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通过结合多个生长期数据和改进的YOLOv8建模来得出早期果产量估计
Menglin Zhai1, Juanli Jing1, Shiqing Dou1
1College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China.
Sensors (Basel, Switzerland)
|August 14, 2025
概括
一个新的轻量级YOLOv8-RL模型使用多种生长期数据准确预测果产量. 这种高效的方法提高了精准农业和可持续水果生产,具有高精度和低错误率.
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:
- 预测早期的作物产量对于精准农业和可持续水果生产至关重要.
- 精确检测水果发育阶段 (开花,绿色果实,成熟) 对于产量估计至关重要.
- 现有的YOLO模型研究往往侧重于单个成熟阶段,限制了全面的分析.
研究的目的:
- 开发一个高效和快速的作物产量估计模型,以实现可持续的水果生产.
- 提出一种新的轻量级网络,YOLOv8-RL,用于分析的多种生长期特征.
- 使用拟议的网络构建和验证果产量估计模型.
主要方法:
- 提出了利用类多种生长期数据的YOLOv8-RL网络模型.
- 通过将网络识别计数与手动田间计数相结合,构建并验证了一种果产量估计模型.
- 基于识别率,mAP@.5,FPS,推断时间和预测错误率的评估模型性能.
主要成果:
- 与YOLOv8相比,YOLOv8-RL模型的参数减少了50.7%,浮点运算减少了49.4%,模型大小只有3.2 MB.
- 花,绿色水果和色水果的平均识别率为95.6%,mAP@.5为94.6%和高FPS (123.1).
- 产量估计模型的准确性高,确定系数 (R2) 为0.91992和0.95639,预测误差率低 (绿色水果6.96%,色水果3.71%).
结论:
- YOLOv8-RL模型提供了一个轻量级和高效的解决方案,用于作物产量预测,适合嵌入式设备.
- 开发的产量估计模型提供了准确可靠的预测,性能优于传统的网络计数方法.
- 这项研究为在复杂的农业环境中预测早期水果产量提供了理论基础和技术支持.
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