Vélez Sergio, Ariza-Sentís Mar, Valente João
Information Technology Group, Wageningen University & Research, 6708 PB Wageningen, the Netherlands.
Data Brief. 2024 May 3;54:110497. doi: 10.1016/j.dib.2024.110497. eCollection 2024 Jun.
The "EscaYard" dataset comprises multimodal data collected from vineyards to support agricultural research, specifically focusing on vine health and productivity. Data collection involved two primary methods: (1) unmanned aerial vehicle (UAV) for capturing multispectral images and 3D point clouds, and (2) smartphones for detailed ground-level photography. The UAV used was DJI Matrice 210 V2 RTK, equipped with a Micasense Altum sensor, flying at 30 m above ground level to ensure detailed coverage. Ground-level data were collected using smartphones (iPhone X and Xiaomi Poco X3 Pro), which provided high-resolution images of individual plants. These images were geotagged, enabling location mapping, and included data on the phytosanitary status and number of grape clusters per plant. Additionally, the dataset contains RTK GNSS data, offering high-precision location information for each vine, enhancing the dataset's value for spatial analysis. Moreover, the dataset is structured to support various research applications, including agronomy, remote sensing, and machine learning. It is particularly suited for studying disease detection, yield estimation, and vineyard management strategies. The high-resolution and multispectral nature of the data allows for a detailed analysis of vineyard conditions. Potential reuse of the dataset spans multiple disciplines, enabling studies on environmental monitoring, geographic information systems (GIS), and precision agriculture. Its comprehensive nature makes it a valuable resource for developing and testing algorithms for disease classification, yield prediction, and plant phenotyping. For instance, the images of bunches and grape leaves can be used to train object detection algorithms for accurate disease detection and consequent precise spraying. Moreover, yield prediction algorithms can be trained by extracting the phenotypic traits of the grape bunches. The "EscaYard" dataset provides a foundation for advancing research in sustainable farming practices, optimising crop health, and improving productivity through precise agricultural technologies.
“EscaYard”数据集包含从葡萄园收集的多模态数据，以支持农业研究，特别关注葡萄藤的健康状况和生产力。数据收集涉及两种主要方法：（1）使用无人机（UAV）获取多光谱图像和三维点云，（2）使用智能手机进行详细的地面摄影。所使用的无人机是大疆经纬M210 V2 RTK，配备了Micasense Altum传感器，在离地面30米的高度飞行，以确保详细的覆盖范围。地面数据是使用智能手机（iPhone X和小米Poco X3 Pro）收集的，这些手机提供了单株植物的高分辨率图像。这些图像带有地理标记，能够进行位置映射，并且包含每株植物的植物检疫状况和葡萄串数量的数据。此外，该数据集还包含RTK GNSS数据，为每株葡萄藤提供高精度的位置信息，提高了该数据集在空间分析方面的价值。此外，该数据集的结构支持各种研究应用，包括农学、遥感和机器学习。它特别适合于研究病害检测、产量估计和葡萄园管理策略。数据的高分辨率和多光谱特性使得能够对葡萄园状况进行详细分析。该数据集的潜在再利用跨越多个学科，可用于环境监测、地理信息系统（GIS）和精准农业等研究。其综合性使其成为开发和测试疾病分类、产量预测和植物表型分析算法的宝贵资源。例如，葡萄串和葡萄叶的图像可用于训练目标检测算法以进行准确的病害检测并进而进行精准喷洒。此外，产量预测算法可以通过提取葡萄串的表型特征来进行训练。“EscaYard”数据集为推进可持续农业实践、优化作物健康状况以及通过精准农业技术提高生产力的研究奠定了基础。
