IntroductionThe ecological and economic impacts of forest pests have intensified, particularly in remote areas. Traditional pest detection methods are often inefficient and inaccurate in complex environments, posing significant challenges for effective pest management. Enhancing the efficiency and accuracy of pest detection under resource-limited conditions has thus become a critical issue. This study aims to address these challenges by proposing an improved lightweight forestry pest detection algorithm, RSD-YOLOv8, based on YOLOv8.MethodsTo improve the performance of pest detection, we introduced several modifications to the YOLOv8 architecture. First, we proposed RepLightConv to replace conventional convolution in HGNetV2, forming the Rep-HGNetV2 backbone, which significantly reduces the number of model parameters. Additionally, the neck of the model was enhanced by integrating a slim-neck structure and adding a Dyhead module before the output layer. Further optimization was achieved through model pruning, which contributed to additional lightweighting of the model. These improvements were designed to balance detection accuracy with computational efficiency, particularly for deployment in resource-constrained environments.ResultsThe experimental results demonstrate the effectiveness of the proposed RSD-YOLOv8 model. The model achieved a Map@0.5:0.95(%) of 88.6%, representing a 4.2% improvement over the original YOLOv8 model. Furthermore, the number of parameters was reduced by approximately 36%, the number of operations decreased by 36%, and the model size was reduced by 33%. These improvements indicate that the RSD-YOLOv8 model not only enhances detection accuracy but also significantly reduces computational burden and resource consumption.DiscussionThe lightweight technology and architectural improvements introduced in this study have proven effective in enhancing pest detection accuracy while minimizing resource requirements. The RSD-YOLOv8 model's ability to operate efficiently in remote areas with limited resources makes it highly practical for real-world applications. This advancement holds positive implications for agroforestry ecology and supports the broader goals of intelligent and sustainable development. Future work could explore further optimization techniques and the application of this model to other domains requiring lightweight and accurate detection systems.

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The global electric pruning shears market, valued at $126 million in 2025, is projected to experience steady growth, driven by several key factors. The increasing adoption of precision agriculture techniques in vineyards, orchards, and landscaping is a significant driver. Farmers and landscapers are increasingly seeking efficient and ergonomic tools to improve productivity and reduce labor costs. The rising popularity of cordless electric pruning shears, offering greater maneuverability and ease of use compared to their corded counterparts, is further fueling market expansion. Technological advancements, such as improved battery life and enhanced cutting performance, are also contributing to the market's growth. While the market faces certain restraints, such as the relatively high initial cost of electric pruning shears compared to manual tools, these are likely to be offset by long-term cost savings and productivity gains. Market segmentation reveals a strong demand across various applications, with vineyards and orchards representing significant market shares due to the large-scale use of pruning in these sectors. The cordless segment is expected to witness faster growth due to its convenience and portability. Geographically, North America and Europe are currently leading the market, driven by higher adoption rates and technological advancements. However, emerging economies in Asia-Pacific, particularly China and India, are poised for substantial growth due to increasing agricultural activities and rising disposable incomes. The forecast period (2025-2033) anticipates a continued expansion of the market, propelled by the aforementioned factors, though the exact growth trajectory will be influenced by macroeconomic conditions and technological innovation. The competitive landscape is characterized by a mix of established players and emerging manufacturers. Companies like Infaco, Pellenc, and Felco hold significant market share due to their strong brand reputation and established distribution networks. However, several Chinese manufacturers are rapidly gaining ground, offering competitive pricing and innovative product features. Future market dynamics will likely involve consolidation, strategic partnerships, and technological advancements leading to improved efficiency, battery life, and overall cutting performance in electric pruning shears. This will shape the competitive landscape and create opportunities for both established and emerging players. The market’s sustainable growth will depend on continued technological innovation, wider market penetration in emerging economies, and the ability of manufacturers to address concerns related to cost and affordability.