Zobrazeno 1 - 10
of 26
pro vyhledávání: '"Nejati, Mahla"'
Amidst growing food production demands, early plant disease detection is essential to safeguard crops; this study proposes a visual machine learning approach for plant disease detection, harnessing RGB and NIR data collected in real-world conditions
Externí odkaz:
http://arxiv.org/abs/2402.07895
The use of synthetic data in machine learning saves a significant amount of time when implementing an effective object detector. However, there is limited research in this domain. This study aims to improve upon previously applied implementations in
Externí odkaz:
http://arxiv.org/abs/2402.07098
Humanoid robots are designed to be relatable to humans for applications such as customer support and helpdesk services. However, many such systems, including Softbank's Pepper, fall short because they fail to communicate effectively with humans. The
Externí odkaz:
http://arxiv.org/abs/2402.07095
Autor:
Qureshi, Ans, Smith, David, Gee, Trevor, Nejati, Mahla, Shahabi, Jalil, Lim, JongYoon, Ahn, Ho Seok, McGuinness, Ben, Downes, Catherine, Jangali, Rahul, Black, Kale, Lim, Hin, Duke, Mike, MacDonald, Bruce, Williams, Henry
Aotearoa New Zealand has a strong and growing apple industry but struggles to access workers to complete skilled, seasonal tasks such as thinning. To ensure effective thinning and make informed decisions on a per-tree basis, it is crucial to accurate
Externí odkaz:
http://arxiv.org/abs/2308.07512
Autor:
Kweon, Andy, Hu, Vishnu, Lim, Jong Yoon, Gee, Trevor, Liu, Edmond, Williams, Henry, MacDonald, Bruce A., Nejati, Mahla, Sa, Inkyu, Ahn, Ho Seok
As technology progresses, smart automated systems will serve an increasingly important role in the agricultural industry. Current existing vision systems for yield estimation face difficulties in occlusion and scalability as they utilize a camera sys
Externí odkaz:
http://arxiv.org/abs/2304.06177
Autor:
Xing, Yuning, Pham, Dexter, Williams, Henry, Smith, David, Ahn, Ho Seok, Lim, JongYoon, MacDonald, Bruce A., Nejati, Mahla
Publikováno v:
Proceedings of the Australasian conference on robotics and automation (ACRA 2022)
Smart farming is a growing field as technology advances. Plant characteristics are crucial indicators for monitoring plant growth. Research has been done to estimate characteristics like leaf area index, leaf disease, and plant height. However, few m
Externí odkaz:
http://arxiv.org/abs/2304.03610
Publikováno v:
Proceedings of the Australasian conference on robotics and automation (ACRA 2022)
This research sets out to assess the viability of using game engines to generate synthetic training data for machine learning in the context of pallet segmentation. Using synthetic data has been proven in prior research to be a viable means of traini
Externí odkaz:
http://arxiv.org/abs/2304.03602
Autor:
Qureshi, Ans, Loh, Neville, Kwon, Young Min, Smith, David, Gee, Trevor, Bachelor, Oliver, McCulloch, Josh, Nejati, Mahla, Lim, JongYoon, Green, Richard, Ahn, Ho Seok, MacDonald, Bruce, Williams, Henry
Following a global trend, the lack of reliable access to skilled labour is causing critical issues for the effective management of apple orchards. One of the primary challenges is maintaining skilled human operators capable of making precise fruitlet
Externí odkaz:
http://arxiv.org/abs/2302.09716
Autor:
Nejati, Mahla, Penhall, Nicky, Williams, Henry, Bell, Jamie, Lim, JongYoon, Ahn, Ho Seok, MacDonald, Bruce
Accurate and reliable kiwifruit detection is one of the biggest challenges in developing a selective fruit harvesting robot. The vision system of an orchard robot faces difficulties such as dynamic lighting conditions and fruit occlusions. This paper
Externí odkaz:
http://arxiv.org/abs/2006.11729
This paper describes steps taken to develop a sensing module for a robotic kiwifruit flower pollinator, which could be integrated into an imaging module and a spray module. The paper described different indicators to present the performance of the se
Externí odkaz:
http://arxiv.org/abs/2006.08045