基于混合视觉的GNN,对咖啡植物中的害虫病进行早期检测和保护
Suresh Maruthai1, Raveena Selvanarayanan2, Tamilvizhi Thanarajan3
1Department of Electronics and Communication Engineering, St. Joseph's College of Engineering, Chennai, 600 119, India.
Scientific reports
|April 6, 2025
概括
一个新的混合视觉图神经网络 (HV-GNN) 模型使用图像分析准确地检测咖啡害虫. 这种自动化病虫检测的进步可以保护咖啡农场,提高农业生产率.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 咖啡生产对全球经济至关重要,但容易受到害虫造成的重大作物损失.
- 传统的害虫检测方法耗时,需要专门的专业知识.
- 使用计算机视觉和深度学习的自动检测提供了一个有希望的解决方案.
研究的目的:
- 开发和评估一种新的混合视觉图神经网络 (HV-GNN),用于在咖啡种植园中早期检测害虫.
- 提高识别咖啡作物害虫的准确性和效率.
- 为农业中的积极害虫管理提供一个工具.
主要方法:
- 一个混合视觉图神经网络 (HV-GNN) 模型被设计用于害虫检测.
- 该模型在2850张标有各种昆虫侵袭的咖啡植物图像的数据集上进行了训练和验证.
- HV-GNN分析了视觉害虫特征及其在图像中的相互关系.
主要成果:
- 该HV-GNN模型实现了93.6625%的高检测精度.
- 这种准确性超过了现有的主要害虫检测模型.
- 该模型表现出识别单个害虫及其复杂相互作用的能力.
结论:
- HV-GNN为自动检测咖啡害虫提供了一种可行且高度准确的方法.
- 这项技术使积极的害虫防治策略成为可能,保护咖啡农场.
- 这些发现支持人工智能的实际实施,以提高农业产量和可持续性.
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