在番茄种植中整合人工智能检测和语言模型进行实时害虫管理
Yavuz Selim Şahin1, Nimet Sema Gençer1, Hasan Şahin2
1Bursa Uludağ University, Faculty of Agriculture, Department of Plant Protection, Bursa, Türkiye.
Frontiers in plant science
|March 10, 2025
概括
这项研究引入了一个结合YOLOv8以准确检测番茄害虫和ChatGPT-4以获得可操作的管理见解的人工智能系统. 这项技术旨在提高作物产量,减少农民对农药的依赖.
科学领域:
- 农业科学 农业科学
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 番茄种植在全球范围内至关重要,但受到像Tuta absoluta这样的害虫的威胁,导致产量下降和农药使用增加.
- 传统的害虫检测方法是低效的,劳动密集的,容易出错,需要先进的解决方案来有效地管理作物.
研究的目的:
- 通过整合基于人工智能的检测和语言模型,增强番茄种植中的害虫检测.
- 为害虫管理提供实时,可操作的见解,即使是未经培训的生产者也可以访问.
主要方法:
- 利用YOLOv8进行对象检测和对番茄作物农业害虫的细分.
- 在对虫害图像和植物损害的数据集上训练了YOLOv8模型.
- 集成的ChatGPT-4为检测到的害虫提供详细的解释和管理建议.
主要成果:
- 在检测 (精度98.91%,回调98.98%) 和细分 (精度97.47%,回调98.81%) 方面,YOLOv8实现了高性能.
- 综合系统提供了准确的虫害识别和专家级管理建议.
- 与传统的害虫检测和管理策略相比,显著改进.
结论:
- 人工智能检测 (YOLOv8) 和语言模型 (ChatGPT-4) 的结合提供了一个强大的工具,可以彻底改变农业害虫管理.
- 这种方法可以民主化对专家知识的获取,促进可持续和知情的农业实践.
- 未来的工作应该集中在特定领域的数据培训和解决计算局限性以实现更广泛的采用.
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