用无人机和视觉技术实现8种黄瓜病害的早期精准识别
Advancing Cucumber Disease Detection in Agriculture through Machine Vision and Drone Technology
- 基于真实田间采集的高光谱图像，构建多样化病害数据集
- 经数据增强后模型对8类病害识别准确率达87.5%
- 无人机获取高清影像，助力高效农情监测与管理
本研究结合机器视觉与无人机技术，提出一种面向农业黄瓜病害诊断的新方法。核心是精心构建的高光谱图像数据集，采集自真实田间环境，涵盖多种病害类型，支持早期精准检测。经过充分的数据增强，模型在区分八类不同黄瓜病害时达到87.5%的准确率。利用无人机获取高分辨率影像，显著提升病害评估效果。该技术有望改善作物管理，降低人力成本，提高农业生产效率。研究实现了病害检测自动化，为更高效、可持续的农业发展迈出关键一步。
原文摘要 · Abstract (English)
This study uses machine vision and drone technologies to propose a unique method for the diagnosis of cucumber disease in agriculture. The backbone of this research is a painstakingly curated dataset of hyperspectral photographs acquired under genuine field conditions. Unlike earlier datasets, this study included a wide variety of illness types, allowing for precise early-stage detection. The model achieves an excellent 87.5\% accuracy in distinguishing eight unique cucumber illnesses after considerable data augmentation. The incorporation of drone technology for high-resolution images improves disease evaluation. This development has enormous potential for improving crop management, lowering labor costs, and increasing agricultural productivity. This research, which automates disease detection, represents a significant step toward a more efficient and sustainable agricultural future.
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