Soil Moisture Dataset for Image Based Soil Classification

This dataset contains high-quality images of soil surfaces categorized into three moisture levels—Wet, Moderate, and Dry—captured under natural outdoor lighting in Sirajganj, Bangladesh. Images were collected at seven time intervals (0 min, 30 min, 1 hr, 2 hr, 4 hr, 5 hr, and 7+ hr after saturation) using a Sony Xperia 1 Mark II smartphone. A total of 1,177 raw images were captured, with blurry, noisy, and low-quality photos removed during pre-processing. The dataset reflects real-world agricultural conditions and serves as a benchmark for training machine learning and deep learning models for non-invasive soil moisture classification. Subject Areas: Computer Science, Agriculture Science, AI, Computer Vision, Environmental Monitoring, Pattern Recognition Data Format: JPG images (raw and filtered) Data Collection: Captured using Sony Xperia 1 Mark II under natural outdoor lighting in multiple soil locations. Organized into three labeled categories (Wet, Moderate, Dry) based on time intervals after saturation. Can be split into training and testing sets (recommended 80:20 ratio). Usage Notes: Ideal for developing AI models in soil moisture classification, precision irrigation scheduling, and image-based environmental monitoring. Supports affordable, sensor-free soil analysis for sustainable farming practices, particularly in resource-limited settings.本数据集收录高质量土壤表面图像，按湿度等级划分为三类：湿润（Wet）、中等（Moderate）与干燥（Dry），采集自孟加拉国锡拉杰甘杰（Sirajganj）的自然户外光照环境。图像采集于土壤饱和后的七个时间节点：0分钟、30分钟、1小时、2小时、4小时、5小时及7+小时，采集设备为索尼Xperia 1 Mark II智能手机。本次共采集原始图像1177张，预处理阶段已剔除模糊、带噪及低质量样本。 该数据集贴合真实农业场景，可作为训练非侵入式土壤湿度分类机器学习（Machine Learning）与深度学习（Deep Learning）模型的基准数据集。 学科领域：计算机科学、农业科学、人工智能（AI）、计算机视觉（Computer Vision）、环境监测（Environmental Monitoring）、模式识别（Pattern Recognition） 数据格式：JPG图像（包含原始样本与滤波后样本） 数据采集说明： - 采集自孟加拉国锡拉杰甘杰的多个土壤点位，采用自然户外光照，使用索尼Xperia 1 Mark II智能手机拍摄； - 依据土壤饱和后的时间间隔划分为三个标注类别：湿润、中等与干燥； - 支持按训练集与测试集拆分，推荐拆分比例为80:20。 使用说明： 本数据集适用于开发土壤湿度分类、精准灌溉调度及基于图像的环境监测相关人工智能模型，可为资源受限地区的可持续农业实践提供低成本、无传感器的土壤分析解决方案。