A Low-Cost Global Navigation Satellite System Positioning Accuracy Assessment Method for Agricultural Machinery

The high-precision positioning and navigation of agricultural machinery represent a backbone for precision agriculture, while its worldwide implementation is in rapid growth. Previous studies improved low-cost global navigation satellite system (GNSS) hardware solutions and fused GNSS data with complementary sources, but there is still no affordable and flexible framework for positioning accuracy assessment of agricultural machinery. Such a low-cost method was proposed in this study, simulating the actual movement of the agricultural machinery during agrotechnical operations. Four of the most commonly used GNSS corrections in Croatia were evaluated in two repetitions: Croatian Positioning System (CROPOS), individual base station, Satellite-based Augmentation Systems (SBASs), and an absolute positioning method using a smartphone. CROPOS and base station produced the highest mean GNSS positioning accuracy of 2.4 and 2.9 cm, respectively, but both of these corrections produced lower accuracy than declared. All evaluated corrections produced significantly different median values in two repetitions, representing inconsistency of the positioning accuracy regarding field conditions. While the proposed method allowed flexible and effective application in the field, future studies will be directed towards the reduction of the operator’s subjective impact, mainly by implementing autosteering solutions in agricultural machinery.

Accuracy Assessment of 3D Model Based on Laser Scan and Photogrammetry Data

A three-dimensional (3D) model extraction represents the best way to reflect the reality in all details. This explains the trends and tendency of many scientific disciplines towards making measurements, calculations and monitoring in various fields using such model. Although there are many ways to produce the 3D model like as images, integration techniques, and laser scanning, however, the quality of their products is not the same in terms of accuracy and detail. This article aims to assess the 3D point clouds model accuracy results from close range images and laser scan data based on Agi soft photoscan and cloud compare software to determine the compatibility of both datasets for several applications. College of Science, Departments of Mathematics and Computer in the University of Baghdad campus were exploited to create the proposed 3D model as this area location, which is one of the distinctive features of the university, allows making measurements freely from all sides. Results of this study supported by statistical analysis including 2 sample T-test and RMSE calculation in addition to visual comparison. Through this research, we note that the laser3D model provides many points in a short time, so it will reduce the field work and also its data is faster in processing to produce a reliable model of the scanned area compared with data derived from photogrammetry, then the difference were computed for all the reference points.

Accuracy Assessment for points coordinates surveyed using low-cost Unmanned Aerial Vehicle and Global Positioning System with 3Dsurvey and 3DF Zephyr software

Ground Control Points GCPs are the only way to obtain accurate positions in aerial surveys. At least three points should be utilized, and the model will get increasingly accurate in X, Y, and Z coordinates as the number rises. The accuracy of the 3D model created from aerial photography is also affected by the arrangement of GCPs. The goal of this research is to determine the optimal number and arrangement of GCPs in order to obtain the lowest possible error in point positioning. A conventional UAV called DJI Mavic 2 pro was used to photograph one and a half square kilometer site at an elevation equal to hundred meters from earth’s surface with nadir camera configuration. GSD (ground sampling distance) of 2.3 centimeters was used to collect 1515 pictures. 62 GCPs were observed in PPK (Post Processing kinematic) method using a DGPS (differential global positioning system) receiver GS 15 from Leica. The study area was split into two areas, one with a straight arrangement of GCPs and the other with a diagonal arrangement of GCPs. The pictures were processed using 3Dsurvey and 3DF Zephyr software utilizing a full bundle adjustment procedure with increasing GCPs number beginning with three GCPs and ending with twenty-six GCPs for both arrangement layout, with the other points serving as check points for the model’s accuracy at each attempt. The check point coordinates obtained were compared to the DGPS coordinates. The result indicates the optimal GCP number needed for the most accurate position and spread layout. That the minimum gap between adjacent GCPs ought to be not over than 100 meters and spread homogenously.

Identificação das Dunas do Atacama (Norte do Chile) a partir da avaliação de três algoritmos no Google Earth Engine

As dunas correspondem a processos de sedimentação eólica, que podem estar tanto nas áreas costeiras marinhas, como no interior do continente com algumas diferenças na modelagem. No Sul do deserto do Atacama, no Norte do Chile, há um conjunto de seis campos de dunas intermontanhas chamadas Mar de Dunas do Atacama, as quais têm tipologias complexas de dunas do deserto, que podem ser ativas, semiativas ou estabilizadas. O seu monitoramento é conveniente para conhecer detalhes sobre a possível invasão de areias das dunas ao sul do rio Copiapó. Dessa forma, esta pesquisa tem como objetivo avaliar os métodos de classificação supervisionada Random Forest, CART e SmileCART através de duas metodologias de amostragens, aleatória e estratificada, numa imagem Landsat 5 na plataforma em nuvem Google Earth Engine, a fim de verificar qual método oferece o melhor resultado para o mapeamento do Mar de Dunas do Atacama. Para conseguir este objetivo, foram criados polígonos de classes para a realização da amostragem aleatória estratificada e chave de interpretação para amostragem aleatória simples. O processo de avaliação da acurácia foi feito através de imagem Sentinel 2 com a aplicação dos índices de Simultaneidade Geográfica, Erros de Comissão e Omissão, e Exatidão Global. Observou-se como resultados para os algoritmos testados, que os três algoritmos foram eficientes para o mapeamento das Dunas do Atacama, entretanto, a técnica de classificação supervisionada por CART, com a metodologia da amostragem aleatória simples, representou o melhor desempenho. Identification of the Atacama Dunes (Northern Chile) from the evaluation of three algorithms on Google Earth EngineA B S T R A C TThe dunes correspond to wind sedimentation processes, which can be found both in marine coastal areas and in the interior of the continent with some differences in modeling. In the south of the Atacama desert, in northern Chile, there are a set of six inter-mountain dune fields called Mar de Dunas do Atacama, which have complex types of desert dunes, which can be active, semi-active or stabilized. Its monitoring is convenient to know details about the possible invasion of sand from the dunes south of the Copiapó River. Thus, this research aims to evaluate the supervised classification methods Random Forest, CART and SmileCART through two sampling methodologies, random and stratified, in a Landsat 5 image on the Google Earth Engine cloud platform, in order to verify which method offers the best result for mapping the Atacama Dunes Sea. In order to achieve this objective, class polygons were created to perform stratified random sampling and the interpretation key for simple random sampling. The accuracy assessment process was performed using a Sentinel 2 image with the application of the Geographic Simultaneity indices and the Commission and Omission Errors. It was observed as results for the tested algorithms, that the three algorithms were efficient for mapping the Atacama Dunes, however, the CART supervised classification technique, with the simple random sampling methodology, represents the best performance.

Infrared Thermography for Measuring Elevated Body Temperature: Clinical Accuracy, Calibration, and Evaluation

Infrared thermographs (IRTs) implemented according to standardized best practices have shown strong potential for detecting elevated body temperatures (EBT), which may be useful in clinical settings and during infectious disease epidemics. However, optimal IRT calibration methods have not been established and the clinical performance of these devices relative to the more common non-contact infrared thermometers (NCITs) remains unclear. In addition to confirming the findings of our preliminary analysis of clinical study results, the primary intent of this study was to compare methods for IRT calibration and identify best practices for assessing the performance of IRTs intended to detect EBT. A key secondary aim was to compare IRT clinical accuracy to that of NCITs. We performed a clinical thermographic imaging study of more than 1000 subjects, acquiring temperature data from several facial locations that, along with reference oral temperatures, were used to calibrate two IRT systems based on seven different regression methods. Oral temperatures imputed from facial data were used to evaluate IRT clinical accuracy based on metrics such as clinical bias (Δcb), repeatability, root-mean-square difference, and sensitivity/specificity. We proposed several calibration approaches designed to account for the non-uniform data density across the temperature range and a constant offset approach tended to show better ability to detect EBT. As in our prior study, inner canthi or full-face maximum temperatures provided the highest clinical accuracy. With an optimal calibration approach, these methods achieved a Δcb between ±0.03 °C with standard deviation (σΔcb) less than 0.3 °C, and sensitivity/specificity between 84% and 94%. Results of forehead-center measurements with NCITs or IRTs indicated reduced performance. An analysis of the complete clinical data set confirms the essential findings of our preliminary evaluation, with minor differences. Our findings provide novel insights into methods and metrics for the clinical accuracy assessment of IRTs. Furthermore, our results indicate that calibration approaches providing the highest clinical accuracy in the 37–38.5 °C range may be most effective for measuring EBT. While device performance depends on many factors, IRTs can provide superior performance to NCITs.

Mapping Invasive Plant Species with Hyperspectral Data Based on Iterative Accuracy Assessment Techniques

Recent developments in computer hardware made it possible to assess the viability of permutation-based approaches in image classification. Such approaches sample a reference dataset multiple times in order to train an arbitrary number of machine learning models while assessing their accuracy. So-called iterative accuracy assessment techniques or Monte-Carlo-based approaches can be a useful tool when it comes to assessment of algorithm/model performance but are lacking when it comes to actual image classification and map creation. Due to the multitude of models trained, one has to somehow reason which one of them, if any, should be used in the creation of a map. This poses an interesting challenge since there is a clear disconnect between algorithm assessment and the act of map creation. Our work shows one of the ways this disconnect can be bridged. We calculate how often a given pixel was classified as given class in all variations of a multitude of post-classification images delivered by models trained during the iterative assessment procedure. As a classification problem, a mapping of Calamagrostis epigejos, Rubus spp., Solidago spp. invasive plant species using three HySpex hyperspectral datasets collected in June, August and September was used. As a classification algorithm, the support vector machine approach was chosen, with training hyperparameters obtained using a grid search approach. The resulting maps obtained F1-scores ranging from 0.87 to 0.89 for Calamagrostis epigejos, 0.89 to 0.97 for Rubus spp. and 0.99 for Solidago spp.