no code implementations • 30 Apr 2024 • Zhipeng Yuan, Nasamu Musa, Katarzyna Dybal, Matthew Back, Daniel Leybourne, Po Yang
In this paper, we survey and categorise the studies and available datasets on nematode detection through deep-learning models.
no code implementations • 18 Mar 2024 • Shanglong Yang, Zhipeng Yuan, Shunbao Li, Ruoling Peng, Kang Liu, Po Yang
In the rapidly evolving field of artificial intelligence (AI), the application of large language models (LLMs) in agriculture, particularly in pest management, remains nascent.
no code implementations • 6 Nov 2023 • Xulong Wang, Yu Zhang, Menghui Zhou, Tong Liu, Jun Qi, Po Yang
The experimental results show that compared with directly ROI based learning, our proposed method is more effective in predicting disease progression.
no code implementations • 6 Aug 2023 • Ruoling Peng, Kang Liu, Po Yang, Zhipeng Yuan, Shunbao Li
Pest identification is a crucial aspect of pest control in agriculture.
no code implementations • 7 Nov 2022 • Hongrui Shi, Valentin Radu, Po Yang
The heterogeneity of hardware and data is a well-known and studied problem in the community of Federated Learning (FL) as running under heterogeneous settings.
no code implementations • 6 Sep 2020 • Po Yang, Jun Qi, Xulong Wang, Yun Yang
The fused sparse group Lasso (FSGL) method allows the simultaneous selection of a common set of country-based factors for multiple time points of COVID-19 epidemic and also enables incorporating temporal smoothness of each factor over the whole early phase period.