植物信息系统 (PlantInfoCMS):可扩展的植物疾病信息收集和管理系统,用于训练人工智能模型
Dong Jin1,2, Helin Yin3, Ri Zheng1,2
1Department of Computer Science and Engineering, Sejong University, Seoul 05006, Republic of Korea.
Sensors (Basel, Switzerland)
|June 10, 2023
概括
一个新的系统,PlantInfoCMS,有效地收集和管理高质量的植物疾病图像,以进行深度学习. 这通过改善作物害虫和疾病诊断来支持智能农业.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 数据管理数据管理
背景情况:
- 深度学习增强了智能和精准农业,但需要大量高质量的数据.
- 收集和管理大型,质量可靠的农业人工智能数据集是一个重大挑战.
研究的目的:
- 提出一个可扩展的植物疾病信息收集和管理系统 (PlantInfoCMS).
- 为解决获取和管理农业深度学习模型的高质量图像数据的关键问题.
主要方法:
- 开发了带有数据收集,注释,检查和仪表板报告的模块的PlantInfoCMS.
- 实施了统计功能,以有效地监控任务进展.
- 该系统目前支持32种作物类型和185种害虫/疾病类型.
主要成果:
- 成功管理了301,667个原始和195,124个标记图像.
- 确保准确和高质量的害虫和疾病图像数据集用于AI模型培训.
- 证明了高效的管理和进度跟踪能力.
结论:
- 植物信息系统 (PlantInfoCMS) 为农业图像数据管理提供了一个强大的解决方案.
- 该系统预计将通过改进人工智能培训数据,显著提升作物害虫和疾病诊断.
- 促进作物健康问题的更好整体管理.
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