classification included both a thorough performance evaluation of two separate classification functions based on experimental data manipulations, and application of classification models to validation seed samples not included in training data. Proposed experimental data manipulations are discussed in broader contexts and general relevance, and they are suggested as methods for in-depth performance assessments of optical classification models.

Assessing Grapevine Biophysical Parameters From Unmanned Aerial Vehicles Hyperspectral Imagery

Over the last 50 years, many approaches for extracting plant key parameters from remotely sensed data have been developed, especially in the last decade with the spread of unmanned aerial vehicles (UAVs) in agriculture. Multispectral sensors are very useful for the elaboration of common vegetation indices (VIs), however, the spectral accuracy and range may not be enough. In this scenario, hyperspectral (HS) technologies are gaining particular attention thanks to the highest spectral resolution, which allows deep characterization of vegetative/soil response. Literature presents few papers encompassing UAV-based HS applications in vineyard, a challenging conditions respect to other crops due to high presence of bare soil, grass cover, shadows and high heterogeneity canopy structure with different leaf inclination. The purpose of this paper is to present the first contribution combining traditional and multivariate HS data elaboration techniques, supported by strong ground truthing of vine ecophysiological, vegetative and productive variables. Firstly the research describes the UAV image acquisition and processing workflow to generate a 50 bands HS orthomosaic of a study vineyard. Subsequently, the spectral data extracted from 60 sample vines were elaborated both investigating the relationship between traditional narrowband VIs and grapevine traits. Then, multivariate calibration models were built using a double approach based on Partial Least Square (PLS) regression and interval-PLS (iPLS), to evaluate the correlation performance between the biophysical parameters and HS imagery using the whole spectral range and a selection of more relevant bands applying a variable selection algorithm, respectively. All techniques (VIs, PLS and iPLS) provided satisfactory correlation performances for the ecophysiological (R 2 = 0.65), productive (R 2 = 0.48), and qualitative (R 2 = 0.63) grape parameters. The novelty of this work is represented by the first assessment of a UAV HS dataset with the expression of the entire vine ecosystem, from the physiological and vegetative state to grapes production and quality, using narrowband VIs and multivariate PLS regressions. A correct non-destructive estimation of key parameters in vineyard, above all physiological parameters which must be measured in a short time as they are extremely influenced by the variability of environmental conditions during the day, represents a powerful tool to support the winegrower in vineyard management.

OpenHSI: A Complete Open-Source Hyperspectral Imaging Solution for Everyone

OpenHSI is an initiative to lower the barriers of entry and bring compact pushbroom hyperspectral imaging spectrometers to a wider audience. We present an open-source optical design that can be replicated with readily available commercial-off-the-shelf components, and an open-source software platform openhsi that simplifies the process of capturing calibrated hyperspectral datacubes. Some of the features that the software stack provides include: an ISO 19115-2 metadata editor, wavelength calibration, a fast smile correction method, radiance conversion, atmospheric correction using 6SV (an open-source radiative transfer code), and empirical line calibration. A pipeline was developed to customise the desired processing and make openhsi practical for real-time use. We used the OpenHSI optical design and software stack successfully in the field and verified the performance using calibration tarpaulins. By providing all the tools needed to collect documented hyperspectral datasets, our work empowers practitioners who may not have the financial or technical capability to operate commercial hyperspectral imagers, and opens the door for applications in new problem domains.

Hyperspectral Monitoring Driven by Machine Learning Methods for Grassland Above-Ground Biomass

Above-ground biomass (AGB) is a key indicator for studying grassland productivity and evaluating carbon sequestration capacity; it is also a key area of interest in hyperspectral ecological remote sensing. In this study, we use data from a typical alpine meadow in the Qinghai–Tibet Plateau during the main growing season (July–September), compare the results of various feature selection algorithms to extract an optimal subset of spectral variables, and use machine learning methods and data mining techniques to build an AGB prediction model and realize the optimal inversion of above-ground grassland biomass. The results show that the Lasso and RFE_SVM band filtering machine learning models can effectively select the global optimal feature and improve the prediction effect of the model. The analysis also compares the support vector machine (SVM), least squares regression boosting (LSB), and Gaussian process regression (GPR) AGB inversion models; our findings show that the results of the three models are similar, with the GPR machine learning model achieving the best outcomes. In addition, through the analysis of different data combinations, it is found that the accuracy of AGB inversion can be significantly improved by combining the spectral characteristics with the growing season. Finally, by constructing a machine learning interpretable model to analyze the specific role of features, it was found that the same band plays different roles in different records, and the related results can provide a scientific basis for the research of grassland resource monitoring and estimation.

A Robust and Accurate Landing Methodology for Drones on Moving Targets

This paper presents a framework for performing autonomous precise landing of unmanned aerial vehicles (UAVs) on dynamic targets. The main goal of this work is to design the methodology and the controlling algorithms that will allow multi-rotor drones to perform a robust and efficient landing in dynamic conditions of changing wind, dynamic obstacles, and moving targets. Unlike existing GNSS-based vertical landing solutions, the suggested framework does not rely on global positioning and uses adaptive diagonal approaching angle visual landing. The framework was designed to work on existing camera-drone platforms, without any need for additional sensors, and it was implemented using DJI’s API on Android devices. The presented concept of visual sliding landing (VSL) was tested on a wide range of commercial drones, performing hundreds of precise and robust autonomous landings on dynamic targets, including boats, cars, RC-boats, and RC-rovers.

Plant Viral Disease Detection: From Molecular Diagnosis to Optical Sensing Technology—A Multidisciplinary Review

Plant viral diseases result in productivity and economic losses to agriculture, necessitating accurate detection for effective control. Lab-based molecular testing is the gold standard for providing reliable and accurate diagnostics; however, these tests are expensive, time-consuming, and labour-intensive, especially at the field-scale with a large number of samples. Recent advances in optical remote sensing offer tremendous potential for non-destructive diagnostics of plant viral diseases at large spatial scales. This review provides an overview of traditional diagnostic methods followed by a comprehensive description of optical sensing technology, including camera systems, platforms, and spectral data analysis to detect plant viral diseases. The paper is organized along six multidisciplinary sections: (1) Impact of plant viral disease on plant physiology and consequent phenotypic changes, (2) direct diagnostic methods, (3) traditional indirect detection methods, (4) optical sensing technologies, (5) data processing techniques and modelling for disease detection, and (6) comparison of the costs. Finally, the current challenges and novel ideas of optical sensing for detecting plant viruses are discussed.

Machine Learning Strategies for the Retrieval of Leaf-Chlorophyll Dynamics: Model Choice, Sequential Versus Retraining Learning, and Hyperspectral Predictors

Monitoring leaf Chlorophyll (Chl) in-situ is labor-intensive, limiting representative sampling for detailed mapping of Chl variability at field scales across time. Unmanned aeria-l vehicles (UAV) and hyperspectral cameras provide flexible platforms for observing agricultural systems, overcoming this spatio-temporal sampling constraint. Here, we evaluate a customized machine learning (ML) workflow to retrieve multi-temporal leaf-Chl levels, combining sub-centimeter resolution UAV-hyperspectral imagery (400-1,000 nm) with leaf-level reflectance spectra and SPAD measurements, capturing temporal correlations, selecting relevant predictors, and retrieving accurate results under different conditions. The study is performed within a phenotyping experiment to monitor wild tomato plants' development. Several analyses were conducted to evaluate multiple ML strategies, including: (1) exploring sequential versus retraining learning; (2) comparing insights gained from using 272 spectral bands versus 60 pigment-based vegetation indices (VIs); and (3) assessing six regression methods (linear, partial-least-square regression; PLSR, decision trees, support vector, ensemble trees, and Gaussian process; GPR). Goodness-of-fit (R 2) and accuracy metrics (MAE, RMSE) were determined using training/testing and validation data subsets to assess the models' performance. Overall, while equally good performance was obtained using either PLSR, GPR, or random forest, results show: (1) the retraining strategy improved the ability of most of the approaches to model SPAD-based Chl dynamics; (2) comparative analysis between retrievals and validation data distributions informed the models' ability to capture Chl dynamics through SPAD levels; (3) VI predictors slightly improved R 2 (e.g., from 0.59 to 0.74 units for GPR) and accuracy (e.g., MAE and RMSE differences of up to 2 SPAD units) in specific algorithms; (4) feature importance examined through these methods, revealed strong overlaps between relevant bands and VI predictors, highlighting a few decisive spectral ranges and indices useful for retrieving leaf-Chl levels. The proposed ML framework allows the retrieval of high-quality spatially distributed and multi-temporal SPAD-based chlorophyll maps at an ultra-high pixel resolution (e.g., 7 mm).

Laboratory Hyperspectral Image Acquisition System Setup and Validation

Hyperspectral Imaging (HSI) techniques have demonstrated potential to provide useful information in a broad set of applications in different domains, from precision agriculture to environmental science. A first step in the preparation of the algorithms to be employed outdoors starts at a laboratory level, capturing a high amount of samples to be analysed and processed in order to extract the necessary information about the spectral characteristics of the studied samples in the most precise way. In this article, a custom-made scanning system for hyperspectral image acquisition is described. Commercially available components have been carefully selected in order to be integrated into a flexible infrastructure able to obtain data from any Generic Interface for Cameras (GenICam) compliant devices using the gigabyte Ethernet interface. The entire setup has been tested using the Specim FX hyperspectral series (FX10 and FX17) and a Graphical User Interface (GUI) has been developed in order to control the individual components and visualise data. Morphological analysis, spectral response and optical aberration of these pushbroom-type hyperspectral cameras have been evaluated prior to the validation of the whole system with different plastic samples for which spectral signatures are extracted and compared with well-known spectral libraries.

Automated hyperspectral vegetation index derivation using a hyperparameter optimisation framework for high-throughput plant phenotyping

Hyperspectral vegetation indices (VIs) are widely deployed in agriculture remote sensing and plant phenotyping to estimate plant biophysical and biochemical traits. However, existing VIs consist mainly of simple two-band indices that limit the net performance and often do not generalise well for traits other than those for which they were originally designed. We present an automated hyperspectral vegetation index (AutoVI) system for the rapid generation of novel two- to six-band trait-specific indices in a streamlined process covering model selection, optimisation and evaluation, driven by the tree parzen estimator algorithm. Its performance was tested in generating novel indices to estimate chlorophyll and sugar contents in wheat. Results showed that AutoVI can rapidly generate complex novel VIs (at least a four-band index) that correlated strongly (R²  > 0.8) with measured chlorophyll and sugar contents in wheat. Automated hyperspectral vegetation index-derived indices were used as features in simple and stepwise multiple linear regressions for chlorophyll and sugar content estimation, and outperformed the results achieved with the existing 47 VIs and those provided using partial least squares regression. The AutoVI system can deliver novel trait-specific VIs readily adoptable to high-throughput plant phenotyping platforms and should appeal to plant scientists and breeders. A graphical user interface for the AutoVI is provided here.

Predicting Canopy Chlorophyll Content in Sugarcane Crops Using Machine Learning Algorithms and Spectral Vegetation Indices Derived from UAV Multispectral Imagery

The use of satellite-based Remote Sensing (RS) is a well-developed field of research. RS techniques have been successfully utilized to evaluate the chlorophyll content for the monitoring of sugarcane crops. This research provides a new framework for inferring the chlorophyll content in sugarcane crops at the canopy level using unmanned aerial vehicles (UAVs) and spectral vegetation indices processed with multiple machine learning algorithms. Studies were conducted in a sugarcane field located in Sugarcane Research Institute (SRI, Uda Walawe, Sri Lanka), with various fertilizer applications over the entire growing season from 2020 to 2021. An UAV with multispectral camera was used to collect the aerial images to generate the vegetation indices. Ground measurements of leaf chlorophyll were used as indications for fertilizer status in the sugarcane field. Different machine learning (ML) algorithms were used ground-truthing data of chlorophyll content and spectral vegetation indices to forecast sugarcane chlorophyll content. Several machine learning algorithms such as MLR, RF, DT, SVR, XGB, KNN and ANN were applied in two ways: before feature selection (BFS) by training the algorithms with all twenty-four (24) vegetation indices with five (05) spectral bands and after feature selection (AFS) by training algorithms with fifteen (15) vegetation indices. All the algorithms with both BFS and AFS methods were compared with an estimated coefficient of determination (R2) and root mean square error (RMSE). Spectral indices such as RVI and DVI were shown to be the most reliable indices for estimating chlorophyll content in sugarcane fields, with coefficients of determination (R2) of 0.94 and 0.93, respectively. XGB model shows the highest validation score (R2) and lowest RMSE in both methods of BFS (0.96 and 0.14) and AFS (0.98 and 0.78), respectively. However, KNN and SVR algorithms show the lowest validation accuracy than other models. According to the results, the AFS validation score is higher than BFS in MLR, SVR, XGB and KNN. Even though, validation score of the ANN model is decreased in AFS. The findings demonstrated that the use of multispectral UAV could be utilized to estimate chlorophyll content and measure crop health status over a larger sugarcane field. This methodology will aid in real-time crop nutrition management in sugarcane plantations by reducing the need for conventional measurement of sugarcane chlorophyll content.

Image Correction and In Situ Spectral Calibration for Low-Cost, Smartphone Hyperspectral Imaging

Developments in the portability of low-cost hyperspectral imaging instruments translate to significant benefits to agricultural industries and environmental monitoring applications. These advances can be further explicated by removing the need for complex post-processing and calibration. We propose a method for substantially increasing the utility of portable hyperspectral imaging. Vertical and horizontal spatial distortions introduced into images by ‘operator shake’ are corrected by an in-scene reference card with two spatial references. In situ light-source-independent spectral calibration is performed. This is achieved by a comparison of the ground-truth spectral reflectance of an in-scene red–green–blue target to the uncalibrated output of the hyperspectral data. Finally, bias introduced into the hyperspectral images due to the non-flat spectral output of the illumination is removed. This allows for low-skilled operation of a truly handheld, low-cost hyperspectral imager for agriculture, environmental monitoring, or other visible hyperspectral imaging applications.

Toward Integrated Large-Scale Environmental Monitoring Using WSN/UAV/Crowdsensing: A Review of Applications, Signal Processing, and Future Perspectives

Fighting Earth's degradation and safeguarding the environment are subjects of topical interest and sources of hot debate in today's society. According to the United Nations, there is a compelling need to take immediate actions worldwide and to implement large-scale monitoring policies aimed at counteracting the unprecedented levels of air, land, and water pollution. This requires going beyond the legacy technologies currently employed by government authorities and adopting more advanced systems that guarantee a continuous and pervasive monitoring of the environment in all its different aspects. In this paper, we take the research on integrated and large-scale environmental monitoring a step further by providing a comprehensive review that covers transversally all the main applications of wireless sensor networks (WSNs), unmanned aerial vehicles (UAVs), and crowdsensing monitoring technologies. By outlining the available solutions and current limitations, we identify in the cooperation among terrestrial (WSN/crowdsensing) and aerial (UAVs) sensing, coupled with the adoption of advanced signal processing techniques, the major pillars at the basis of future integrated (air, land, and water) and large-scale environmental monitoring systems. This review not only consolidates the progresses achieved in the field of environmental monitoring, but also sheds new lights on potential future research directions and synergies among different research areas.

Sensing Technology Applications in the Mining Industry-A Systematic Review

Introduction Industry 4.0 has enhanced technological development in all fields. Currently, one can analyse, treat, and model completely different variables in real time; these include production, environmental, and occupational variables. Resultingly, there has been a significant improvement in the quality of life of workers, the environment, and in businesses in general, encouraging the implementation of continuous improvement measures. However, it is not entirely clear how the mining industry is evolving alongside this industrial evolution. With this in mind, this systematic review aimed to find sensing technology applications within this sector, in order to assist the mining industry in its goal to evolve digitally. Methodology: The research and reporting of this article were carried out by means of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Results and discussion: A total of 29 papers were included in the study, with sensors being applied in several fields, namely safety, management, and localisation. Three different implementation phases were identified regarding its execution: prototype, trial, and (already) implemented. The overall results highlighted that many mechanisms are in need of improvement in underground settings. This might be due to the fact that underground mining has particular safety challenges. Conclusions: Ventilation and mapping are primary issues to be solved in the underground setting. With regard to the surface setting, the focus is directed toward slope stability and ways of improving it regarding monitoring and prevention. The literature screening revealed a tendency in these systems to keep advancing in technologically, becoming increasingly more intelligent. In the near future, it is expected that a more technologically advanced mining industry will arise, and this will be created and sustained by the optimisation of processes, equipment, and work practices, in order to improve both the quality of life of people and the health of the environment.

Flower Mapping in Grasslands With Drones and Deep Learning

Manual assessment of flower abundance of different flowering plant species in grasslands is a time-consuming process. We present an automated approach to determine the flower abundance in grasslands from drone-based aerial images by using deep learning (Faster R-CNN) object detection approach, which was trained and evaluated on data from five flights at two sites. Our deep learning network was able to identify and classify individual flowers. The novel method allowed generating spatially explicit maps of flower abundance that met or exceeded the accuracy of the manual-count-data extrapolation method while being less labor intensive. The results were very good for some types of flowers, with precision and recall being close to or higher than 90%. Other flowers were detected poorly due to reasons such as lack of enough training data, appearance changes due to phenology, or flowers being too small to be reliably distinguishable on the aerial images. The method was able to give precise estimates of the abundance of many flowering plant species. In the future, the collection of more training data will allow better predictions for the flowers that are not well predicted yet. The developed pipeline can be applied to any sort of aerial object detection problem.

Early Detection of Basal Stem Rot Disease in Oil Palm Tree Using Unmanned Aerial Vehicle-Based Hyperspectral Imaging

Early detection of basal stem rot (BSR) disease in oil palm trees is important for the sustainable production of palm oil in the limited land for plantation in Southeast Asia. However, previous studies based on satellite and aircraft hyperspectral remote sensing could not discriminate oil palm trees in the early-stage of the BSR disease from healthy or late-stage trees. In this study, hyperspectral imaging of oil palm trees from an unmanned aerial vehicle (UAV) and machine learning using a random forest algorithm were employed for the classification of four infection categories of the BSR disease: healthy, early-stage, late-stage, and dead trees. A concentric disk segmentation was applied to tree crown segmentation at the sub-plant scale, and recursive feature elimination was used for feature selection. The results revealed that the classification performance for the early-stage trees is maximum at the specific tree crown segments, and only a few spectral bands in the red-edge region are sufficient to classify the infection categories. These findings will be useful for future UAV-based multispectral imaging to efficiently cover a wide area of oil palm plantations for the early detection of BSR disease.

A Semi-Empirical Anisotropy Correction Model for UAS-Based Multispectral Images of Bare Soil

The recent developments in the performance and miniaturization of unmanned aircraft systems (UAS) and multispectral imaging sensors provide new tools for the assessment of the spatial and temporal variability of soil properties at sub-meter resolution and at relatively low costs, in comparison to traditional chemical analysis. The accuracy of multispectral data is nevertheless influenced by the anisotropic behaviour of natural surfaces, framed in the general theory of the bidirectional reflectance distribution function (BRDF). Accounting for BRDF effects in multispectral data is paramount before formulating any scientific interpretation. This study presents a semi-empirical spectral normalization methodology for UAS-based multispectral imaging datasets of bare soils to account for the effects of the BRDF, based on the application of an anisotropy factor (ANIF). A dataset of images from 15 flights over bare soil fields in the Belgian loam belt was used to calibrate a model relating the ANIF to a wide range of illumination geometry conditions by using only two angles: relative sensor-pixel-sun zenith and relative sensor-pixel-sun azimuth. The employment of ANIF-corrected images for multispectral orthomosaic generation with photogrammetric software provided spectral maps free of anisotropic-related artefacts in most cases, as assessed by several ad hoc indexes, and was also tested on an independent validation set. Most notably, the standard deviation in the measured reflectance of the same georeferenced point by different pictures decreased from 0.032 to 0.023 (p < 0.05) in the calibration dataset and from 0.037 to 0.030 in the validation dataset. The validation dataset, however, showed the presence of some systematic errors, the causes of which require further investigation.

Evaluation of Random Forests (RF) for Regional and Local-Scale Wheat Yield Prediction in Southeast Australia

Wheat accounts for more than 50% of Australia's total grain production. The capability to generate accurate in-season yield predictions is important across all components of the agricultural value chain. The literature on wheat yield prediction has motivated the need for more novel works evaluating machine learning techniques such as random forests (RF) at multiple scales. This research applied a Random Forest Regression (RFR) technique to build regional and local-scale yield prediction models at the pixel level for three southeast Australian wheat-growing paddocks, each located in Victoria (VIC), New South Wales (NSW) and South Australia (SA) using 2018 yield maps from data supplied by collaborating farmers. Time-series Normalized Difference Vegetation Index (NDVI) data derived from Planet's high spatio-temporal resolution imagery, meteorological variables and yield data were used to train, test and validate the models at pixel level using Python libraries for (a) regional-scale three-paddock composite and (b) individual paddocks. The composite region-wide RF model prediction for the three paddocks performed well (R² = 0.86, RMSE = 0.18 t ha^-1). RF models for individual paddocks in VIC (R² = 0.89, RMSE = 0.15 t ha^-1) and NSW (R² = 0.87, RMSE = 0.07 t ha^-1) performed well, but moderate performance was seen for SA (R² = 0.45, RMSE = 0.25 t ha^-1). Generally, high values were underpredicted and low values overpredicted. This study demonstrated the feasibility of applying RF modeling on satellite imagery and yielded 'big data' for regional as well as local-scale yield prediction.

Spectral Comparison of UAV-Based Hyper and Multispectral Cameras for Precision Viticulture

Analysis of the spectral response of vegetation using optical sensors for non-destructive remote monitoring represents a key element for crop monitoring. Considering the wide presence on the market of unmanned aerial vehicle (UAVs) based commercial solutions, the need emerges for clear information on the performance of these products to guide the end-user in their choice and utilization for precision agriculture applications. This work aims to compare two UAV based commercial products, represented by DJI P4M and SENOP HSC-2 for the acquisition of multispectral and hyperspectral images, respectively, in vineyards. The accuracy of both cameras was evaluated on 6 different targets commonly found in vineyards, represented by bare soil, bare-stony soil, stony soil, soil with dry grass, partially grass covered soil and canopy. Given the importance of the radiometric calibration, four methods for multispectral images correction were evaluated, taking in account the irradiance sensor equipped on the camera (M1–M2) and the use of an empirical line model (ELM) based on reference reflectance panels (M3–M4). In addition, different DJI P4M exposure setups were evaluated. The performance of the cameras was evaluated by means of the calculation of three widely used vegetation indices (VIs), as percentage error (PE) with respect to ground truth spectroradiometer measurements. The results highlighted the importance of reference panels for the radiometric calibration of multispectral images (M1–M2 average PE = 21.8–100.0%; M3–M4 average PE = 11.9–29.5%). Generally, the hyperspectral camera provided the best accuracy with a PE ranging between 1.0% and 13.6%. Both cameras showed higher performance on the pure canopy pixel target, compared to mixed targets. However, this issue can be easily solved by applying widespread segmentation techniques for the row extraction. This work provides insights to assist end-users in the UAV spectral monitoring to obtain reliable information for the analysis of spatio-temporal variability within vineyards.

Overview of Propulsion Systems for Unmanned Aerial Vehicles

Unmanned Aerial Vehicle (UAV) propulsion technology is significantly related to the flight performance of UAVs, which has become one of the most important development directions of aviation. It should be noted that UAVs have three types of propulsion systems, namely the fuel, hybrid fuel-electric, and pure electric, respectively. This paper presents and discusses the classification, working principles, characteristics, and critical technologies of these three types of propulsion systems. It is helpful to establish the development framework of the UAV propulsion system and provide the essential information on electric propulsion UAVs. Additionally, future technologies and development, including the high-power density motors, converters, power supplies, are discussed for the electric propulsion UAVs. In the near future, the electric propulsion system would be widely used in UAVs. The high-power density system would become the development trend of electric UAVs. Thus, this review article provides comprehensive views and multiple comparisons of propulsion systems for UAVs.