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.