YOLOv7-Plum:通过深度学习在自然环境中推进梅果检测
Rong Tang1,2, Yujie Lei3, Beisiqi Luo1
1College of Information Engineering, Sichuan Agricultural University, Ya'an 625000, China.
Plants (Basel, Switzerland)
|August 12, 2023
概括
这项研究引入了一种改进的YOLOv7模型,用于在复杂的果园中准确检测梅果. 新的YOLOv7梅模型提高了检测准确度,帮助智能梅种植.
科学领域:
- 农业工程 农业工程
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 梅果检测对于在梅子行业产量估计和疾病监测至关重要.
- 复杂的果园环境,叶子阴影和水果重叠,对传统的检测方法构成重大挑战.
- 手动估计方法是低效的,昂贵的,缺乏准确性.
研究的目的:
- 为复杂的果园环境开发一种高效准确的梅果检测模型.
- 改进现有的农业应用物体检测算法.
主要方法:
- 从自然果园条件创建了一个高分辨率梅子水果图像数据集.
- 开发了一种改进的You Only Look Once第7版 (YOLOv7) 模型,称为YOLOv7-plum.
- 关键的修改包括整合卷积块注意力模块 (CBAM) 和跨阶段部分空间金字塔聚合快速 (CSPSPPF),并使用双线插值进行上采样.
主要成果:
- YOLOv7-plum模型实现了94.91%的平均精度 (AP).
- 与原来的YOLOv7模型相比,这代表了AP的2.03%的改进.
- 废弃实验和统计分析验证了模型在复杂背景中的有效性.
结论:
- 拟议的YOLOv7-梅模型在具有挑战性的果园环境中展示了在梅果检测方面卓越的性能.
- 这一进步支持了梅子行业智能种植实践的发展.
- 该模型为传统的水果计数和监测方法提供了更准确,更有效的替代方案.
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