精准农业:计算机视觉支持在化阶段计数甘植物
Muhammad Talha Ubaid1, Sameena Javaid2
1Faculty of Information Technology, University of Central Punjab, Lahore P.O. Box 54000, Pakistan.
Journal of imaging
|May 24, 2024
概括
本研究引入了一套新的数据集和方法,用于估计在耕种阶段的甘植物. 开发的Faster R-CNN模型实现了82.10%的准确性,有助于工业生产规划.
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:
- 甘是全球重要的作物,对于糖,乙醇和其他工业产品至关重要.
- 准确的预收获产量估计对于甘行业规划和农民协议至关重要.
- 目前的方法在关键的耕作阶段估计植物数量时缺乏精度.
研究的目的:
- 开发和介绍一种用于估计在耕作阶段的甘植物数量的新方法.
- 为研究目的引入新的,公开可用的甘田数据集.
- 改善甘行业的收获前规划.
主要方法:
- 修改后的Faster R-CNN架构用于植物检测和分类.
- 使用VGG-16与Inception-v3模块来增强功能提取.
- 为了提高检测准确度,集成了一个西格体值函数.
主要成果:
- 拟议的方法在检测和分类甘植物方面实现了有希望的82.10%的准确性.
- 开发的数据集,在秋季期间捕获,支持进一步研究甘作物监测.
- 该模型证明了在甘田地评估中实际应用的可行性.
结论:
- 这项研究提出了一种可行的人工智能驱动的方法,用于准确估计甘植物.
- 新的数据集和模型有助于在甘生产中推进精准农业.
- 这些发现支持全球甘行业更好的生产和采购规划.
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