Yujie Xue is a business reporter at South China Morning Post. She focuses on energy transition, climate change, and sustainability issues, while also covering themes related to agriculture, automotive, and electric vehicles. Yujie's work has been featured in ABS-CBN Corporation, Newswav, The Star Online, MSN, Nature Portfolio, MDPI, and The Diplomat.
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Yujie Xue's coverage predominantly focuses on business, energy, and the environment, with a specific emphasis on China and Hong Kong. Yujie appears to prioritize articles that discuss initiatives related to renewable energy, climate change, electric vehicles, and government announcements in these areas.
Given the focus on government announcements and industry-specific data cited in her articles, Yujie may be receptive to pitches from experts who can offer insights into policy changes or new regulations impacting the fields of renewable energy, electric vehicles, sustainability efforts by companies in China and Hong Kong.
Additionally,Yujie seems particularly interested in pieces about innovations within these industries such as eco-friendly initiatives or advancements in technology for sustainable practices. Therefore reaching out with unique stories about similar innovative ideas would likely appeal to her interests.
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By: Yujie Xue
Sales in China jumped 28 per cent, while Europe's demand rose 23 per cent to 390,000 units last month, according to Rho Motion
By: Yujie Xue
Tesla teases the launch of the longer wheelbase Model Y L, due this autumn, in a social media post Yujie Xue in Hong Kong and
By: Yujie Xue
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By: Yujie Xue
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By: Yujie Xue
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By: Yujie Xue
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By: Yujie Xue
The Atto 2 is priced at HK$169,800 after tax, while the cheapest Tesla Model 3 ‘rear-wheel drive' starts at HK$249,000 before tax
By: Yujie Xue
The Atto 2 is priced at HK$169,800 after tax, while the cheapest Tesla Model 3 ‘rear-wheel drive’ starts at HK$249,000 before tax.
By: Yujie Xue, Tingkun Chen, Junyi Li
Confronted with the severe imperatives to food security posed by a growing population and the urgent need for sustainable development amid climate change, traditional agricultural models face significant resource-intensive efficiency bottlenecks. Deep learning-based image recognition is driving a future-oriented intelligent agricultural revolution by enabling high-throughput phenotyping and autonomous decision-making across the production chain. This paper systematically reviews key advancements in image recognition within modern agriculture, mapping the fundamental paradigm shift from traditional hand-crafted feature engineering to adaptive deep feature learning. We critically analyze technological implementation and performance across five core application scenarios: high-precision pest and disease diagnosis, spatio-temporal growth monitoring and yield prediction through multi-source image fusion, agricultural robots for automated harvesting, non-destructive quality inspection of products, and intelligent precision management of farmland. The review further identifies critical challenges hindering large-scale technology adoption, primarily centered on the high costs of constructing high-quality agricultural datasets and model robustness in complex field environments. Consequently, this study provides a comprehensive and forward-looking reference for advancing the deep integration of vision technology, thereby offering a strategic path toward achieving more intelligent, efficient, and sustainable global agricultural production systems in the digital era.
By: Yujie Xue
If established brands do not pivot to electric vehicles immediately, they risk losing market share to a new generation of competitors across the region.
