https://www.kaggle.com/datasets?search=soil

Dear Assma Azeroual ,

The soil is the entity that keeps living on Earth alive. Despite substantial progress in the service sector, agriculture remains India's primary source of employment and revenue. The soil sample is a useful method for determining the present nutrient level of soil and determining the appropriate quantity of nutrition to apply to a soil depending on its fertility requirements.

Preprint Machine Learning and Deep Learning for Soil Analysis and Cla...

Regards,

Shafagat

Soil Texture Datasets for Deep Learning

Deep learning, particularly Convolutional Neural Networks (CNNs), is well-suited for classifying soil texture from images due to their ability to learn complex spatial patterns.

• Soil Types Dataset (Kaggle)This dataset contains 144 labeled images across six distinct soil types: Alluvial, Clayey, Laterite, Loamy, Sandy Loam, and Sandy Soil. These classifications are inherently related to soil texture. Source: Soil Types Dataset
• Roboflow Universe - Soil Datasets Roboflow hosts a variety of community-contributed datasets. Searching for "soil" on their platform can yield several image datasets, some of which are explicitly designed for soil type or texture classification. Examples include:"Soil type class test" (820 images) and "Soil Type Classification" (154 images) related to texture. "Soil Classification Thesis" (154 images) which mentions soil texture analysis. Source: Top Soil Datasets and Models | Roboflow Universe
• Soil Classification Example (APMonitor)This resource provides a Python Jupyter Notebook and MATLAB Live Script for soil classification using CNNs, along with a soil_photos.zip archive. The dataset is structured for training classifiers to identify soil types such as Gravel, Sand, and Silt, which are textural components. Source: Image Classification: Soil Type | Machine Learning for Engineers - APMonitor
• Research Papers mentioning Texture Datasets:Some research, such as "Soil Texture Classification Using Deep Learning" (on ResearchGate), discusses the use of smartphone images (e.g., a dataset of 576 images for sand and clay classification) and depth camera images (e.g., 27 images split into 17,442 smaller images for fine-grained and coarse-grained texture) for soil texture analysis. While direct dataset download links may not be in the abstract, the papers often provide details on data collection or access. Source: Soil Texture Classification Using Deep Learning

Soil Moisture Datasets for Deep Learning

Predicting soil moisture from images often involves satellite imagery or ground-level images with associated moisture measurements.

• NASA SMAP (Soil Moisture Active Passive) DatasetThis is a satellite-based dataset that provides global soil moisture measurements. While it's not "visual images" in the traditional sense, it provides valuable remote sensing data suitable for deep learning approaches, especially for large-scale moisture mapping and prediction. Source: 15 datasets to revolutionize agriculture with AI - Innovatiana (listed as dataset #5)
• Soil Moisture Prediction (GitHub Project)A GitHub project describes a mobile application that uses AI to predict soil moisture content from standard digital images. The project likely contains or refers to the image data used for training their model. Source: Abhinav1004/Soil-Moisture-Prediction - GitHub
• Satellite Data for Soil Moisture Prediction Research:Several research papers discuss deep learning models (like CNNs and LSTMs) for predicting soil moisture content using satellite images (e.g., Sentinel-1, Sentinel-2, MODIS, SMAP data). These papers demonstrate the feasibility of image-based deep learning for soil moisture, but the raw image datasets are typically large and linked to satellite archives rather than standalone downloads. Sources:A comprehensive study of deep learning for soil moisture prediction - HESS Soil Moisture Prediction Based on Satellite Data Using a Novel Deep Learning Model - ResearchGate Soil Moisture Prediction from Remote Sensing Images Coupled with Climate, Soil Texture and Topography via Deep Learning - MDPI

Soil Nutrients Datasets for Deep Learning (Image-Based)

Predicting soil nutrient content directly from standard RGB visual images is more challenging than texture or moisture. Often, it requires hyperspectral imaging or spectroscopic data which provides more detailed chemical information beyond what the human eye can see. However, deep learning models can integrate various data sources for nutrient prediction.

• Crop and Soil Dataset (Kaggle)While this dataset primarily contains numerical data (Nitrogen, Phosphorus, Potassium, pH, moisture, organic matter, etc.) rather than direct visual images for nutrient prediction, it is crucial for correlating visual characteristics (if combined with an image dataset) or other sensor data with nutrient levels. Source: Crop and Soil DataSet - Kaggle
• Research on Deep Learning for Nutrient Prediction:Many studies use deep learning to predict soil nutrients, often leveraging hyperspectral images, near-infrared spectroscopy (NIR), or a combination of satellite data, climate data, and soil properties. Direct publicly available datasets consisting of visual soil images specifically labeled with nutrient content for deep learning are less common. Sources:Deep Learning-Based Soil Nutrient Content Prediction for Crop Yield Estimation - ResearchGate GeaGrow: a mobile tool for soil nutrient prediction and fertilizer optimization using artificial neural networks - Frontiers (mentions iSDAsoil for mapped soil properties via API, not direct images)

General Considerations for Deep Learning on Soil Images:

• Data Collection: For specific research, you might need to collect your own images with corresponding lab measurements for texture, moisture, and nutrient content.
• Preprocessing: Images often require preprocessing (resizing, normalization, augmentation, noise reduction) before being fed into deep learning models.
• Feature Extraction: CNNs excel at automatic feature extraction from images, reducing the need for manual feature engineering.
• Model Choice: Various CNN architectures (e.g., ResNet, AlexNet, Inception, MobileNet) are commonly used for image classification and can be adapted for soil analysis tasks. Transfer learning with pre-trained models is a common and effective approach.