用无人机采集西澳农田多光谱数据，实现92%的杂草检测准确率。
Multispectral Remote Sensing for Weed Detection in West Australian Agricultural Lands
- 结合植被指数与多光谱通道，用深度学习进行杂草识别
- ResNet模型在测试集上达到92.13%准确率，mIOU达78.88%
- 专为西澳农业设计的数据集，适合精准农业研究者使用
西澳大利亚科丁宁地区因广泛杂草入侵面临重大农业挑战，造成经济损失和生态影响。本研究构建了面向该地区农业应用的定制化多光谱遥感数据集，并提出端到端杂草检测框架。利用无人机在两个试验区域（E2和E8）连续四年采集原始多光谱数据，覆盖面积0.6046平方公里，通过配备GPS的车辆人工标注杂草与作物作为地面真值。数据集专为西澳农业场景设计。所提框架包含去噪、辐射校准、图像配准、正射校正和拼接等预处理步骤。方法融合NDVI、GNDVI、EVI、SAVI、MSAVI等植被指数与多光谱波段生成分类特征，采用多种深度学习模型识别杂草。其中ResNet表现最佳，杂草检测准确率达0.9213，F1-Score为0.8735，mIOU为0.7888，mDC为0.8865，验证了数据集与方法的有效性。
原文摘要 · Abstract (English)
The Kondinin region in Western Australia faces significant agricultural challenges due to pervasive weed infestations, causing economic losses and ecological impacts. This study constructs a tailored multispectral remote sensing dataset and an end-to-end framework for weed detection to advance precision agriculture practices. Unmanned aerial vehicles were used to collect raw multispectral data from two experimental areas (E2 and E8) over four years, covering 0.6046 km^{2} and ground truth annotations were created with GPS-enabled vehicles to manually label weeds and crops. The dataset is specifically designed for agricultural applications in Western Australia. We propose an end-to-end framework for weed detection that includes extensive preprocessing steps, such as denoising, radiometric calibration, image alignment, orthorectification, and stitching. The proposed method combines vegetation indices (NDVI, GNDVI, EVI, SAVI, MSAVI) with multispectral channels to form classification features, and employs several deep learning models to identify weeds based on the input features. Among these models, ResNet achieves the highest performance, with a weed detection accuracy of 0.9213, an F1-Score of 0.8735, an mIOU of 0.7888, and an mDC of 0.8865, validating the efficacy of the dataset and the proposed weed detection method.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。