About this Research Topic

Background

The agricultural landscape, particularly the tea industry, is experiencing significant technological advancements in pest detection and tea sprout identification. The increasing demand for high-quality tea, coupled with improvements in people's quality of life, has heightened the necessity for precise and efficient detection methods for tea sprouts and pests. Conventional manual and mechanical picking methods are proving to be inefficient and costly, leading to greater post-harvest processing burdens and challenges in screening premium tea leaves. Furthermore, current detection methods face accuracy and speed issues due to the complex background and small size of tea buds, which also pose similar challenges in pest detection within intricate environments.

This Research Topic aims to address these critical challenges by exploring the integration of advanced machine learning, image processing, and object detection techniques to improve the detection of tea sprouts, buds, and pests. By leveraging sophisticated models such as YOLO, the goal is to enhance detection accuracy and speed, minimize false and missed detections, and optimize model efficiency. Researchers are focusing on innovative approaches to feature extraction, reducing model parameters, and lowering computational requirements. This Research Topic Topic aims to establish a robust theoretical foundation and provide technical support for more efficient tea picking and pest detection processes, with the potential to transform the tea industry and agricultural technology. Contributions that push the boundaries of agricultural technologies are welcomed in this Research Topic, offering the promise of revolutionizing the tea industry and beyond.

To gather further insights into the field of agricultural technology, we welcome articles addressing, but not limited to, the following themes:

• The use of YOLO technology in agricultural detection models

• AI-Powered Edge Computing for Real-Time Agricultural Applications

• The integration of attention mechanisms and feature extraction modules in detection models

• The challenges and solutions in tea sprout and pest detection

• The impact of improved detection models on the tea industry and pest control

• The Role of Artificial Intelligence in Sustainable Agriculture

• Theoretical research and technical support for subsequent tea picking and pest detection

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Data Report
• Editorial
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Data Report
• Editorial
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Review
• Systematic Review
• Technology and Code

Keywords: machine learning, image processing, YOLO technology, tea sprout, pest detection, sustainable agriculture

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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