7,194 research outputs found
Unsupervised learning-based approach for detecting 3D edges in depth maps
3D edge features, which represent the boundaries between different objects or surfaces in a 3D scene, are crucial for many computer vision tasks, including object recognition, tracking, and segmentation. They also have numerous real-world applications in the field of robotics, such as vision-guided grasping and manipulation of objects. To extract these features in the noisy real-world depth data, reliable 3D edge detectors are indispensable. However, currently available 3D edge detection methods are either highly parameterized or require ground truth labelling, which makes them challenging to use for practical applications. To this extent, we present a new 3D edge detection approach using unsupervised classification. Our method learns features from depth maps at three different scales using an encoder-decoder network, from which edge-specific features are extracted. These edge features are then clustered using learning to classify each point as an edge or not. The proposed method has two key benefits. First, it eliminates the need for manual fine-tuning of data-specific hyper-parameters and automatically selects threshold values for edge classification. Second, the method does not require any labelled training data, unlike many state-of-the-art methods that require supervised training with extensive hand-labelled datasets. The proposed method is evaluated on five benchmark datasets with single and multi-object scenes, and compared with four state-of-the-art edge detection methods from the literature. Results demonstrate that the proposed method achieves competitive performance, despite not using any labelled data or relying on hand-tuning of key parameters.</p
Predicting Alder shrub expansion in Sub-Arctic Alaska using machine learning, satellite data, and environmental variables
The wider Fairbanks area, a sub-Arctic region of Alaska, USA, is home to a variety of alpine, oroarctic tundra that is being impacted by climate warming. This has resulted in an infilling and expansion of shrubs across the tundra and an elevational increase in the range limits of tall shrubs. Expansion of Alder (a key pioneer tall shrub) is thought to result from Arctic warming and shifts in its spread are likely to be a result of such warming.
Alder can fix atmospheric nitrogen by virtue of a mutualistic association with soil bacteria, which subsequently becomes available to other shrubs, potentially relieving local soil nitrogen limitations and promoting a positive growth response to climate warming. This potential landscape-scale change requires information of change at a suitable scale. However, Alder and other tall shrubs have been hard to measure using existing remote sensing approaches alone. This is mainly due to issues surrounding data availability and suitable spatial resolution of imagery.
Satellite remote sensing and environmental data are combined to create a map of Alder expansion across the wider Fairbanks area. A methodology is presented where ecological variables are integrated into prediction maps using a combination of regression and machine learning to estimate spatial extents. A baseline for a minimum number of high resolution training polygons is found to understand minimum required inputs. Field-based validation data were collected using a random sampling design across four different locations within the Yukon-Koyukuk area, Alaska. The combination of satellite data and environmental variables yields the best results for predicting Alder locations across the study area with a model accuracy of 0.99 and User’s accuracy of 43.66%. Orthomosaics as validation data are found to be very useful, enabling better quantification of smaller plant functional types for more accurate error matrix class assignment increasing overall model accuracy
Forecasting global climate drivers using Gaussian processes and convolutional autoencoders
Machine learning (ML) methods have become an important tool for modelling and forecasting complex high-dimensional spatiotemporal datasets such as those found in environmental and climate modelling applications. ML approaches can offer a fast, low-cost alternative to short-term forecasting than expensive numerical simulation while addressing a significant outstanding limitation of numerical modelling by being able to robustly and dynamically quantify predictive uncertainty. Low-cost and near-instantaneous forecasting of high-level climate variables has clear applications in early warning systems, nowcasting, and parameterising small-scale locally relevant simulations. This paper presents a novel approach for multi-task spatiotemporal regression by combining data-driven autoencoders with Gaussian Processes (GP) to produce a probabilistic tensor-based regression model. The proposed method is demonstrated for forecasting one-step-ahead temperature and pressure on a global scale simultaneously. By conducting probabilistic regression in the learned latent space, samples can be propagated back to the original feature space to produce uncertainty estimates at a vastly reduced computational cost. The composite GP-autoencoder model was able to simultaneously forecast global temperature and pressure values with average errors of 3.82 °C and 638 hPa, respectively. Further, on average the true values were within the proposed posterior distribution 95.6% of the time illustrating that the model produces a well-calibrated predictive posterior distribution
DSCA-PSPNet: Dynamic spatial-channel attention pyramid scene parsing network for sugarcane field segmentation in satellite imagery
Sugarcane plays a vital role in many global economies, and its efficient cultivation is critical for sustainable development. A central challenge in sugarcane yield prediction and cultivation management is the precise segmentation of sugarcane fields from satellite imagery. This task is complicated by numerous factors, including varying environmental conditions, scale variability, and spectral similarities between crops and non-crop elements. To address these segmentation challenges, we introduce DSCA-PSPNet, a novel deep learning model with a unique architecture that combines a modified ResNet34 backbone, the Pyramid Scene Parsing Network (PSPNet), and newly proposed Dynamic Squeeze-and-Excitation Context (D-scSE) blocks. Our model effectively adapts to discern the importance of both spatial and channel-wise information, providing superior feature representation for sugarcane fields. We have also created a comprehensive high-resolution satellite imagery dataset from Guangxi’s Fusui County, captured on December 17, 2017, which encompasses a broad spectrum of sugarcane field characteristics and environmental conditions. In comparative studies, DSCA-PSPNet outperforms other state-of-the-art models, achieving an Intersection over Union (IoU) of 87.58%, an accuracy of 92.34%, a precision of 93.80%, a recall of 93.21%, and an F1-Score of 92.38%. Application tests on an RTX 3090 GPU, with input image resolutions of 512 × 512, yielded a prediction time of 4.57ms, a parameter size of 22.57MB, GFLOPs of 11.41, and a memory size of 84.47MB. An ablation study emphasized the vital role of the D-scSE module in enhancing DSCA-PSPNet’s performance. Our contributions in dataset generation and model development open new avenues for tackling the complexities of sugarcane field segmentation, thus contributing to advances in precision agriculture. The source code and dataset will be available on the GitHub repository https://github.com/JulioYuan/DSCA-PSPNet/tree/main
A Review of the Factors Affecting Adoption of Precision Agriculture Applications in Cotton Production
Precision agriculture (PA) is a modern farming management system adopted throughout the world, which employs cropping practices by observing and measuring the temporal and spatial variability in fields to enhance the sustainability of agricultural production through more efficient use of land, water, fuel, fertilizer, and pesticides. The efficiency of precision agriculture technologies (PAT) in agricultural production mainly depends on the use of site-specific agricultural inputs accurately through decision support mechanisms by observing and measuring the variables such as soil condition, plant health, and weed intensity. Although there have been significant developments in PAT, especially remote sensing as a key source of information available in support of PA in recent years, its adoption has been very slow by farmers due to a variety of reasons. The main aim of this chapter is to provide a critical overview of how recent developments in sensing technologies, geostatistical analysis, data fusion, and interpolation techniques can be used in the cotton production systems to optimize yields while minimizing water, chemical pesticide, and nitrogen inputs and analysis the main factors influencing the adoption of PAT by cotton farmers. Therefore, this chapter includes a compressive literature survey of the studies done on the current use and trends of PAT, and on farm level use of PA in cotton production worldwide
A Multi-Scale Feasibility Study into Acid Mine Drainage (AMD) Monitoring Using Same-Day Observations
This is the final version. Available on open access from MDPI via the DOI in this recordGlobally, many mines emit acid mine drainage (AMD) during and after their operational life cycle. AMD can affect large and often inaccessible areas. This leads to expensive monitoring via conventional ground-based sampling. Recent advances in remote sensing which are both non-intrusive and less time-consuming hold the potential to unlock a new paradigm of automated AMD analysis. Herein, we test the feasibility of remote sensing as a standalone tool to map AMD at various spatial resolutions and altitudes in water-impacted mining environments. This was achieved through the same-day collection of satellite-based simulated Sentinel-2 (S2) and PlanetScope (PS2.SD) imagery and drone-based UAV Nano-Hyperspec (UAV) imagery, in tandem with ground-based visible and short-wave infrared analysis. The study site was a historic tin and copper mine in Cornwall, UK. The ground-based data collection took place on the 30 July 2020. Ferric (Fe(III) iron) band ratio (665/560 nm wavelength) was used as an AMD proxy to map AMD pixel distribution. The relationship between remote-sensed Fe(III) iron reflectance values and ground-based Fe(III) iron reflectance values deteriorated as sensor spatial resolution decreased from high-resolution UAV imagery (<50 mm2 per pixel; r2 = 0.78) to medium-resolution PlanetScope Dove-R (3 m2 per pixel; r2 = 0.51) and low-resolution simulated Sentinel-2 (10 m2 per pixel; r2 = 0.23). A fractioned water pixel (FWP) analysis was used to identify mixed pixels between land and the nearby waterbody, which lowered spectral reflectance. Increases in total mixed pixels were observed as the spatial resolution of sensors decreased (UAV: 2.4%, PS: 3.7%, S2: 8.5%). This study demonstrates that remote sensing is a non-intrusive AMD surveying tool with varying degrees of effectiveness relative to sensor spatial resolution. This was achieved by identifying and successfully mapping a cross-sensor Fe(III) iron band ratio whilst recognizing water bodies as reflectance inhibitors for passive sensors.European Space AgencyUniversity of Exete
Investigation of the metabolism of rare nucleotides in plants
Nucleotides are metabolites involved in primary metabolism, and specialized
metabolism and have a regulatory role in various biochemical reactions in all forms of life. While in other organisms, the nucleotide metabolome was characterized
extensively, comparatively little is known about the cellular concentrations of
nucleotides in plants. The aim of this dissertation was to investigate the nucleotide metabolome and enzymes influencing the composition and quantities of nucleotides in plants. For this purpose, a method for the analysis of nucleotides and nucleosides in plants and algae was developed (Chapter 2.1), which comprises efficient quenching of enzymatic
activity, liquid-liquid extraction and solid phase extraction employing a weak-anionexchange resin. This method allowed the analysis of the nucleotide metabolome of plants in great depth including the quantification of low abundant deoxyribonucleotides and deoxyribonucleosides. The details of the method were summarized in an article, serving as a laboratory protocol (Chapter 2.2).
Furthermore, we contributed a review article (Chapter 2.3) that summarizes the
literature about nucleotide analysis and recent technological advances with a focus on plants and factors influencing and hindering the analysis of nucleotides in plants, i.e., a complex metabolic matrix, highly stable phosphatases and physicochemical
properties of nucleotides. To analyze the sub-cellular concentrations of metabolites, a protocol for the rapid isolation of highly pure mitochondria utilizing affinity chromatography was developed (Chapter 2.4).
The method for the purification of nucleotides furthermore contributed to the
comprehensive analysis of the nucleotide metabolome in germinating seeds and in
establishing seedlings of A. thaliana, with a focus on genes involved in the synthesis of thymidilates (Chapter 2.5) and the characterization of a novel enzyme of purine nucleotide degradation, the XANTHOSINE MONOPHOSPHATE PHOSPHATASE (Chapter 2.6). Protein homology analysis comparing A. thaliana, S. cerevisiae, and H. sapiens led to the identification and characterization of an enzyme involved in the metabolite damage repair system of plants, the INOSINE TRIPHOSPHATE PYROPHOSPHATASE (Chapter 2.7). It was shown that this enzyme dephosphorylates deaminated purine nucleotide triphosphates and thus prevents their incorporation into nucleic acids. Lossof-function mutants senesce early and have a constitutively increased content of salicylic acid. Also, the source of deaminated purine nucleotides in plants was investigated and it was shown that abiotic factors contribute to nucleotide damage.Nukleotide sind Metaboliten, die am Primärstoffwechsel und an spezialisierten
Stoffwechselvorgängen beteiligt sind und eine regulierende Rolle bei verschiedenen
biochemischen Reaktionen in allen Lebensformen spielen. Während bei anderen
Organismen das Nukleotidmetabolom umfassend charakterisiert wurde, ist in Pflanzen
vergleichsweise wenig über die zellulären Konzentrationen von Nukleotiden bekannt.
Ziel dieser Dissertation war es, das Nukleotidmetabolom und die Enzyme zu
untersuchen, die die Zusammensetzung und Menge der Nukleotide in Pflanzen
beeinflussen. Zu diesem Zweck wurde eine Methode zur Analyse von Nukleotiden und
Nukleosiden in Pflanzen und Algen entwickelt (Kapitel 2.1), die ein effizientes Stoppen
enzymatischer Aktivität, eine Flüssig-Flüssig-Extraktion und eine
Festphasenextraktion unter Verwendung eines schwachen Ionenaustauschers
umfasst. Mit dieser Methode konnte das Nukleotidmetabolom von Pflanzen eingehend
analysiert werden, einschlieĂźlich der Quantifizierung von Desoxyribonukleotiden und
Desoxyribonukleosiden mit geringer Abundanz. Die Einzelheiten der Methode wurden
in einem Artikel zusammengefasst, der als Laborprotokoll dient (Kapitel 2.2).
DarĂĽber hinaus wurde ein Ăśbersichtsartikel (Kapitel 2.3) verfasst, der die Literatur
ĂĽber die Analyse von Nukleotiden und die jĂĽngsten technologischen Fortschritte
zusammenfasst. Der Schwerpunkt lag hierbei auf Pflanzen und Faktoren, die die
Analyse von Nukleotiden in Pflanzen beeinflussen oder behindern, d. h. eine komplexe
Matrix, hochstabile Phosphatasen und physikalisch-chemische Eigenschaften von
Nukleotiden.
Um die subzellulären Konzentrationen von Metaboliten zu analysieren, wurde ein
Protokoll fĂĽr die schnelle Isolierung hochreiner Mitochondrien unter Verwendung einer
Affinitätschromatographie entwickelt (Kapitel 2.4).
Die Methode zur Analyse von Nukleotiden trug auĂźerdem zu einer umfassenden
Analyse des Nukleotidmetaboloms in keimenden Samen und in sich etablierenden
Keimlingen von A. thaliana bei, wobei der Schwerpunkt auf Genen lag, die an der
Synthese von Thymidilaten beteiligt sind (Kapitel 2.5), sowie zu der Charakterisierung
eines neuen Enzyms des Purinnukleotidabbaus, der XANTHOSINE
MONOPHOSPHATE PHOSPHATASE (Kapitel 2.6). Eine Proteinhomologieanalyse, die A. thaliana, S. cerevisiae und H. sapiens
miteinander verglich fĂĽhrte zur Identifizierung und Charakterisierung eines Enzyms,
das an der Reparatur von geschädigten Metaboliten in Pflanzen beteiligt ist, der
INOSINE TRIPHOSPHATE PYROPHOSPHATASE (Kapitel 2.7). Es konnte gezeigt
werden, dass dieses Enzym desaminierte Purinnukleotidtriphosphate
dephosphoryliert und so deren Einbau in Nukleinsäuren verhindert.
Funktionsverlustmutanten altern früh und weisen einen konstitutiv erhöhten Gehalt an Salicylsäure auf. Außerdem wurde die Quelle der desaminierten Purinnukleotide in Pflanzen untersucht, und es wurde gezeigt, dass abiotische Faktoren zur
Nukleotidschädigung beitragen
Multitemporal Feature-Level Fusion on Hyperspectral and LiDAR Data in the Urban Environment
publishedVersio
Review on Automatic Variable-Rate Spraying Systems Based on Orchard Canopy Characterization
Pesticide consumption and environmental pollution in orchards can be greatly decreased by combining variable-rate spray treatments with proportional control systems. Nowadays, farmers can use variable-rate canopy spraying to apply weed killers only where they are required which provides environmental friendly and cost-effective crop protection chemicals. Moreover, restricting the use of pesticides as Plant Protection Products (PPP) while maintaining appropriate canopy deposition is a serious challenge. Additionally, automatic sprayers that adjust their application rates to the size and shape of orchard plantations has indicated a significant potential for reducing the use of pesticides. For the automatic spraying, the existing research used an Artificial Intelligence and Machine Learning. Also, spraying efficiency can be increased by lowering spray losses from ground deposition and off-target drift. Therefore, this study involves a thorough examination of the existing variable-rate spraying techniques in orchards. In addition to providing examples of their predictions and briefly addressing the influences on spraying parameters, it also presents various alternatives to avoiding pesticide overuse and explores their advantages and disadvantages
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