Applications of AI in UAVs Using In‐Flight Parameters DOI
Yogesh Beeharry, Raviduth Ramful

Published: Dec. 13, 2024

Recent developments in UAV (unmanned aerial vehicle) technology have led to its use a wide variety of applications, namely, large-scale mapping, autonomous navigation, and package delivery. The present challenges that restrict further applications UAVs are their poor flight endurance, reduced payload capacity, limited obstacle avoidance ability various flying conditions. To achieve optimum characteristics, principal parameters, which indispensable for UAVs' operation, such as airspeed, power status, navigation data, stabilization, altitude, need be specifically controlled. Besides the variations in-flight parameters accordance with specific notable changes can also occur result weather conditions wind gusts, rain, clouds, thermal formation environments topographies. In fully long-range platforms susceptible collision other airborne objects, living organisms, or man-made constructions. exploit benefits extend application our daily lives, aforementioned limitations, optimization well improvement avoidance, explored. Artificial intelligence (AI) is powerful tool several supervised, semi-supervised, unsupervised predictive models found corresponding cases realm well. Different come overlapping hardware health analytics, path planning, avoidance. order up possible help solving these challenges, data collection pertaining different scenarios required. types collected internal external environment conditions, image environment. this study, dataset positional energy has been considered. Various ML regression implemented MATLAB using built-in functions two main relationships, consumption versus speed angle. simulation results obtained when training testing relationships discussed presented chapter. Results demonstrated neural network model performs best case instantaneous angle an RMSE value 159.58, while decision tree was perform 159.80.

Language: Английский

Precision livestock farming: an overview on the application in extensive systems DOI Creative Commons
Gloria Bernabucci, Chiara Evangelista, Pedro Girotti

et al.

Italian Journal of Animal Science, Journal Year: 2025, Volume and Issue: 24(1), P. 859 - 884

Published: March 24, 2025

Language: Английский

Citations

2
Livestock Management With Unmanned Aerial Vehicles: A Review DOI Creative Commons

Mohammed Ateeq Alanezi,

Mohammad Shoaib Shahriar, Md. Bakhtiar Hasan

et al.

IEEE Access, Journal Year: 2022, Volume and Issue: 10, P. 45001 - 45028

Published: Jan. 1, 2022

The ease of use and advancements in drone technology is resulting the widespread application Unmanned Aerial Vehicles (UAVs) to diverse fields, making it a booming technology. Among UAVs' several applications, livestock agriculture one most promising, where UAVs facilitate various operations for efficient animal management. But field characterized by multiple environmental, technical, economic, strategic challenges. However, advanced technological techniques like Artificial Intelligence (AI), Internet Things (IoT), Machine Learning (ML), Deep (DL), sensors, etc., along with assurance welfare while operating UAVs, can lead adoption amongst farmers. This paper discusses management research monitor farm animals via detection, counting, tracking animals, etc. In this article, an attempt has been made elucidate different aspects broader issues around highlighting associated challenges, opportunities, prospects. work first review on subject matter all necessary information analysis, best our knowledge. Therefore, article promises provide interested researchers detailed about field, guiding future research.

Language: Английский

Citations

62
Mask R-CNN and Centroid Tracking Algorithm to Process UAV Based Thermal-RGB Video for Drylot Cattle Heat Stress Monitoring DOI Creative Commons

Keshawa M. Dadallage,

Basavaraj R. Amogi, Lav R. Khot

et al.

Drones, Journal Year: 2024, Volume and Issue: 8(9), P. 491 - 491

Published: Sept. 17, 2024

This study developed and evaluated an algorithm for processing thermal-RGB video feeds captured by unmanned aerial vehicle (UAV) to automate heat stress monitoring in cattle housed the drylots. The body surface temperature (BST) of individual cows was used as indicator stress. UAV data were collected using RGB thermal infrared imagers, respectively, at 2 6.67 cm per pixel spatial resolution Spring 2023 (dataset-1) Summer 2024 (dataset-2). Study sites two commercial drylots Washington State. custom algorithms to: (1) detect localize a Mask R-CNN-based instance segmentation model combined with centroid tracking; (2) extract BST averaging thermal-imagery pixels each segmented cows. showed higher detection accuracy images input (F1 score: 0.89) compared 0.64). extraction imaging approach required corrections alignment problems associated differences optics, field view, resolution, lens properties. Consequently, imaging-only adopted assessing real-time cow localization estimation. Operating one frame second, successfully detected 72.4% 81.65% total frames from dataset-1 (38 s) -2 (48 s), respectively. mean absolute difference between output ground truth (BSTGT) 2.1 °C 3.3 (dataset-2), demonstrating satisfactory performance. With further refinements, this could be viable tool large-scale drylot production systems.

Language: Английский

Citations

2
Pasture Research Using Aerial Photography and Photogrammetry DOI
Kalin Dimitrov, Iliyan Damyanov, Durhan Saliev

et al.

Published: Oct. 28, 2021

This article focuses on the topic of using unmanned aerial vehicles and modern software solutions systems in study pastures, photography photogrammetry.

Language: Английский

Citations

11
Auto-Encoder Learning-Based UAV Communications for Livestock Management DOI Creative Commons

Mohammed Ateeq Alanezi,

Abdullahi Mohammad, Yusuf A. Sha’aban

et al.

Drones, Journal Year: 2022, Volume and Issue: 6(10), P. 276 - 276

Published: Sept. 25, 2022

The advancement in computing and telecommunication has broadened the applications of drones beyond military surveillance to other fields, such as agriculture. Livestock farming using unmanned aerial vehicle (UAV) systems requires monitoring animals on relatively large farmland. A reliable communication system between UAVs ground control station (GCS) is necessary achieve this. This paper describes learning-based strategies techniques that enable interaction data exchange a GCS. We propose deep auto-encoder UAV design framework for end-to-end communications. Simulation results show learns joint transmitter receiver mapping functions various strategies, QPSK, 8PSK, 16PSK 16QAM, without prior knowledge.

Language: Английский

Citations

7
Effects of continuous drone herding on behavioral response and spatial distribution of grazing cattle DOI
Hiroki Anzai,

Mahiro Kumaishi

Applied Animal Behaviour Science, Journal Year: 2023, Volume and Issue: 268, P. 106089 - 106089

Published: Oct. 21, 2023

Language: Английский

Citations

2
Assessing the Impact of Clearing and Grazing on Fuel Management in a Mediterranean Oak Forest through Unmanned Aerial Vehicle Multispectral Data DOI Creative Commons
Luís Pádua, João Paulo Castro, José Castro

et al.

Drones, Journal Year: 2024, Volume and Issue: 8(8), P. 364 - 364

Published: July 31, 2024

Climate change has intensified the need for robust fire prevention strategies. Sustainable forest fuel management is crucial in mitigating occurrence and rapid spread of fires. This study assessed impact vegetation clearing and/or grazing over a three-year period herbaceous shrub parts Mediterranean oak forest. Using high-resolution multispectral data from an unmanned aerial vehicle (UAV), four flight surveys were conducted 2019 (pre- post-clearing) to 2021. These used evaluate different scenarios: combined grazing, individual application each method, control scenario that was neither cleared nor purposely grazed. The UAV allowed detailed monitoring dynamics, enabling classification into arboreal, shrubs, herbaceous, soil categories. Grazing pressure estimated through GPS collars on sheep flock. Additionally, good correlation (r = 0.91) observed between UAV-derived volume estimates field measurements. practices proved be efficient management, with grazed areas showing lower regrowth, followed by only subjected clearing. On other hand, not any these treatments presented growth.

Language: Английский

Citations

0
VIPER: Vision-Based System to Detect Potential Predators for Herding with Robots DOI

Xiao Li Yang,

Abel Carnicero,

Lídia Sánchez-González

et al.

Lecture notes in computer science, Journal Year: 2024, Volume and Issue: unknown, P. 214 - 223

Published: Oct. 8, 2024

Language: Английский

Citations

0
Applications of AI in UAVs Using In‐Flight Parameters DOI
Yogesh Beeharry, Raviduth Ramful

Published: Dec. 13, 2024

Recent developments in UAV (unmanned aerial vehicle) technology have led to its use a wide variety of applications, namely, large-scale mapping, autonomous navigation, and package delivery. The present challenges that restrict further applications UAVs are their poor flight endurance, reduced payload capacity, limited obstacle avoidance ability various flying conditions. To achieve optimum characteristics, principal parameters, which indispensable for UAVs' operation, such as airspeed, power status, navigation data, stabilization, altitude, need be specifically controlled. Besides the variations in-flight parameters accordance with specific notable changes can also occur result weather conditions wind gusts, rain, clouds, thermal formation environments topographies. In fully long-range platforms susceptible collision other airborne objects, living organisms, or man-made constructions. exploit benefits extend application our daily lives, aforementioned limitations, optimization well improvement avoidance, explored. Artificial intelligence (AI) is powerful tool several supervised, semi-supervised, unsupervised predictive models found corresponding cases realm well. Different come overlapping hardware health analytics, path planning, avoidance. order up possible help solving these challenges, data collection pertaining different scenarios required. types collected internal external environment conditions, image environment. this study, dataset positional energy has been considered. Various ML regression implemented MATLAB using built-in functions two main relationships, consumption versus speed angle. simulation results obtained when training testing relationships discussed presented chapter. Results demonstrated neural network model performs best case instantaneous angle an RMSE value 159.58, while decision tree was perform 159.80.

Language: Английский

Citations

0