HydroGrowNet of Batavia Dataset

Over a three-month period, we conducted three consecutive 30-day experiments to monitor Batavia lettuce growth under varying conditions (water temperature, electrical conductivity, and pH). Each day, a dual-camera system traversed the hydroponic channels, capturing high-resolution images of every plant. In total, the dataset contains over 390,000 images, each annotated with environmental and water-quality metadata, forming a robust resource for machine learning–based growth prediction and anomaly detection in hydroponic farming. This data is provided after segmentation of yolo-v8. The HydroGrowNet dataset is a comprehensive multi-modal dataset designed for hydroponic farming research, particularly for plant growth prediction, anomaly detection, and environmental impact analysis. It integrates sensor-based environmental and water quality measurements with high-resolution plant growth images, providing a rich dataset for machine learning (ML) applications in controlled environment agriculture (CEA). The dataset was collected from a Nutrient Film Technique (NFT) hydroponic system cultivated with Batavia lettuce (Lactuca sativa L.) over a three-month period. It consists of structured numerical sensor data and unstructured image data, synchronized via timestamps, allowing for detailed growth analysis and fusion-based machine learning applications. Please check and cite our publication at DOI: 10.1016/j.engappai.2025.111214 We encourage expanding our dataset by adding more plants growth data other than Batavia lettuce, for more collaboration please contact [e-mail] or [e-mail] or [e-mail].本研究于三个月周期内开展了三组连续30天的实验，监测水温、电导率、pH值等不同条件下的巴达维亚生菜（Batavia lettuce）生长状态。每日，双摄像头系统遍历水培通道，采集每一株植株的高分辨率图像。本数据集总计包含超过39万张图像，每张图像均附带环境与水质元数据标注，可为水培农业中基于机器学习（Machine Learning）的生长预测与异常检测任务提供可靠的研究支撑资源。该数据集已完成YOLOv8（YOLO-v8）的分割标注处理。 HydroGrowNet数据集是一款面向水培农业研究的多模态综合数据集，尤其适配植株生长预测、异常检测与环境影响分析等研究方向。该数据集将基于传感器的环境与水质测量数据与高分辨率植株生长图像进行融合，为可控环境农业（Controlled Environment Agriculture）领域的机器学习应用提供了丰富的数据基础。 本数据集采集自以巴达维亚生菜（Lactuca sativa L.）为栽培作物的营养膜技术（Nutrient Film Technique）水培系统，实验周期为三个月。数据集包含结构化数值传感器数据与非结构化图像数据，二者通过时间戳实现精准同步，可支持精细化生长分析与基于多模态融合的机器学习应用。 请引用本研究的相关出版物，DOI：10.1016/j.engappai.2025.111214。 我们欢迎各方拓展本数据集，添加巴达维亚生菜以外的更多植株生长数据；如需开展更多合作，请联系以下邮箱：[e-mail]、[e-mail] 或 [e-mail]。