Precision Weed Management Based on UAS Image Streams, Machine Learning, and PWM Sprayers

Weed populations in agricultural production fields are often scattered and unevenly distributed; however, herbicides are broadcast across fields evenly. Although effective, in the case of post-emergent herbicides, exceedingly more pesticides are used than necessary. A novel weed detection and control workflow was evaluated targeting Palmer amaranth in soybean (Glycine max) fields. High spatial resolution (0.4 cm) unmanned aircraft system (UAS) image streams were collected, annotated, and used to train 16 object detection convolutional neural networks (CNNs; RetinaNet, Faster R-CNN, Single Shot Detector, and YOLO v3) each trained on imagery with 0.4, 0.6, 0.8, and 1.2 cm spatial resolutions. Models were evaluated on imagery from four production fields containing approximately 7,800 weeds. The highest performing model was Faster R-CNN trained on 0.4 cm imagery (precision = 0.86, recall = 0.98, and F1-score = 0.91). A site-specific workflow leveraging the highest performing trained CNN models was evaluated in replicated field trials. Weed control (%) was compared between a broadcast treatment and the proposed site-specific workflow which was applied using a pulse-width modulated (PWM) sprayer. Results indicate no statistical (p \u3c .05) difference in weed control measured one (M = 96.22%, SD = 3.90 and M = 90.10%, SD = 9.96), two (M = 95.15%, SD = 5.34 and M = 89.64%, SD = 8.58), and three weeks (M = 88.55, SD = 11.07 and M = 81.78%, SD = 13.05) after application between broadcast and site-specific treatments, respectively. Furthermore, there was a significant (p \u3c 0.05) 48% mean reduction in applied area (m2) between broadcast and site-specific treatments across both years. Equivalent post application efficacy can be achieved with significant reductions in herbicides if weeds are targeted through site-specific applications. Site-specific weed maps can be generated and executed using accessible technologies like UAS, open-source CNNs, and PWM sprayers

Precision Weed Management Based on UAS Image Streams, Machine Learning, and PWM Sprayers

Weed populations in agricultural production fields are often scattered and unevenly distributed; however, herbicides are broadcast across fields evenly. Although effective, in the case of post-emergent herbicides, exceedingly more pesticides are used than necessary. A novel weed detection and control workflow was evaluated targeting Palmer amaranth in soybean (Glycine max) fields. High spatial resolution (0.4 cm) unmanned aircraft system (UAS) image streams were collected, annotated, and used to train 16 object detection convolutional neural networks (CNNs; RetinaNet, Faster R-CNN, Single Shot Detector, and YOLO v3) each trained on imagery with 0.4, 0.6, 0.8, and 1.2 cm spatial resolutions. Models were evaluated on imagery from four production fields containing approximately 7,800 weeds. The highest performing model was Faster R-CNN trained on 0.4 cm imagery (precision = 0.86, recall = 0.98, and F1-score = 0.91). A site-specific workflow leveraging the highest performing trained CNN models was evaluated in replicated field trials. Weed control (%) was compared between a broadcast treatment and the proposed site-specific workflow which was applied using a pulse-width modulated (PWM) sprayer. Results indicate no statistical (p \u3c .05) difference in weed control measured one (M = 96.22%, SD = 3.90 and M = 90.10%, SD = 9.96), two (M = 95.15%, SD = 5.34 and M = 89.64%, SD = 8.58), and three weeks (M = 88.55, SD = 11.07 and M = 81.78%, SD = 13.05) after application between broadcast and site-specific treatments, respectively. Furthermore, there was a significant (p \u3c 0.05) 48% mean reduction in applied area (m2) between broadcast and site-specific treatments across both years. Equivalent post application efficacy can be achieved with significant reductions in herbicides if weeds are targeted through site-specific applications. Site-specific weed maps can be generated and executed using accessible technologies like UAS, open-source CNNs, and PWM sprayers

Introduction to Criminal Justice

This Grants Collection for Introduction to Criminal Justice was created under a Round Five ALG Textbook Transformation Grant. Affordable Learning Georgia Grants Collections are intended to provide faculty with the frameworks to quickly implement or revise the same materials as a Textbook Transformation Grants team, along with the aims and lessons learned from project teams during the implementation process. Documents are in .pdf format, with a separate .docx (Word) version available for download. Each collection contains the following materials: Linked Syllabus Initial Proposal Final Reporthttps://oer.galileo.usg.edu/criminal-collections/1001/thumbnail.jp

Zoonotic realism, computational cognitive science and pandemic prevention

Using animals in food and food production systems is one of many drivers of novel zoonoses. Moving toward less dependence on animal proteins is a possible avenue for reducing pandemic risk, but we think that Wiebers & Feigin’s proposed change to food policy (phasing out animal meat production) is unrealistic in its political achievability and its current capacity to feed the world in a cost-effective and sustainable manner. We suggest that improvements in communication strategies, precipitated by developments in computational cognitive neuroscience, can lead the way to a safer future and are feasible now

Zoonotic realism, computational cognitive science and pandemic prevention

Using animals in food and food production systems is one of many drivers of novel zoonoses. Moving toward less dependence on animal proteins is a possible avenue for reducing pandemic risk, but we think that Wiebers & Feigin’s proposed change to food policy (phasing out animal meat production) is unrealistic in its political achievability and its current capacity to feed the world in a cost-effective and sustainable manner. We suggest that improvements in communication strategies, precipitated by developments in computational cognitive neuroscience, can lead the way to a safer future and are feasible now

Incidental findings detected on preoperative CT imaging obtained for robotic-assisted joint replacements: clinical importance and the effect on the scheduled arthroplasty

OBJECTIVE: To determine the type and frequency of incidental findings detected on preoperative computed tomography (CT) imaging obtained for robotic-assisted joint replacements and their effect on the planned arthroplasty. MATERIALS AND METHODS: All preoperative CT examinations performed for a robotic-assisted knee or total hip arthroplasty were obtained. This resulted in 1432 examinations performed between September 2016 and February 2020 at our institution. These examinations were initially interpreted by 1 of 9 fellowship-trained musculoskeletal radiologists. Using a diagnosis search, the examination reports were then reviewed to catalog all incidental findings and further classify as significant or non-significant findings. Demographic information was obtained. In those with significant findings, a chart review was performed to record the relevant workup, outcomes, and if the planned arthroplasty was affected. RESULTS: Incidental findings were diagnosed in 740 (51.7%) patients. Of those with incidental findings, 41 (5.5%) were considered significant. A significant finding was more likely to be detected in males (P = 0.007) and on the hip protocol CT (P = 0.014). In 8 patients, these diagnoses resulted in either delay or cancelation of the arthroplasty. A planned total hip arthroplasty was more likely to be altered as compared to a knee arthroplasty (P = 0.018). CONCLUSION: Incidental findings are commonly detected by radiologists on preoperative CT imaging obtained for robotic-assisted joint replacement. Several were valuable findings and resulted in a delay or even cancelation of the planned arthroplasty after the detection of critical diagnoses, which if not identified may have resulted in devastating outcomes

The phase transition phenomena in anisotropic superconductors: effect of the orthorhombic crystal field and the potential impurity scattering

A combined effect of the orthorhombic crystal field and potential impurity scattering on several superconducting states of a tetragonal symmetry is studied within a weak-coupling mean field approach. It is shown that the nonmagnetic impurities stabilize the states belonging to the identity irreducible representation. The electronic specific heat jump at the phase transition is analyzed. Its dependence on the potential scattering rate for large impurity concentration is shown to be remarkably different for the states with a nonzero value of the Fermi surface averaged order parameter than for those with a vanishing one. In particular, very distinct signals from d_{x^2-y^2} state in YBCO and d_{xy} state in BSCCO compound are predicted. This effect may be used as a test for the presence of these states in the above cuprates.Comment: 21 pages, 2 tables, RevTex, 12 PostScript figure

Influence of Urbanicity and County Characteristics on the Association between Ozone and Asthma Emergency Department Visits in North Carolina

Background: Air pollution epidemiologic studies, often conducted in large metropolitan areas because of proximity to regulatory monitors, are limited in their ability to examine potential associations between air pollution exposures and health effects in rural locations

Shining Light on Merging Galaxies I: The Ongoing Merger of a Quasar with a `Green Valley' Galaxy

Serendipitous observations of a pair z = 0.37 interacting galaxies (one hosting a quasar) show a massive gaseous bridge of material connecting the two objects. This bridge is photoionized by the quasar (QSO) revealing gas along the entire projected 38 kpc sightline connecting the two galaxies. The emission lines that result give an unprecedented opportunity to study the merger process at this redshift. We determine the kinematics, ionization parameter (log U ~ -2.5 +- 0.03), column density (N_H ~ 10^{21} cm^{-2}), metallicity ([M/H] ~ -0.20 +- 0.15), and mass (~ 10^8 Msun) of the gaseous bridge. We simultaneously constrain properties of the QSO-host (M_DM>8.8x 10^{11} Msun) and its companion galaxy (M_DM>2.1 x 10^{11} Msun; M_star ~ 2 x 10^{10} Msun; stellar burst age=300-800 Myr; SFR~6 Msun/yr; and metallicity 12+log (O/H)= 8.64 +- 0.2). The general properties of this system match the standard paradigm of a galaxy-galaxy merger caught between first and second passage while one of the galaxies hosts an active quasar. The companion galaxy lies in the so-called `green valley', with a stellar population consistent with a recent starburst triggered during the first passage of the merger and has no detectable AGN activity. In addition to providing case-studies of quasars associated with galaxy mergers, quasar/galaxy pairs with QSO-photoionized tidal bridges such as this one offer unique insights into the galaxy properties while also distinguishing an important and inadequately understood phase of galaxy evolution.Comment: 23 pages, 12 figures, 5 tables, Submitted to ApJ, revised to address referee's comment