Here’s a detailed description for updating and improving your crop recommendation system based on soil data:

Description of a Crop Recommendation System with Soil Data

A crop recommendation system helps farmers select the best crops to grow based on the specific properties of their soil. This system uses soil characteristics and environmental factors to determine the crops that are most likely to thrive. Recommendations are provided to improve crop yield, optimize resource use, and ensure sustainable farming practices.

Core Components for Recommendations

The system should consider the following soil parameters and external factors to make accurate recommendations:

1. Soil Nutrients:

   • Nitrogen (N): Promotes leafy growth; ideal for crops like spinach, lettuce, and wheat.
   • Phosphorus (P): Essential for root development; crucial for legumes, peas, and root vegetables like carrots.
   • Potassium (K): Enhances disease resistance and fruit quality; important for fruiting plants like tomatoes, bananas, and potatoes.

2. Soil pH:

   • Indicates soil acidity or alkalinity.
   • Neutral pH (6.5-7.5) supports most crops like rice, wheat, and maize.
   • Acidic soil (<6.5) favors crops like tea and coffee.
   • Alkaline soil (>7.5) supports crops like barley and asparagus.

3. Organic Matter:

   • High organic content improves water retention and nutrient availability.
   • Crops like vegetables and fruits benefit from rich organic matter.

4. Moisture Level:

   • Determines irrigation needs and crop suitability.
   • High moisture crops: Paddy, sugarcane.
   • Low moisture crops: Millet, sunflower.

5. Temperature:

   • Warm crops: Maize, rice, and cotton.
   • Cool crops: Wheat, barley, and cabbage.

6. Rainfall:

   • Rain-fed crops (e.g., rice) thrive in high rainfall areas.
   • Drought-resistant crops (e.g., millets) perform well in low-rainfall zones.

7. Geographical Factors:

   • Altitude, latitude, and local climate conditions.
   • Example: Coffee grows well in high altitudes, while coconut thrives in coastal regions.

How to Update Recommendations

1. Dynamic Soil Profiles:

   • Use real-time soil testing data to determine nutrient levels, pH, and moisture.
   • Example: If the nitrogen level is low, recommend nitrogen-fixing crops like legumes.

2. Crop Rotation Insights:

   • Suggest crop rotations to maintain soil health.
   • Example: After a nitrogen-depleting crop like wheat, recommend a nitrogen-fixing crop like lentils.

3. Fertilizer Suggestions:

   • Provide recommendations for fertilizers based on deficiencies.
   • Example: If phosphorus is low, suggest adding rock phosphate.

4. Weather and Climate Integration:

   • Include real-time weather data like rainfall forecasts and temperature trends.
   • Example: Recommend drought-tolerant crops during dry seasons.

5. Regional Crop Suitability:

   • Use regional data to match crops with local soil and climate.
   • Example: Recommend paddy in water-rich regions like Punjab, and millet in arid regions like Rajasthan.

Sample Output for Crop Recommendations

Based on soil and environmental data: - Soil Parameters: - pH: 6.8 (neutral) - Nitrogen: Medium - Phosphorus: Low - Potassium: High - Moisture: Moderate - Recommendations: - Primary Crops: Wheat, Maize, Barley. - Secondary Crops (Improving Soil Health): Lentils, Chickpeas (for nitrogen fixation). - Fertilizer Recommendation: Use phosphorus-rich fertilizers (e.g., DAP).

How to Present Recommendations

• Use a dashboard or mobile app for farmers.
• Show clear visualizations of soil test results and matched crops.
• Include:
  • Top recommended crops.
  • Fertilizer and irrigation tips. -``

The National Soil Database has produced a national database of soil geochemistry including point and spatial distribution maps of major nutrients, major elements, essential trace elements, trace elements of special interest and minor elements. In addition, this study has generated a National Soil Archive, comprising bulk soil samples and a nucleic acids archive each of which represent a valuable resource for future soils research in Ireland. The geographical coherence of the geochemical results was considered to be predominantly underpinned by underlying parent material and glacial geology. Other factors such as soil type, land use, anthropogenic effects and climatic effects were also evident. The coherence between elements, as displayed by multivariate analyses, was evident in this study. Examples included strong relationships between Co, Fe, As, Mn and Cu. This study applied large-scale microbiological analysis of soils for the first time in Ireland and in doing so also investigated microbial community structure in a range of soil types in order to determine the relationship between soil microbiology and chemistry. The results of the microbiological analyses were consistent with geochemical analyses and demonstrated that bacterial community populations appeared to be predominantly determined by soil parent material and soil type. .hidden { display: none }

Soil Classification Datasets

Please ensure to cite the paper when utilizing the dataset in a research study. Refer to the paper link or BibTeX provided below.

This repository contains comprehensive datasets for soil classification and recognition research. The Original Dataset comprises soil images sourced from various online repositories, which have been meticulously cleaned and preprocessed to ensure data quality and consistency. To enhance the dataset's size and diversity, we employed Generative Adversarial Networks (GANs), specifically the CycleGAN architecture, to generate synthetic soil images. This augmented collection is referred to as the CyAUG Dataset. Both datasets are specifically designed to advance research in soil classification and recognition using state-of-the-art deep learning methodologies.

This dataset was curated as part of the research study titled "An advanced artificial intelligence framework integrating ensembled convolutional neural networks and Vision Transformers for precise soil classification with adaptive fuzzy logic-based crop recommendations" by Farhan Sheth, Priya Mathur, Amit Kumar Gupta, and Sandeep Chaurasia, published in Engineering Applications of Artificial Intelligence.

Links

• Paper: An advanced artificial intelligence framework integrating ensembled convolutional neural networks and Vision Transformers for precise soil classification with adaptive fuzzy logic-based crop recommendations
• Code: Development Code (model and recommendation system)

Application produced by this research is available at:

• Agro Companion: Agro Companion
• Server Code: Agro Companion Server

Note: If you are using any part of this project; dataset, code, application, then please cite the work as mentioned in the Citation section below.

Dataset

Both dataset consists of images of 7 different soil types.

The Soil Classification Dataset is structured to facilitate the classification of various soil types based on images. The dataset includes images of the following soil types:

• Alluvial Soil
• Black Soil
• Laterite Soil
• Red Soil
• Yellow Soil
• Arid Soil
• Mountain Soil

The dataset is organized into folders, each named after a specific soil type, containing images of that soil type. The images vary in resolution and quality, providing a diverse set of examples for training and testing classification models.

Original Dataset Details

• Total Images: 1189 images
• Image Format: JPG/JPEG
• Image Size: Varies
• Source: Collected from various online repositories and cleaned for consistency.

CyAUG Dataset Details

• Total Images: 5097 images
• Image Format: JPG/JPEG
• Image Size: Varies
• Source: Generated using CycleGAN to augment the original dataset, enhancing its size and diversity.

Input and Output Parameters

• Input Parameters:
  • Image: The images of the soils (JPG/JPEG format).
  • Label: The labels are in the format 'soil types' (folder names).
• Output Parameter:
  • Classification: The predicted class (soil type) based on the input image.

Citation

If you are using any of the derived dataset, please cite the following paper:

@article{SHETH2025111425,
  title = {An advanced artificial intelligence framework integrating ensembled convolutional neural networks and Vision Transformers for precise soil classification with adaptive fuzzy logic-based crop recommendations},
  journal = {Engineering Applications of Artificial Intelligence},
  volume = {158},
  pages = {111425},
  year = {2025},
  issn = {0952-1976},
  doi = {https://doi.org/10.1016/j.engappai.2025.111425},
  url = {https://www.sciencedirect.com/science/article/pii/S0952197625014277},
  author = {Farhan Sheth and Priya Mathur and Amit Kumar Gupta and Sandeep Chaurasia},
  keywords = {Soil classification, Crop recommendation, Vision transformers, Convolutional neural network, Transfer learning, Fuzzy logic}
}

One of the obstacles in applying advanced crop simulation models such as DSSAT at a grid-based platform is the lack of gridded soil input data at various resolutions. Recently, there has been many efforts in scientific communities to develop spatially continuous soil database across the globe. The most representative example is the SoilGrids 1km released by ISRIC in 2014. In addition recent AfSIS project put a lot of efforts to develop more accurate soil database in Africa at high spatial resolution. Taking advantage of those two available high resolution soil databases (SoilGrids 1km and ISRIC-AfSIS at 1km resolution), this project aims to develop a set of DSSAT compatible soil profiles on 5 arc-minute grid (which is HarvestChoice’s standard grid). Six soil properties (bulk density, organic carbon, percentage of clay and silt, soil pH and cation exchange capacity) available from the original SoilGrids 1km or ISRIC-AfSIS were directly used as DSSAT inputs. We applied a pedo-transfer function to derive some soil hydraulic properties (saturated hydraulic conductivity, soil water content at field capacity, wilting point and saturation) which are critical to simulate crop growth. For other required variables, HarvestChoice’s HC27 database are used as a reference. Final outputs are provided in *.SOL file format (DSSAT soil database) for each country at 5-min resolution. In addition, uncertainty maps for organic carbon and soil water content at wilting points at the top 15 cm soil layers were generated to provide brief idea about accuracy of the final products. The generated soil properties were evaluated by visualizing their global maps and by comparing them with IIASA-IFPRI cropland map and AfSIS-GYGA’s available water content maps.

Description

In this dataset, there are soil data analyses with properties such as pH, organic matter (OM), salinity (EC), etc., major elements (N, P, K, Mg) as well as some microelements (Fe, Zn, Mn, Cu, B) with significant impact on plant nutrition.

Categories

Agricultural Soil

Acknowledgements & Source

Panagiotis Tziachris

Data Source

View Details

Image Source

This dataset is a digital soil survey and generally is the most detailed level of soil geographic data developed by the National Cooperative Soil Survey. The information was prepared by digitizing maps, by compiling information onto a planimetric correct base and digitizing, or by revising digitized maps using remotely sensed and other information.

This dataset consists of georeferenced digital map data and computerized attribute data. The map data are in a soil survey area extent format and include a detailed, field verified inventory of soils and miscellaneous areas that normally occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. A special soil features layer (point and line features) is optional. This layer displays the location of features too small to delineate at the mapping scale, but they are large enough and contrasting enough to significantly influence use and management. The soil map units are linked to attributes in the National Soil Information System relational database, which gives the proportionate extent of the component soils and their properties.

SSURGO depicts information about the kinds and distribution of soils on the landscape. The soil map and data used in the SSURGO product were prepared by soil scientists as part of the National Cooperative Soil Survey.

The U.S. Department of Agriculture, Agriculture and Agri-Food Canada, the Russian Academy of Agricultural Sciences, the University of Copenhagen Institute of Geography, the European Soil Bureau, the University of Manchester Institute of Landscape Ecology, MTT Agrifood Research Finland, and the Agricultural Research Institute Iceland have shared data and expertise in order to develop the Northern and Mid Latitude Soil Database (Cryosol Working Group, 2001). This database was the source of data for the current product. The spatial coverage of the Northern and Mid Latitude Soil Database is the polar and mid-latitude regions of the northern hemisphere: Alaska, Canada, Conterminous United States, Eurasia (except Italy), Greenland, Iceland, Kazakstan, Mexico, Mongolia, Italy, and Svalbard. The Northern and Mid Latitude Soil Database represents the proportion (percentage) of polygon encompassed by the dominant soil or nonsoil. Soils include turbels, orthels, histels, histosols, mollisols, vertisols, aridisols, andisols, entisols, spodosols, inceptisols (and hapludolls), alfisols (cryalf and udalf), natric great groups, aqu-suborders, glaciers, and rocklands. Also included are data on the circumpolar distribution of gelisols (turbels, orthels, and histels), and the ice content (low, medium, or high) of circumpolar soil materials (from the International Permafrost Association, 1997). The resulting maps show the dominant soil of the spatial polygon unless the polygon is over 90 percent rock or ice. Data are in the U.S. soil classification system and includes the distribution of soil types (%) within a map unit (polygon). Data are available in ESRI shapefile format and include the same attribute values with the exception of Italy, which does not contain distribution values.

This is a dataset download, not a document. The Open button will start the download.Detailed soil units from Soils Surveys covering nonfederal land conducted by the U.S. Natural Resource Conservation Service (NRCS) that differentiates mapped units on the basis of a range of physical, topographic, and chemical properties.

This dataset consists of general soil association units. It was developed by the National Cooperative Soil Survey and supersedes the State Soil Geographic (STATSGO) dataset published in 1994. It consists of a broad based inventory of soils and non-soil areas that occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped of 1:250,000 in the continental U.S., Hawaii, Puerto, and the Virgin Islands and 1:1,000,000 in Alaska. The dataset was created by generalizing more detailed soil survey maps. Where more detailed soil survey maps were not available, data on geology, topography, vegetation, and climate were assembled, together with Land Remote Sensing Satellite (LANDSAT) images. Soils of like areas were studied, and the probable classification and extent of the soils were determined.

Map unit composition was determined by transecting or sampling areas on the more detailed maps and expanding the data statistically to characterize the entire map unit.

This dataset consists of georeferenced vector digital data and tabular digital data. The map data were collected in 1- by 2-degree topographic quadrangle units and merged into a seamless national dataset. The soil map units are linked to attributes in the National Soil Information system relational database, which gives the proportionate extent of the component soils and their properties.

These data provide information about soil features on or near the surface of the Earth. Data were collected as part of the National Cooperative Soil Survey. These data are intended for geographic display and analysis at the state, regional, and national level. The data should be displayed and analyzed at scales appropriate for 1:250,000-scale data.

This data set provides soil maps for the United States (US) (including Alaska), Canada, Mexico, and a part of Guatemala. The map information content includes maximum soil depth and eight soil attributes including sand, silt, and clay content, gravel content, organic carbon content, pH, cation exchange capacity, and bulk density for the topsoil layer (0-30 cm) and the subsoil layer (30-100 cm). The spatial resolution is 0.25 degree. The Unified North American Soil Map (UNASM) combined information from the state-of-the-art US General Soil Map (STATSGO2) and Soil Landscape of Canada (SLCs) databases. The area not covered by these data sets was filled by using the Harmonized World Soil Database version 1.21 (HWSD1.21). The Northern Circumpolar Soil Carbon (NCSCD) database was used to provide more accurate and up-to-date soil organic carbon information for the high-latitude permafrost region and was combined with soil organic carbon content derived from the UNASM (Liu et al., 2013). The UNASM data were utilized in the North American Carbon Program (NACP) Multi-Scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP) as model input driver data (Huntzinger et al., 2013). The driver data were used by 22 terrestrial biosphere models to run baseline and sensitivity simulations. The compilation of these data was facilitated by the NACP Modeling and Synthesis Thematic Data Center (MAST-DC). MAST-DC was a component of the NACP (www.nacarbon.org) designed to support NACP by providing data products and data management services needed for modeling and synthesis activities.

The dataset consists of three raster GeoTIFF files describing the following soil properties in the US: available water capacity, field capacity, and soil porosity. The input data were obtained from the gridded National Soil Survey Geographic (gNATSGO) Database and the Gridded Soil Survey Geographic (gSSURGO) Database with Soil Data Development tools provided by the Natural Resources Conservation Service. The soil characteristics derived from the databases were Available Water Capacity (AWC), Water Content (one-third bar) (WC), and Bulk Density (one-third bar) (BD) aggregated as weighted average values in the upper 1 m of soil. AWC and WC layers were converted to mm/m to express respectively available water capacity and field capacity in 1 m of soil, and BD layer was used to produce soil porosity raster assuming that the average particle density of soils is equal to 2.65 g/cm3. For each soil property, soil maps with CONUS, Alaska, and Hawaii geographic coverages were derived from s ...

(Link to Metadata) This data set is a digital soil survey and generally is the most detailed level of soil geographic data developed by the National Cooperative Soil Survey. The information was prepared by digitizing maps, by compiling information onto a planimetric correct base and digitizing, or by revising digitized maps using remotely sensed and other information. This data set consists of georeferenced digital map data and computerized attribute data. The map data are in a soil survey area extent format and include a detailed, field verified inventory of soils and miscellaneous areas that normally occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. A special soil features layer (point and line features) is optional. This layer displays the location of features too small to delineate at the mapping scale, but they are large enough and contrasting enough to significantly influence use and management. The soil map units are linked to attributes in the National Soil Information System relational database, which gives the proportionate extent of the component soils and their properties. Survey Dates - https://www.nrcs.usda.gov/wps/portal/nrcs/surveylist/soils/survey/state/?stateId=VT

The International Soil Reference and Information Centre-World Inventory of Soil Emission Potentials (ISRIC-WISE) international soil profile data set consists of a homogenized, global set of 1,125 soil profiles for use by global modelers. These profiles provided the basis for the Global Pedon Database (GPDB) of the International Geosphere-Biosphere Programme (IGBP) - Data and Information System (DIS). The data set consists of a selection of 665 profiles originating from the Natural Resources Conservation Service (NRCS, Lincoln), 250 profiles obtained from the Food and Agriculture Organization (FAO, Rome), and 210 profiles from the reference collection of the International Soil Reference and Information Centre (ISRIC, Wageningen). All profiles are georeferenced and classified according to the 1974 Legend of the FAO-UNESCO Soil Map (FAC-UNESCO, 1974) of the World, as well as the 1988 Revised Legend of FAO-UNESCO (FAO, 1990). The data set includes information on soil classification, site data, soil horizon data, source of data, and methods used for determining analytical data. The data files are in a comma-delimited format. Data Citation: The data set should be cited as follows: Batjes, N. H. (ed). 2000. Global Soil Profile Data (ISRIC-WISE). Available on-line from the ORNL Distributed Active Archive Center, Oak Ridge National Laboratory, Oak Ridge, Tennessee, U.S.A.

These Soil Mapping Data Packages include 1. a Soil Map dataset which includes the equivalents to Soil Project Boundaries, Soil Survey Spatial View mapping polygons with attributes from the Soil Name and Layer Files, plus + A Soil Site dataset which includes soil pit site information and detailed soil pit descriptions and any associated lab analyses, and + The Soil Data Dictionary which documents the fields and allowable codes within the data. The Soil Map geodatabase contains the 'best available' data ranging from 1:20,000 scale to 1:250,000 scale with overlapping data removed. The choice of the datasets that remain is based on connectivity to the soil attributes (soil name and layer files), map scale and survey date. (Note: the BC Soil Landscapes of Canada (BCSLC) 1:1,000,000 data has not been included in the Soil_Map or SIFT, but is available from: CANSIS. (A complete soils data package with overlapping soil survey mapping and BCSLC is available on request. Note that the soil survey data with attributes can also be viewed interactively in the [Soil Information Finder Tool](The Soil Map dataset is also available for interactive map viewing or as KMZs from the Soil Information Finder Tool website.

This data set is a digital soil survey and generally is the most detailed level of soil geographic data developed by the National Cooperative Soil Survey. The information was prepared by digitizing maps, by compiling information onto a planimetric correct base and digitizing, or by revising digitized maps using remotely sensed and other information. This data set consists of georeferenced digital map data and computerized attribute data. The map data are in a soil survey area extent format and include a detailed, field verified inventory of soils and miscellaneous areas that normally occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. A special soil features layer (point and line features) is optional. This layer displays the location of features too small to delineate at the mapping scale, but they are large enough and contrasting enough to significantly influence use and management. The soil map units are linked to attributes in the National Soil Information System relational database, which gives the proportionate extent of the component soils and their properties. For more information, contact: GIS Manager Information Technology & Innovation (ITI) Montgomery County Planning Department, MNCPPC T: 301-650-5620

This soils feature class is a cleaned and edited version of WA Department of Natural Resources and Soil Conservation Service soils coverage for Washington State received in December of 2000. Soil polygons were merged to eliminate township lines from original coverage after its convertion to a feature class. Slivers, overlaps, and other topological discrepancies were cleaned, and DNR soil map unit names and identifier symbol ('IDENTIFIER') were added to the attribute table. Polygons were subsequently dissolved by 'Identifier', and associated info lookup tables (SOILS_CLASS, SOILS_COMPLX, SOILS_COMB, SOILS_MAIN, SOILS_PFLG) then joined to the polygon attribute table. _ Information for the soils data layer was derived from the Private Forest Land Grading system (PFLG) and subsequent soil surveys. PFLG was a five year mapping program completed in 1980 for the purpose of forest land taxation. It was funded by the Washington State Department of Revenue in cooperation with the Department of Natural Resources Soil Conservation Service (USCS), USDA Forest Service and Washington State University. State and private lands which had the potential of supporting commercial forest stands were surveyed. Some Indian tribal and federal lands were surveyed. Because this was a cooperative soils survey project, agricultural and non-commercial forest lands were also included within some survey areas. After the DNR originally developed its GIS, digitized soils delineations and a few soil attributes were transferred to the system. Remaining PFLG soils attributes were added at a later time and are available through associated lookup tables. SCS soils data on agricultural lands also have subsequently been added to this data layer. Approximately 1100 townships wholly or partially contain digitized soils data (2101 townships would provide complete coverage of the state of Washington). - "Detailed Layer Description", Washington State Department of Natural Resources Geographic Information System, 1998. - _

This global data set shows the spatial distribution of generalized soil classes as defined for IPCC Tier-I level national greenhouse gas inventory assessments. The database was derived from the Harmonized World Soil Data Base (HWSD ver. 1.1, at scale 1:1-1:5 M) and a series of taxotransfer procedures to convert FAO soil classifications (1974, 1985 and 1990 Legend) to the seven default IPCC soil classes: high activity clay (HAC), low activity clay (LAC), Sandy (SAN), Spodic (POD), Volcanic (VOL), wetlands (WET) and Organic (ORG). The resulting GIS database may be used for exploratory assessments at national and broader scale, for regions that lack more detailed soil information; inherent limitations of the data are discussed in the documentation. This dataset has been compiled in the framework of the GEF co-funded 'Carbon Benefits Project: Measuring, modelling and monitoring', Component A ( http://carbonbenefitsproject-compa.colostate.edu/index.htm). March 2021 (version 1.2): Minor updates were applied for the 'SAN' class; for details see below and download file.

This layer shows soil type, based on the result of a classification established from Kalimantan RePPProT data on 'SL_ORDER' field (1990, 1:250,000 scale) . This data was provided and processed by Daemeter Consulting. Soil categories from RePPProT were then re-classified by the World Resources Institute according to the FAO Digital Soil Map of the World, for use in the Suitability Mapper (2012). The FAO data is available at http://www.fao.org/geonetwork/srv/en/metadata.show?id=14116 . Data separated into categories: Inceptisol; Oxisol; Alfisol; Ultisol; Spodosol; Entisol; Histosol.

Soil texture classes (USDA system) for 6 soil depths (0, 10, 30, 60, 100 and 200 cm) at 250 m Derived from predicted soil texture fractions using the soiltexture package in R. Processing steps are described in detail here. Antarctica is not included. To access and visualize maps outside of Earth Engine, use this page. If you discover a bug, artifact or inconsistency in the LandGIS maps or if you have a question please use the following channels: Technical issues and questions about the code General questions and comments

Soil Survey Geographic Database (SSURGO) consists of spatial data and a comprehensive relational database with tables that describe soil properties, interpretations and productivity values. The USDA Natural Resources Conservation Service (NRCS, formerly Soil Conservation Service) provides a download of the statewide SSURGO database that includes vector and raster spatial data, database tables and their relationship classes, and a user guide.Access SSURGO through the Web Soil Survey (WSS).