no code implementations • 12 Feb 2024 • Violet Liu, Jason Chen, Ans Qureshi, Mahla Nejati
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 through a JAI FS-1600D-10GE camera to build an RGBN dataset.
no code implementations • 11 Feb 2024 • Henry Gann, Josiah Bull, Trevor Gee, Mahla Nejati
This study achieved performance improvements on the stacked and racked pallet categories by 69% and 50% mAP50, respectively when being evaluated on real data.
no code implementations • 12 Apr 2023 • Andy Kweon, Vishnu Hu, Jong Yoon Lim, Trevor Gee, Edmond Liu, Henry Williams, Bruce A. MacDonald, Mahla Nejati, Inkyu Sa, Ho Seok Ahn
As technology progresses, smart automated systems will serve an increasingly important role in the agricultural industry.
no code implementations • 7 Apr 2023 • Yuning Xing, Dexter Pham, Henry Williams, David Smith, Ho Seok Ahn, JongYoon Lim, Bruce A. MacDonald, Mahla Nejati
The overall measurement system (leaf detection and size estimation algorithms combine) delivers an RMSE value of 8. 13mm and an R^2 value of 0. 899.
no code implementations • 7 Apr 2023 • Jouveer Naidoo, Nicholas Bates, Trevor Gee, Mahla Nejati
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.
no code implementations • 20 Feb 2023 • Ans Qureshi, Neville Loh, Young Min Kwon, David Smith, Trevor Gee, Oliver Bachelor, Josh McCulloch, Mahla Nejati, JongYoon Lim, Richard Green, Ho Seok Ahn, Bruce MacDonald, Henry Williams
Following a global trend, the lack of reliable access to skilled labour is causing critical issues for the effective management of apple orchards.
no code implementations • 21 Jun 2020 • Mahla Nejati, Nicky Penhall, Henry Williams, Jamie Bell, JongYoon Lim, Ho Seok Ahn, Bruce MacDonald
Alone the semantic segmentation approach achieves an F1_score of 0. 82 on the typical lighting image set, but struggles with harsh lighting with an F1_score of 0. 13.
no code implementations • 8 Jun 2020 • JongYoon Lim, Ho Seok Ahn, Mahla Nejati, Jamie Bell, Henry Williams, Bruce A. MacDonald
In this paper, we present a novel approach to kiwi fruit flower detection using Deep Neural Networks (DNNs) to build an accurate, fast, and robust autonomous pollination robot system.