使用YOLO估计户外自由放牧猪生产的植被指数
Sang-Hyon Oh1, Hee-Mun Park2, Jin-Hyun Park2
1Division of Animal Science, College of Agriculture and Life Science, Gyeongsang National University, Jinju 52725, Korea.
Journal of animal science and technology
|June 19, 2023
概括
无人驾驶飞行器 (UAV) 和你只看一次 (YOLO) 技术量化了自由放牧猪生产中的放牧损害. 结果表明,为了保护覆盖作物,在5天后从50x100米的田间旋转20头母猪.
科学领域:
- 农业技术 农业技术
- 遥感 遥感 遥感 遥感
- 计算机视觉 计算机视觉
背景情况:
- 户外放牧生猪生产需要有效的方法来监测放牧影响.
- 评估自由放牧的牲畜对覆盖作物的损害对于可持续农业至关重要.
- 农业中现有的机器学习应用主要集中在害虫和水果检测上.
研究的目的:
- 量化估计自由放牧猪生产中放牧面积损害.
- 评估无人机和深度学习对监测作物占用率的有效性.
- 确定自由放牧母猪的最佳放牧时间,以防止过度放牧.
主要方法:
- 使用带有RGB传感器的无人机在两周内捕获100x50米的玉米田的空中图像.
- 使用You Only Look Once (YOLO) 深度学习模型处理的图像用于玉米状况检测.
- 采用了广泛的数据增强技术,从原始图像生成了超过24,000个训练数据集.
主要成果:
- 通过YOLO模型,在放牧区有效估计了玉米占用率.
- 在母猪放牧的9天内,观察到玉米的显著消失.
- 计算出20头母猪在50x100米的田野上 (250m2/母猪) 需要在大约五天后轮换.
结论:
- 无人机与YOLO相结合,提供了一种有效的方法来评估农业环境中的牧场损害.
- 数据增强对于训练深度学习模型至关重要,因为专家收集的数据有限.
- 结果为管理放牧强度提供了实际建议,以确保覆盖作物的可持续性.
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