一种新的人工智能方法用于用于养殖的浮标检测
Ying Bi1, Bing Xue1, Dana Briscoe2
1School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand.
Journal of the Royal Society of New Zealand
|October 23, 2024
概括
这项研究为新西兰养殖场引入了一种基于人工智能的浮标检测系统,自动化了一项劳动密集型任务. 与现有方法相比,新方法显著提高了检测准确度.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 海洋生物学 海洋生物学
背景情况:
- 新西兰 (NZ) 的贝养殖依赖于人工检查作物支浮标,这是一个劳动密集的过程.
- 人工智能 (AI) 提供了自动化这些任务的潜力,但其在养殖中的应用是有限的.
研究的目的:
- 开发和评估一种新的基于人工智能的方法,用于在新西兰养殖场图像中自动检测浮标.
- 解决水产养殖中手动浮标监测的劳动密集性问题.
主要方法:
- 一个四步人工智能方法:数据收集/预处理,使用卷积神经网络 (CNN) 的图像细分,通过一种新的遗传编程 (GP) 方法检测/提取关键点/特征,并进行分类.
- 该GP方法使用了一个新的表示,函数集和终端集,用于演变的特征描述符.
主要成果:
- 与三个基线方法相比,开发的AI方法在自动检测各种背景的浮标方面表现出卓越的性能.
- 该系统在七个子集和一个完整的贝养殖场图像数据集上进行了测试.
结论:
- 这种基于人工智能的新系统有效地自动化了养殖中的浮标检测,为手工检查提供了更有效的替代方案.
- 进一步的分析提供了对参数和演变的解决方案的见解,突出了该方法对水产养殖的潜力.
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