scholarly journals An Evaluation of the Factors Affecting ‘Poacher’ Detection with Drones and the Efficacy of Machine-Learning for Detection

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4074
Author(s):  
Katie E. Doull ◽  
Carl Chalmers ◽  
Paul Fergus ◽  
Steve Longmore ◽  
Alex K. Piel ◽  
...  
Keyword(s):  
Machine Learning ◽  
Detection Probability ◽  
Visible Spectrum ◽  
Time Of Day ◽  
Automated Detection ◽  
Probability Of Detection ◽  
Factors Affecting ◽  
Camera Angle ◽  
Manual Analysis ◽  
Stationary Test

Drones are being increasingly used in conservation to tackle the illegal poaching of animals. An important aspect of using drones for this purpose is establishing the technological and the environmental factors that increase the chances of success when detecting poachers. Recent studies focused on investigating these factors, and this research builds upon this as well as exploring the efficacy of machine-learning for automated detection. In an experimental setting with voluntary test subjects, various factors were tested for their effect on detection probability: camera type (visible spectrum, RGB, and thermal infrared, TIR), time of day, camera angle, canopy density, and walking/stationary test subjects. The drone footage was analysed both manually by volunteers and through automated detection software. A generalised linear model with a logit link function was used to statistically analyse the data for both types of analysis. The findings concluded that using a TIR camera improved detection probability, particularly at dawn and with a 90° camera angle. An oblique angle was more effective during RGB flights, and walking/stationary test subjects did not influence detection with both cameras. Probability of detection decreased with increasing vegetation cover. Machine-learning software had a successful detection probability of 0.558, however, it produced nearly five times more false positives than manual analysis. Manual analysis, however, produced 2.5 times more false negatives than automated detection. Despite manual analysis producing more true positive detections than automated detection in this study, the automated software gives promising, successful results, and the advantages of automated methods over manual analysis make it a promising tool with the potential to be successfully incorporated into anti-poaching strategies.

Factors affecting detection probability, effective area surveyed, and species misidentification in grassland bird point counts

The Condor ◽  
2019 ◽  
Vol 121 (3) ◽  
Author(s):  
Elizabeth A Rigby ◽  
Douglas H Johnson
Keyword(s):  
Wind Speed ◽  
Detection Probability ◽  
Effective Area ◽  
Probability Of Detection ◽  
Bird Songs ◽  
Grassland Bird ◽  
Point Count ◽  
Factors Affecting ◽  
Logistic Regression Models ◽  
Point Counts

ABSTRACT We simulated bird surveys using recorded bird songs to assess factors affecting detection probability in grassland bird point counts. We used mixed effects logistic regression models to estimate effects of those factors and to estimate and visualize the variation in the area around the observer where birds can be perceived (the perception area). We simulated surveys with 8,926 binary opportunities for detection in Minnesota grasslands in 2011 and 2012. Species, distance to the observer, wind speed and direction, observer, and density of vegetation all affected detection of recorded bird songs. Species had a strong effect; the size of the predicted perception area around the observer differed by an order of magnitude among species. Wind also had a strong effect on detection. As wind speed increased, probability of detection downwind of the observer was reduced and the perception area around the observer became smaller and more asymmetrical. The effective distance at which an observer is more likely to detect a bird than not detect it may differ among species and angles to the wind, even within the same survey. Eight of 10 species had low probability of misidentification (≤0.03), but Grasshopper Sparrow (Ammodramus savannarum) and LeConte’s Sparrow (Ammospiza leconteii) were frequently misidentified (probability = 0.09–0.24 among observers), contributing to a low rate of correct detection for those species. We recommend collecting point-count data within distance bands so that data can be analyzed based on the effective radius for each species and standardizing surveys across wind conditions to reduce variation in detection probability.

scholarly journals A Removal Model for Estimating Detection Probabilities From Point-Count Surveys

The Auk ◽  
2002 ◽  
Vol 119 (2) ◽  
pp. 414-425 ◽  
Author(s):  
George L. Farnsworth ◽  
Kenneth H. Pollock ◽  
James D. Nichols ◽  
Theodore R. Simons ◽  
James E. Hines ◽  
...  
Keyword(s):  
Detection Probability ◽  
Time Of Day ◽  
Probability Of Detection ◽  
Point Count ◽  
Great Smoky Mountains ◽  
Point Counts ◽  
Detection Probabilities ◽  
Acadian Flycatcher ◽  
Removal Model ◽  
Count Surveys

AbstractUse of point-count surveys is a popular method for collecting data on abundance and distribution of birds. However, analyses of such data often ignore potential differences in detection probability. We adapted a removal model to directly estimate detection probability during point-count surveys. The model assumes that singing frequency is a major factor influencing probability of detection when birds are surveyed using point counts. This may be appropriate for surveys in which most detections are by sound. The model requires counts to be divided into several time intervals. Point counts are often conducted for 10 min, where the number of birds recorded is divided into those first observed in the first 3 min, the subsequent 2 min, and the last 5 min. We developed a maximum-likelihood estimator for the detectability of birds recorded during counts divided into those intervals. This technique can easily be adapted to point counts divided into intervals of any length. We applied this method to unlimited-radius counts conducted in Great Smoky Mountains National Park. We used model selection criteria to identify whether detection probabilities varied among species, throughout the morning, throughout the season, and among different observers. We found differences in detection probability among species. Species that sing frequently such as Winter Wren (Troglodytes troglodytes) and Acadian Flycatcher (Empidonax virescens) had high detection probabilities (∼90%) and species that call infrequently such as Pileated Woodpecker (Dryocopus pileatus) had low detection probability (36%). We also found detection probabilities varied with the time of day for some species (e.g. thrushes) and between observers for other species. We used the same approach to estimate detection probability and density for a subset of the observations with limited-radius point counts.

Machine Learning in Agriculture

2018 ◽  
Vol 8 (6) ◽  
pp. 26 ◽  
Author(s):  
Matthew N. O. Sadiku ◽  
Chandra M. M Kotteti ◽  
Sarhan M. Musa
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Disease Diagnosis ◽  
Automated Detection ◽  
Disease Prediction ◽  
Paper Briefly ◽  
Agriculture Sector ◽  
Modern Agriculture ◽  
Crop Disease

Machine learning is an emerging field of artificial intelligence which can be applied to the agriculture sector. It refers to the automated detection of meaningful patterns in a given data.  Modern agriculture seeks ways to conserve water, use nutrients and energy more efficiently, and adapt to climate change.  Machine learning in agriculture allows for more accurate disease diagnosis and crop disease prediction. This paper briefly introduces what machine learning can do in the agriculture sector.

scholarly journals Population estimates of Antillean manatees in Puerto Rico: an analytical framework for aerial surveys using multi-pass removal sampling

2019 ◽  
Vol 100 (4) ◽  
pp. 1340-1349
Author(s):  
Jaime A Collazo ◽  
Matthew J Krachey ◽  
Kenneth H Pollock ◽  
Francisco J Pérez-Aguilo ◽  
Jan P Zegarra ◽  
...  
Keyword(s):  
Puerto Rico ◽  
Detection Probability ◽  
Analytical Framework ◽  
Population Estimates ◽  
High Concentration ◽  
Imperfect Detection ◽  
Factors Affecting ◽  
Management Actions ◽  
Aerial Surveys ◽  
Population Size Estimates

AbstractEffective management of the threatened Antillean manatee (Trichechus manatus manatus) in Puerto Rico requires reliable estimates of population size. Estimates are needed to assess population responses to management actions, and whether recovery objectives have been met. Aerial surveys have been conducted since 1976, but none adjusted for imperfect detection. We summarize surveys since 1976, report on current distribution, and provide population estimates after accounting for apparent detection probability for surveys between June 2010 and March 2014. Estimates in areas of high concentration (hotspots) averaged 317 ± 101, three times higher than unadjusted counts (104 ± 0.56). Adjusted estimates in three areas outside hotspots also differed markedly from counts (75 ± 9.89 versus 19.5 ± 3.5). Average minimum island-wide estimate was 386 ± 89, similar to the maximum estimate of 360 suggested in 2005, but fewer than the 700 recently suggested by the Puerto Rico Manatee Conservation Center. Manatees were more widespread than previously understood. Improving estimates, locally or island-wide, will require stratifying the island differently and greater knowledge about factors affecting detection probability. Sharing our protocol with partners in nearby islands (e.g., Cuba, Jamaica, Hispaniola), whose populations share genetic make-up, would contribute to enhanced regional conservation through better population estimates and tracking range expansion.El manejo efectivo del manatí antillano amenazado en Puerto Rico requiere estimados de tamaños de poblaciónes confiables. Dichas estimaciones poblacionales son necesarias para evaluar las respuestas a las acciones de manejo, y para determinar si los objetivos de recuperación han sido alcanzados. Se han realizado censos aéreos desde 1976, pero ninguno de ellos han sido ajustados para detecciones imperfectas. Aquí resumimos los censos desde 1976, actualizamos la distribución, y reportamos los primeros estimados poblacionales ajustados para la probabilidad de detección aparente en los censos de Junio 2010 a Marzo 2014. Las estimaciones poblacionales en áreas de mayor concentración del manatí promedió 317 ± 103, tres veces más abundante que los conteos sin ajuste (104 ± 0.56). Las estimaciones poblacionales en tres áreas fuera de las áreas de mayor concentración del manatí también fueron marcadamente diferentes (75 ± 9.89 vs 19.5 ± 3.5). El estimado mínimo poblacional en la isla entera fue de 386 ± 89, similar al estimado máximo de 360 sugerido en el año 2005, pero menor a los 700 sugeridos recientemente por el Centro de Conservación de Manatíes de Puerto Rico. Documentamos que el manatí tiene una distribución más amplia de lo que se sabía con anterioridad. El mejoramiento de los estimados poblacionales locales o a nivel de isla requerirá que se estratifique a la isla en forma diferente y que se investiguen los factores que influencian a la probabilidad de detección. Compartir protocolos como este con colaboradores de islas vecinas (por. ej., Cuba, Jamaica, Española), cuyas poblaciones de manatíes comparten material genético, contribuiría a la conservación regional mediante mejores estimaciones poblacionales y monitoreo de la expansión de su ámbito doméstico.

scholarly journals Analysis of Low Probability of Detection Capability for Chaotic Standard Map-Based FH-OFDMA System

2021 ◽  
Vol 11 (5) ◽  
pp. 2198
Author(s):  
Junwoo Jung ◽  
Jaesung Lim ◽  
Sungyeol Park ◽  
Haengik Kang ◽  
Seungbok Kwon
Keyword(s):  
Detection Probability ◽  
Probability Of Detection ◽  
Lower Probability ◽  
Symbol Sequence ◽  
Ofdma System ◽  
Standard Map ◽  
Detection Probabilities ◽  
Low Probability ◽  
Transmitted Symbol ◽  
Basic Symbol

A frequency hopping orthogonal frequency division multiple access (FH-OFDMA) can provide low probability of detection (LPD) and anti-jamming capabilities to users against adversary detectors. To obtain an extreme LPD capability that cannot be provided by the basic symbol-by-symbol (SBS)-based FH pattern, we proposed two FH patterns, namely chaotic standard map (CSM) and cat map for FH-OFDMA systems. In our previous work, through analysis of complexity to regenerate the transmitted symbol sequence, at the point of adversary detectors, we found that the CSM had a lower probability of intercept than the cat map and SBS. It is possible when a detector already knows symbol and frame structures, and the detector has been synchronized to the FH-OFDMA system. Unlike the previous work, here, we analyze whether the CSM provides greater LPD capability than the cat map and SBS by detection probability using spectrum sensing technique. We analyze the detection probability of the CSM and provide detection probabilities of the cat map and SBS compared to the CSM. Based on our analysis of the detection probability and numerical results, it is evident that the CSM provides greater LPD capability than both the cat map and SBS-based FH-OFDMA systems.

What Are the Factors Affecting Total Sleep Time During Video Polysomnography in Infants?

2020 ◽  
Author(s):  
Grace R. Paul ◽  
Don Hayes ◽  
Dmitry Tumin ◽  
Ish Gulati ◽  
Sudarshan Jadcherla ◽  
...  
Keyword(s):  
Total Sleep Time ◽  
Sleep Time ◽  
Time Of Day ◽  
Apnea Hypopnea Index ◽  
Clinical Factors ◽  
Initial Management ◽  
Factors Affecting ◽  
Modifiable Factors ◽  
Key Points ◽  
Medical Complexity

Objective The aim of the study is to investigate factors affecting total sleep time (TST) during infant polysomnography (PSG) and assess if <4 hours of TST is sufficient for accurate interpretation. Study Design Overall, 242 PSGs performed in 194 infants <6 months of chronological age between March 2013 and December 2015 were reviewed to identify factors that affect TST, including age of infant, location and timing of study, presence of medical complexity, and presence of nasal tubes. A continuum of apnea-hypopnea index (AHI) in relation to TST was reviewed. Data were examined in infants who had TST <4 hours and low AHI. Results Greater TST (p < 0.001) was noted among infants during nocturnal PSGs, at older chronological and post-menstrual ages, and without medical complexity. The presence of nasogastric/impedance probes reduced TST (p = 0.002). Elevated AHIs were identified even in PSGs with TST <4 hours. Short TST may have affected interpretation and delayed initial management in one infant without any inadvertent complications. Conclusion Clinical factors such as PMA and medical complexity, and potentially modifiable factors such as time of day and location of study appeared to affect TST during infant PSGs. TST < 4 hours can be sufficient to identify high AHI allowing physician interpretation. Key Points

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