Estrada Juan Sebastian, Demarco Rodrigo, Johnson Ciarán Miceal, Zañartu Matias, Fuentes Andres, Auat Cheein Fernando
Department of Electronic Engineering, Universidad Tecnica Federico Santa Maria, Valparaiso, Chile.
Department of Industrial Engineering, Universidad Tecnica Federico Santa Maria, Valparaiso, Chile.
Sci Rep. 2025 Jan 23;15(1):2973. doi: 10.1038/s41598-025-85714-8.
Assessing the health status of vegetation is of vital importance for all stakeholders. Multi-spectral and hyper-spectral imaging systems are tools for evaluating the health of vegetation in laboratory settings, and also hold the potential of assessing vegetation of large portions of land. However, the literature lacks benchmark datasets to test algorithms for predicting plant health status, with most researchers creating tailored datasets. This work presents a dataset composed of multi-spectral images, hyper-spectral reflectance values, and measurements of weight, chlorophyll, and nitrogen content of leaves at five different drying stages, from avocado, olive, and grape trees, which are common crops in the Valparaíso region of Chile. This dataset is a valuable asset for developing tools in the field of precision agriculture and assessing the general health status of vegetation.
评估植被的健康状况对所有利益相关者至关重要。多光谱和高光谱成像系统是在实验室环境中评估植被健康状况的工具，也具有评估大片土地植被的潜力。然而，文献中缺乏用于测试预测植物健康状况算法的基准数据集，大多数研究人员都在创建定制数据集。这项工作展示了一个数据集，该数据集由多光谱图像、高光谱反射率值以及来自智利瓦尔帕莱索地区常见作物鳄梨树、橄榄树和葡萄树在五个不同干燥阶段的叶片重量、叶绿素和氮含量测量值组成。该数据集是开发精准农业领域工具和评估植被总体健康状况的宝贵资产。
