# info.description
This API is in BETA. Please contact us to demo this API, or for more information on production scale models.

# paths./.post.description
Service Overview

The Ag-Analytics® Crop Identification API timely and accurately determines the spatial
distribution, area, and type of crops present on a given field. This beta version uses machine
learning models to provide a back-filling crop identification service meaning that it is used for
the past years in the field where all the relevant information exists, but the crop type is
uncertain.

Crop Identification API in use on FarmScope® via CropAI tool.

Documentation

Github Repo

Notebook

Model Specifications

The Crop Identification Model, of which the Crop Identification API is based on, takes four main
factors into consideration: Location, Weather, Soil information, and Remote Sensing Satellite
Imagery/Data. Location, weather, and soil are the three most determining factors in if a given crop
will be planted and thrive in a certain area. Remote Sensing Satellite imagery/Data provides near
real-time information of a given crop at different developing stages during its growing season.

Sample API Specifications

Please contact us to demo
our crop identification models. See sample API call below.

Request Information ▼

Request Parameters

Parameter

Data Type

Required

Default

Options

Description

SHAPE

Geometry, file/text

Yes

-

GeoJSON (file/text),
JSON Geometry objects (file/text)

Desired area of interest.

CropSeason

Text

Yes

-

2013-2019

The year of interest to retrieve the crop type information.
Ex. "2018"

ModelType

Text String

No*

"NN"

"NN"
(Neural Network)

"TREE"
Decision Tree

The type of AI Model to be used.
*If "NN" or "TREE" are not input, must pass as an empty string.

Request Example - application/json

application/json
{"SHAPE": "{\"type\":\"Feature\",\"properties\":{},\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-100.953840994,38.5946753571],[-100.953832008,38.5948720599],[-100.953876941,38.5952162884],[-100.953957821,38.5955324152],[-100.953984781,38.5955745654],[-100.954029714,38.5957361407],[-100.954245394,38.5961716896],[-100.954452087,38.5964807873],[-100.95473966,38.5968179832],[-100.954910406,38.596965506],[-100.954910406,38.5969795557],[-100.954982299,38.5970287299],[-100.954982299,38.5970427797],[-100.955359739,38.5973378239],[-100.955377712,38.5973378239],[-100.955629338,38.5975134449],[-100.956042724,38.5977312143],[-100.956357257,38.5978646856],[-100.956707736,38.5979911318],[-100.957175042,38.598110553],[-100.957615388,38.5981878255],[-100.958055734,38.598229974],[-100.958514053,38.5982369988],[-100.958963386,38.5982088998],[-100.959412719,38.5981456769],[-100.959906984,38.5980332805],[-100.96023949,38.5979279087],[-100.960598957,38.5977874128],[-100.960976396,38.5976047676],[-100.961281942,38.597422122],[-100.961560528,38.5972113765],[-100.961578502,38.5972113765],[-100.961848101,38.5969865806],[-100.961982901,38.5968390579],[-100.962018848,38.5968179832],[-100.962189594,38.5966283107],[-100.962315407,38.5964526876],[-100.962405274,38.5964526876],[-100.962405274,38.5963894631],[-100.962369327,38.5963894631],[-100.962369327,38.5963754133],[-100.962513113,38.5961716896],[-100.96262994,38.5959539155],[-100.962800686,38.5955324152],[-100.962908526,38.595054712],[-100.962926499,38.5948580097],[-100.962890553,38.5948509846],[-100.962962446,38.5948650348],[-100.963025352,38.5947947839],[-100.962998393,38.5947877588],[-100.962989406,38.5947666835],[-100.962917513,38.5947526333],[-100.962935486,38.5947245329],[-100.962926499,38.5946894073],[-100.962881566,38.5947034575],[-100.962665886,38.5946823822],[-100.958541013,38.5946753571],[-100.9584152,38.5947175077],[-100.958316347,38.5947175077],[-100.958298374,38.5947034575],[-100.958154587,38.5946753571],[-100.953840994,38.5946753571]]]}}",
    "ScalarVariables": {
        "CropSeason": "2018"
    },
    "ModelType":"NN"
}

Response Information ▼

Response Parameters

Parameter

Type

Description

feature_averages

List

Averages of the
bi-week remote sensing data and monthly weather data of the area-of-interest:
I. Greenweek#: Green band value on the # week of the year
II. Blueweek#: Blue band value on the # week of the year
III. Redweek#: Red band value on the # week of the year
IV. NIRweek#: Near Infrared band value on the # week of the year
V. SWIR1week#: Shortwave Infrared band 1 value on the # week of the year
VI. SWIR2week#: Shortwave Infrared band 2 value on the # week of the year
VII. GDD#: Monthly growing degree days
VIII. Precipitation#: Monthly precipitation

raster_filename

-

URL to download result raster (.tif) file.

rasterinfo.CellSize

Resolution

Resolution of result Geotiff file in meters.

rasterinfo.CoordinateSystem

-

Information about the projection of the raster.

rasterinfo.Extent

-

Extents of the result raster. Specifies the bottom left and top right corners of the field
raster in degrees.

rasterinfo.Legend

List

Legend gives
the following details for each range of values:
I. Area: Area covered
II. AreaUnit: Unit of Area covered
III. AreaPercent: Area covered in percentage.
IV. Count: # of pixels from the result raster in that range
V. CountAllPixels: Total # of pixels in the result raster
VI. CropID: Code for the crop identified by model. See Figure 4
VII. CropName: Crop name identified by the model
VIII. Color: Hex color used for the crop type

rasterinfo.pngb64

Link

Base64png image of the result raster with legend entries.

Response Example - application/json

{
  "feature_averages": {
    "Blueweek18": 779.824844896861,
    "Blueweek20": 1975.2838106368124,
    "Blueweek22": 733.3521051359988,
    "Blueweek24": 643.9180616872748,
    "Blueweek26": 344.2115098055459,
    "Blueweek28": 235.72472900506003,
    "Blueweek30": 204.4056550889475,
    "Blueweek32": 216.0730864324732,
    "Blueweek34": 266.59034645931484,
    "Blueweek36": 622.0089586686997,
    "GDD3": 40.4415,
    "GDD4": 88.57439999999998,
    "GDD5": 567.5903999999999,
    "GDD6": 808.3584,
    "GDD7": 216.13589999999996,
    "GDD8": 0.0,
    "Greenweek18": 1138.131570659558,
    "Greenweek20": 2254.270159289066,
    "Greenweek22": 1013.7187401697631,
    "Greenweek24": 928.849268847287,
    "Greenweek26": 571.9230231878212,
    "Greenweek28": 457.9029104885397,
    "Greenweek30": 384.65050915319665,
    "Greenweek32": 375.24875445591255,
    "Greenweek34": 540.8961853834992,
    "Greenweek36": 749.1952922510966,
    "NIRweek18": 2970.0740078383446,
    "NIRweek20": 3536.6660938053815,
    "NIRweek22": 2412.510195847856,
    "NIRweek24": 3292.298120071036,
    "NIRweek26": 3535.8988991057713,
    "NIRweek28": 3721.192446778267,
    "NIRweek30": 3591.1494805204393,
    "NIRweek32": 3262.5500419977257,
    "NIRweek34": 3012.6094064752187,
    "NIRweek36": 2956.2582454919366,
    "Precipitation3": 11.524,
    "Precipitation4": 25.41900000000001,
    "Precipitation5": 70.78399999999999,
    "Precipitation6": 152.91100000000003,
    "Precipitation7": 1.4809999999999999,
    "Precipitation8": 0.0,
    "Redweek18": 1903.1859603449168,
    "Redweek20": 2480.602444221482,
    "Redweek22": 1324.5397082727213,
    "Redweek24": 1069.7853810301349,
    "Redweek26": 512.0971963633662,
    "Redweek28": 379.9113349957818,
    "Redweek30": 283.1936926800272,
    "Redweek32": 279.2847222138893,
    "Redweek34": 467.8558782624387,
    "Redweek36": 727.4151962193739,
    "SWIR1week18": 3338.836322033022,
    "SWIR1week20": 3808.579113377245,
    "SWIR1week22": 3199.2655615880494,
    "SWIR1week24": 1902.9297659572896,
    "SWIR1week26": 1254.004163243775,
    "SWIR1week28": 677.788417488776,
    "SWIR1week30": 962.4707162781339,
    "SWIR1week32": 994.7986702873936,
    "SWIR1week34": 865.6824701031575,
    "SWIR1week36": 628.7585803430337,
    "SWIR2week18": 2391.667558326072,
    "SWIR2week20": 2977.71718115419,
    "SWIR2week22": 2392.3059762302105,
    "SWIR2week24": 1066.0881596873523,
    "SWIR2week26": 577.5711350554554,
    "SWIR2week28": 14.881174763569534,
    "SWIR2week30": 305.1543742073506,
    "SWIR2week32": 299.5264928749243,
    "SWIR2week34": 805.5621165630723,
    "SWIR2week36": 193.83389703934495
  },
  "raster_filename": "result_cropidraster_20191126_183736_2356.tif",
  "rasterinfo": {
    "CellSize": [
      0.0001,
      -0.0001
    ],
    "CoordinateSystem": "GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0],UNIT[\"degree\",0.0174532925199433],AUTHORITY[\"EPSG\",\"4326\"]]",
    "Extent": "-100.963025352, 38.5946369988, -100.953925352, 38.5982369988",
    "Legend": [
      {
        "Area": 4.969644751890395,
        "AreaPercent": '3.82 %',
        "AreaUnit": "ac",
        "Count": 101,
        "CountAllPixels": 2645,
        "CropID": 1,
        "CropName": "CORN_WET",
        "color": "#e8aba4"
      },
      {
        "Area": "96.18 %",
        "Area": 125.17600246345707,
        "AreaPercent": "96.18 %",
        "AreaUnit": "ac",
        "Count": 2544,
        "CountAllPixels": 2645,
        "CropID": 4,
        "CropName": "WHEAT_HRD_RD_SPR",
        "color": "#9d250e"
      }
    ],
    "pngb64": "data:image/png;base64, iVBORw0KGgoAAAANSUhEUgAAAFsAAAAkCAYAAADmQkaNAAABCklEQVR4nO3Xuw3CMBSFYdsD0DECkzEXXbqMxAKR0jEBFSgY27l+3OM8zt9GSpSPI0dYs6Eet8uscd/783XVuG9utufDtXAl9fgB4Ng9gWOh4CHYWwSOpQmvir0nZD8NdBXsPSP7tURvin0kZL8W6E2wj4zsV4Puah9+Jmhj6t63eNlnQ/YrWXgR9tmhl+WgZx8jhP4txyMLm9DhpC5ibEKnk/iIsAkta81pFZvQeaW8ktiELivmFsUmdF0hv+p/kExeEJurbpPv+IdNaL14jCi3HK+LXWDt47IBfUZMbGBfbB4huk3jMHPZwIgNzBnDIwQVlw2M2MCIDYzYwBw/jri4bGB2GgcuG9Qbz/tZxU5PLj0AAAAASUVORK5CYII="
  }
}

Shape Example ▼

Shape Example - GeoJSON

"{\"type\":\"Feature\",\"properties\":{},\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-100.953840994,38.5946753571],[-100.953832008,38.5948720599],[-100.953876941,38.5952162884],[-100.953957821,38.5955324152],[-100.953984781,38.5955745654],[-100.954029714,38.5957361407],[-100.954245394,38.5961716896],[-100.954452087,38.5964807873],[-100.95473966,38.5968179832],[-100.954910406,38.596965506],[-100.954910406,38.5969795557],[-100.954982299,38.5970287299],[-100.954982299,38.5970427797],[-100.955359739,38.5973378239],[-100.955377712,38.5973378239],[-100.955629338,38.5975134449],[-100.956042724,38.5977312143],[-100.956357257,38.5978646856],[-100.956707736,38.5979911318],[-100.957175042,38.598110553],[-100.957615388,38.5981878255],[-100.958055734,38.598229974],[-100.958514053,38.5982369988],[-100.958963386,38.5982088998],[-100.959412719,38.5981456769],[-100.959906984,38.5980332805],[-100.96023949,38.5979279087],[-100.960598957,38.5977874128],[-100.960976396,38.5976047676],[-100.961281942,38.597422122],[-100.961560528,38.5972113765],[-100.961578502,38.5972113765],[-100.961848101,38.5969865806],[-100.961982901,38.5968390579],[-100.962018848,38.5968179832],[-100.962189594,38.5966283107],[-100.962315407,38.5964526876],[-100.962405274,38.5964526876],[-100.962405274,38.5963894631],[-100.962369327,38.5963894631],[-100.962369327,38.5963754133],[-100.962513113,38.5961716896],[-100.96262994,38.5959539155],[-100.962800686,38.5955324152],[-100.962908526,38.595054712],[-100.962926499,38.5948580097],[-100.962890553,38.5948509846],[-100.962962446,38.5948650348],[-100.963025352,38.5947947839],[-100.962998393,38.5947877588],[-100.962989406,38.5947666835],[-100.962917513,38.5947526333],[-100.962935486,38.5947245329],[-100.962926499,38.5946894073],[-100.962881566,38.5947034575],[-100.962665886,38.5946823822],[-100.958541013,38.5946753571],[-100.9584152,38.5947175077],[-100.958316347,38.5947175077],[-100.958298374,38.5947034575],[-100.958154587,38.5946753571],[-100.953840994,38.5946753571]]]}}"

# paths./.get.description
Service Overview
The Ag-Analytics® Crop Identification API timely and accurately determines the spatial distribution, area, and type of crops present on a given field. This beta version uses machine learning models to provide a back-filling crop identification service meaning that it is used for the past years in the field where all the relevant information exists, but the crop type is uncertain.

Crop Identification API in use on FarmScope® via CropAI tool.

Documentation

Github Repo

Notebook

Model Specifications
The Crop Identification Model, of which the Crop Identification API is based on, takes four main factors into consideration: Location, Weather, Soil information, and Remote Sensing Satellite Imagery/Data. Location, weather, and soil are the three most determining factors in if a given crop will be planted and thrive in a certain area. Remote Sensing Satellite imagery/Data provides near real-time information of a given crop at different developing stages during its growing season.
Sample API Specifications
Please contact us to demo our crop identification models. See sample API call below.

GET Request

Parameter | Description | Example | Request

filename

File name to download. From POST request.
Only request a single file at a time.

result_ cropidraster_20191126_183736_ 2356.tif

https://ag-analytics.azure-api.net/crop-identification-model/?filename= result_ cropidraster_20191126_183736_ 2356.tif

# paths./.get.parameters[].description
File name of the file from POST Crop Identification.