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In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
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精确的作物害虫检测基于坐标基于注意的特征金字塔模块.

Chenrui Kang1,2, Lin Jiao2,3, Kang Liu4

  • 1School of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, China.

Insects
|January 25, 2025
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概括

深度学习模型在小昆虫害虫检测方面遇到了困难. 一个新的基于注意力的协调特征金字塔网络 (CAFPN) 改善了特征提取和样本选择,以准确识别害虫.

关键词:
协调注意力 协调注意力农作物害虫害虫害害虫害虫害害虫害害虫害害虫害害害虫害害害虫害害害虫害害虫害害虫害害虫害害害虫害虫害害虫害虫害虫害害害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害功能金字塔网络是一个特征金字塔网络.对象检测检测对象检测对象检测样本选择 选择 选择 选择小规模的害虫害虫害虫害害虫害虫害害虫害害虫害虫害虫害害害虫害虫害害害虫害虫害害害虫害虫害害害害害害害害害害害害害害害害

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科学领域:

  • 农业科学 农业科学
  • 计算机视觉 计算机视觉
  • 机器学习 机器学习

背景情况:

  • 昆虫害虫严重影响全球作物生产和经济价值.
  • 准确和快速的害虫检测对于有效的害虫防治和感染减缓至关重要.
  • 目前的深度学习方法在检测小作物害虫方面面临挑战,原因是特征提取和样本选择中的困难.

研究的目的:

  • 开发一种先进的深度学习模型,用于准确检测和识别小型作物害虫.
  • 解决现有方法在特征提取和积极/消极样本选择中用于检测小型害虫的局限性.

主要方法:

  • 设计了一个基于坐标的注意力特征金字塔网络 (CAFPN),用于增强突出视觉特征提取.
  • 在网络培训期间实施了具有正负权重函数的动态样本选择策略.
  • 在大规模数据集上评估模型:AgriPest 21和IP102.

主要成果:

  • 该CAFPN模型在基准数据集上取得了有希望的检测结果.
  • 在AgriPest 21上获得了77.2%的平均精度 (mAP) 评分,在IP102.2上获得了29.8%的平均精度.
  • 与其他现有的害虫检测模型相比,证明了卓越的性能.

结论:

  • 拟议的CAFPN模型通过改进特征提取和样本选择,有效地克服了小型害虫检测的局限性.
  • 动态样本选择策略提高了分类准确性和定位精度.
  • 结果表明,基于深度学习的作物害虫检测系统取得了重大进展.