Results 211 to 220 of about 36,642 (249)

A Narrative Review on Internet of Things and Artificial Intelligence for Poultry Production. [PDF]

Animals (Basel)
Dhungana A, Paneru B, Dahal S, Chai L.
europepmc   +1 more source

Kisspeptin made in the preoptic area is required for normal estradiol-induced LH surges and optimal fertility in females.

Endocrinology
Puffer MS, Yang J, Esparza LA, Rose L, Duong V, Radovick S, Kauffman AS.   +6 more
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Combining Spectral Approaches and AI for Marine Litter Detection and Identification

2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021
This study presents marine litter detection using Artificial Intelligence (AI) from drone imagery next to the required spectral considerations for plastic identification in aquatic environments. An artificial accumulation zone of marine litter has been created over a lake in Mol, Belgium and different flight scenarios have been tested in order to ...
Mehrdad Moshtaghi, Els Knaeps
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Autonomous Litter Detection & Picking Drone

AIJR Proceedings
This paper suggests a drone system for automated trash discovery and collection that integrates robotic manipulation and computer vision to combat environmental declination in hard- to- reach places. The YOLOv8 object identification algorithm, which has an average perfection of 89.7 in a variety of outside circumstances, is used by the proposed system ...
Krishna N S, Athul, Baby, Nidhin, P M, Nizam, Nipun, Sravan C, M, Athira, T J, Julia   +5 more
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Detect plastic litter in Cyprus region using Sentinel-2

Ninth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2023), 2023
Marine litter is permanent, manufactured or processed solid matter that is disposed of in oceans, rivers, or beaches. Indirectly brought into the sea by rivers, sewage, rainwater and wind, or discarded or lost at sea. Marine litter poses environmental, economic, health, aesthetic and cultural threats. This includes the degradation of marine and coastal
Kalogirou, Eleftheria, Makri, Despina, Kountouri, Josefina, Stylianou, Thrasos, Themistokleous, Kyriakos, Papoutsa, Christiana, Melillos, George, Hadjimitsis, Diofantos G.   +7 more
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Machine Learning for Beach Litter Detection

2018
People from economically weaker sections find consolation in doing unskilled tasks which are easily available, though a few of them are certainly not for humans. They have to be replaced with robots since these jobs fall into the categories of dull, dirty, difficult, and dangerous jobs.
Sridhar Thiagarajan, G. Satheesh Kumar
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Camera distance effect for smart detection littering

AIP Conference Proceedings, 2020
The behavior of littering is mostly done by Indonesian people. Community indifferently throws out garbage not in the place provided. Seen, in public area, there is a lot of garbage scattered out. This makes the environment dirty, uncomfortable and unhealthy. There needs to be an effort to change the bad habits of this society.
Dedy Ariyanto, Yusuf Ari Bahtiar, Muhammad Taufik, Trie Handayani   +3 more
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Efficient Deep Learning Models for Litter Detection in the Wild

2024 IEEE 8th Forum on Research and Technologies for Society and Industry Innovation (RTSI)
Littering presents a substantial environmental hazard and impacts our well-being. The importance of automatic litter detection lies in its ability to identify waste in the environment, thereby enhancing the efficiency of subsequent waste management operations.
Bianco, Simone, Gaviraghi, Elia, Schettini, Raimondo   +2 more
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Marine plastic litter detection offshore Hawai'i by Sentinel-2

Marine Pollution Bulletin, 2021
Marine litter patches were detected from Sentinel-2 offshore Hawaii's Big Island (Hawaii) within 10 miles from the coast in the prevalent windward direction (NE), for a total sea surface of 3.0 km2. The patches have a filament-like shape with different orientation, lengths of several kilometers and width from tens to hundreds of meters.
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Litter survey detects the South Atlantic ‘garbage patch’

Marine Pollution Bulletin, 2014
A distance-based technique was used to assess the distribution and abundance of floating marine debris (>1cm) in the southeast Atlantic Ocean between Cape Town and Tristan da Cunha, crossing the southern edge of the South Atlantic 'garbage patch' predicted by surface drift models. Most litter was made of plastic (97%).
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