Galaxy and mass assembly (GAMA) : The wavelength-dependent sizes and profiles of galaxies revealed by MegaMorph

We investigate the relationship between colour and structure within galaxies using a large, volume-limited sample of bright, low-redshift galaxies with optical-near-infrared imaging from the Galaxy AndMass Assembly survey.We fit single-component,wavelength-dependent, elliptical Sérsic models to all passbands simultaneously, using software developed by the MegaMorph project. Dividing our sample by n and colour, the recovered wavelength variations in effective radius (Re) and Sérsic index (n) reveal the internal structure, and hence formation history, of different types of galaxies. All these trends depend on n; some have an additional dependence on galaxy colour. Late-type galaxies (nr 2.5), even though they maintain constant n with wavelength, revealing that ellipticals are a superimposition of different stellar populations associated with multiple collapse and merging events. Processes leading to structures with larger Re must be associated with lower metallicity or younger stellar populations. This appears to rule out the formation of young cores through dissipative gas accretion as an important mechanism in the recent lives of luminous elliptical galaxies.Peer reviewe

Detecting Volunteer Cotton Plants in a Corn Field with Deep Learning on UAV Remote-Sensing Imagery

The cotton boll weevil, Anthonomus grandis Boheman is a serious pest to the U.S. cotton industry that has cost more than 16 billion USD in damages since it entered the United States from Mexico in the late 1800s. This pest has been nearly eradicated; however, southern part of Texas still faces this issue and is always prone to the pest reinfestation each year due to its sub-tropical climate where cotton plants can grow year-round. Volunteer cotton (VC) plants growing in the fields of inter-seasonal crops, like corn, can serve as hosts to these pests once they reach pin-head square stage (5-6 leaf stage) and therefore need to be detected, located, and destroyed or sprayed . In this paper, we present a study to detect VC plants in a corn field using YOLOv3 on three band aerial images collected by unmanned aircraft system (UAS). The two-fold objectives of this paper were : (i) to determine whether YOLOv3 can be used for VC detection in a corn field using RGB (red, green, and blue) aerial images collected by UAS and (ii) to investigate the behavior of YOLOv3 on images at three different scales (320 x 320, S1; 416 x 416, S2; and 512 x 512, S3 pixels) based on average precision (AP), mean average precision (mAP) and F1-score at 95% confidence level. No significant differences existed for mAP among the three scales, while a significant difference was found for AP between S1 and S3 (p = 0.04) and S2 and S3 (p = 0.02). A significant difference was also found for F1-score between S2 and S3 (p = 0.02). The lack of significant differences of mAP at all the three scales indicated that the trained YOLOv3 model can be used on a computer vision-based remotely piloted aerial application system (RPAAS) for VC detection and spray application in near real-time.Comment: 38 Page

Computer Vision for Volunteer Cotton Detection in a Corn Field with UAS Remote Sensing Imagery and Spot Spray Applications

To control boll weevil (Anthonomus grandis L.) pest re-infestation in cotton fields, the current practices of volunteer cotton (VC) (Gossypium hirsutum L.) plant detection in fields of rotation crops like corn (Zea mays L.) and sorghum (Sorghum bicolor L.) involve manual field scouting at the edges of fields. This leads to many VC plants growing in the middle of fields remain undetected that continue to grow side by side along with corn and sorghum. When they reach pinhead squaring stage (5-6 leaves), they can serve as hosts for the boll weevil pests. Therefore, it is required to detect, locate and then precisely spot-spray them with chemicals. In this paper, we present the application of YOLOv5m on radiometrically and gamma-corrected low resolution (1.2 Megapixel) multispectral imagery for detecting and locating VC plants growing in the middle of tasseling (VT) growth stage of cornfield. Our results show that VC plants can be detected with a mean average precision (mAP) of 79% and classification accuracy of 78% on images of size 1207 x 923 pixels at an average inference speed of nearly 47 frames per second (FPS) on NVIDIA Tesla P100 GPU-16GB and 0.4 FPS on NVIDIA Jetson TX2 GPU. We also demonstrate the application of a customized unmanned aircraft systems (UAS) for spot-spray applications based on the developed computer vision (CV) algorithm and how it can be used for near real-time detection and mitigation of VC plants growing in corn fields for efficient management of the boll weevil pests.Comment: 39 page

Unmanned Aerial Vehicles for High-Throughput Phenotyping and Agronomic Research

Advances in automation and data science have led agriculturists to seek real-time, high-quality, high-volume crop data to accelerate crop improvement through breeding and to optimize agronomic practices. Breeders have recently gained massive data-collection capability in genome sequencing of plants. Faster phenotypic trait data collection and analysis relative to genetic data leads to faster and better selections in crop improvement. Furthermore, faster and higher-resolution crop data collection leads to greater capability for scientists and growers to improve precision-agriculture practices on increasingly larger farms; e.g., site-specific application of water and nutrients. Unmanned aerial vehicles (UAVs) have recently gained traction as agricultural data collection systems. Using UAVs for agricultural remote sensing is an innovative technology that differs from traditional remote sensing in more ways than strictly higher-resolution images; it provides many new and unique possibilities, as well as new and unique challenges. Herein we report on processes and lessons learned from year 1-the summer 2015 and winter 2016 growing seasons-of a large multidisciplinary project evaluating UAV images across a range of breeding and agronomic research trials on a large research farm. Included are team and project planning, UAV and sensor selection and integration, and data collection and analysis workflow. The study involved many crops and both breeding plots and agronomic fields. The project's goal was to develop methods for UAVs to collect high-quality, high-volume crop data with fast turnaround time to field scientists. The project included five teams: Administration, Flight Operations, Sensors, Data Management, and Field Research. Four case studies involving multiple crops in breeding and agronomic applications add practical descriptive detail. Lessons learned include critical information on sensors, air vehicles, and configuration parameters for both. As the first and most comprehensive project of its kind to date, these lessons are particularly salient to researchers embarking on agricultural research with UAVs

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2): Science Requirements, Concept, and Implementation

The Ice, Cloud, and land Elevation Satellite (ICESat) mission used laser altimetry measurements to determine changes in elevations of glaciers and ice sheets, as well as sea ice thickness distribution. These measurements have provided important information on the response of the cryosphere (Earths frozen surfaces) to changes in atmosphere and ocean condition. ICESat operated from 2003-2009 and provided repeat altimetry measurements not only to the cryosphere scientific community but also to the ocean, terrestrial and atmospheric scientific communities. The conclusive assessment of significant ongoing rapid changes in the Earths ice cover, in part supported by ICESat observations, has strengthened the need for sustained, high accuracy, repeat observations similar to what was provided by the ICESat mission. Following recommendations from the National Research Council for an ICESat follow-on mission, the ICESat-2 mission is now under development for planned launch in 2018. The primary scientific aims of the ICESat-2 mission are to continue measurements of sea ice freeboard and ice sheet elevation to determine their changes at scales from outlet glaciers to the entire ice sheet, and from 10s of meters to the entire polar oceans for sea ice freeboard. ICESat carried a single beam profiling laser altimeter that produced approximately 70 m diameter footprints on the surface of the Earth at approximately 150 m along-track intervals. In contrast, ICESat-2 will operate with three pairs of beams, each pair separated by about 3 km across-track with a pair spacing of 90 m. Each of the beams will have a nominal 17 m diameter footprint with an along-track sampling interval of 0.7 m. The differences in the ICESat-2 measurement concept are a result of overcoming some limitations associated with the approach used in the ICESat mission. The beam pair configuration of ICESat-2 allows for the determination of local cross-track slope, a significant factor in measuring elevation change for the outlet glaciers surrounding the Greenland and Antarctica coasts. The multiple beam pairs also provide improved spatial coverage. The dense spatial sampling eliminates along-track measurement gaps, and the small footprint diameter is especially useful for sea surface height measurements in the often narrow leads needed for sea ice freeboard and ice thickness retrievals. The ICESat-2 instrumentation concept uses a low energy 532 nm (green) laser in conjunction with single-photon sensitive detectors to measure range. Combining ICESat-2 data with altimetry data collected since the start of the ICESat mission in 2003, such as Operation IceBridge and ESAs CryoSat-2, will yield a 15+ year record of changes in ice sheet elevation and sea ice thickness. ICESat-2 will also provide information of mountain glacier and ice cap elevations changes, land and vegetation heights, inland water elevations, sea surface heights, and cloud layering and optical thickness

Towards an ISO Standard for Dialogue Act Annotation

This paper describes an ISO project developing an international standard for annotating dialogue with semantic information, in particular concerning the communicative functions of the utterances, the kind of content they address, and the dependency relations to what was said and done earlier in the dialogue. The project, registered as ISO 24617-2 Semantic annotation framework, Part 2: Dialogue acts”, is currently at DIS stage. 1

Mapping the Landscape of Citizen Science in Africa: Assessing its Potential Contributions to Sustainable Development Goals 6 and 11 on Access to Clean Water and Sanitation and Sustainable Cities

Data are vital for and creating knowledge-based solutions to development challenges facing Africa. As a result of gaps in government-funded data collection, and in the interest of promoting community engagement, there is a global movement towards consideration of nontraditional sources of data, including citizen science (CS) data. These data are particularly valuable when collected at a high resolution over large spatial extents and long time periods. CS projects and infrastructure are abundant and well documented in the Global North, while needs for participatory projects to fill environmental monitoring gaps may be greater in the Global South. The paper explores the contributions of citizen science projects originating in Africa for two Sustainable Development Goals (SDGs), namely SDG 6, and SDG 11 which are particularly important to the millions of low-income residents of cities across Africa. Using a mixed methods approach that involves online surveys, interviews, expert conference panels, and a desk review, we analyze a total of 53 CS projects focusing on water, sanitation, and urban planning. The paper addresses CS in Africa and CS for SDGs, and documents evidence for participatory and CS data collection in Africa. It also describes the survey methods, including approaches to training of volunteers, sources of funding, data collection methods, and objectives of the tools and projects. Finally, it provides reflections on the challenges of integrating CS into official statistics in Africa, and some lessons learnt from CS projects in Africa. This paper recommends the establishment of an open-source database, creation of a network of CS projects for communication and collaboration, the uptake of citizen-generated data, and continuous funding for such projects in Africa

Modelling Strategic Conversation: model, annotation design and corpus

International audienceA Gricean view of cognitive agents holds that agents are fully rational and adhere to the maxims of conversation that entail that speakers adopt shared intentions and fully aligned preferences–e.g. (Allen and Litman, 1987; Lochbaum, 1998). These assumptions are unwarranted in many conversational settings. In this paper we propose a different view and an annotation scheme for it

Language for Specific Purposes and Corpus-based Pedagogy

This chapter describes how corpus-based pedagogies are used for teaching and learning language for specific purposes (LSP). Corpus linguistics (CL) refers to the study of large quantities of authentic language using computer-assisted methods, which form the basis for computer-assisted language learning (CALL) that uses corpora for reference, exploration, and interactive learning. The use of corpora as reference resources to create LSP materials is described. Direct student uses of corpora are illustrated by three approaches to data-driven learning (DDL) where students engage in hands-on explorations of texts. A combination of indirect and direct corpus applications is shown in an illustration of interactive CALL technologies, including an example of an inclusive corpus-based tool for genre-based writing pedagogy. The chapter concludes with potential prospects for future developments in LSP