用无人机影像与机器学习自动估算葡萄叶面积指数。
Automating grapevine LAI features estimation with UAV imagery and machine learning
- 融合无人机图像与机器学习，自动提取叶面积特征。
- 深度学习特征提取效果优于传统方法，预测更准确。
- 适合精准农业从业者快速评估作物生长状况。
叶面积指数（LAI）是衡量作物健康与生长的关键指标。传统测算方法耗时、破坏性强、成本高且尺度有限。本研究利用葡萄藤无人机影像数据，结合机器学习模型，通过传统特征提取与深度学习方法从数据中获取有效信息，提升不同机器学习模型对叶面积指数的预测性能。结果表明，基于深度学习的特征提取方法显著优于传统方法。新方法大幅改进了旧有方式，实现了快速、非破坏性、低成本的叶面积指数计算，有助于推动精准农业实践。
原文摘要 · Abstract (English)
The leaf area index determines crop health and growth. Traditional methods for calculating it are time-consuming, destructive, costly, and limited to a scale. In this study, we automate the index estimation method using drone image data of grapevine plants and a machine learning model. Traditional feature extraction and deep learning methods are used to obtain helpful information from the data and enhance the performance of the different machine learning models employed for the leaf area index prediction. The results showed that deep learning based feature extraction is more effective than traditional methods. The new approach is a significant improvement over old methods, offering a faster, non-destructive, and cost-effective leaf area index calculation, which enhances precision agriculture practices.
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