# info.description
The Ag-Analytics Planting Date API uses machine learning models to give an estimate of the date that a certain crop was planted. This is a back-filling model meaning that it is used for determination of planting date for past years in fields where the true planting data is not known. 

# paths./.post.description
The Ag-Analytics® Planting Date API uses machine learning models to give an estimate of the date that a certain crop was planted. This is a back-filling model meaning that it is used for determination of planting date for past years in fields where the true planting data is not known.

Model Specifications

The planting date model relies on a several factors to determine the date. The current model inputs are information about location, soil characteristics, satellite imagery, and past weather data. By training a neural network on several thousand planting records obtained from farmers across the United States, the model achieves a high level of accuracy when tested against out-of-sample data. Future versions of the model will utilize greater amounts of acreage and more specific sets of variables as the driving factors are determined through research and testing.
Further testing will determine the level of importance of each variable used to train the model, but the current variables are effective in estimation of planting date.

Location: the location of the field can provide insight into the geospatial dependence on factors like climate and other local farming practices.

Soil characteristics: data from both POLARIS and SSURGO are used as predictors of the planting date. The idea is that different soil types may correlate with the type of crops grown and when they are best planted.

Satellite imagery: by using the Ag-Analytics® Harmonized Landsat Sentinel Service, the model obtains a wide array of multispectral bands for a growing season. Currently, short-wave infrared 1 and 2, RBG, and near-infrared bands are included in an 18-week window.

Weather conditions: historical weather conditions are critical in determination of planting date because of the influence on field conditions. Extreme heat or cold and rain or drought can delay planting significantly, which changes the output of the model.

Request Parameters ▼

Request Parameters

Parameter

Date Type

Required?

Default

Options

Description

SHAPE

Geometry, file/text

Yes

--

Geojson

Desired area-of-interest. See Fig. 1 for example.

ModelType

Text String

No

Neural
Network

“NN”
(Neural Network)
“TREE”
(Decision Tree)

Type of model to use for prediction. Default is neural network.
Not necessary to specify for most cases.

CropSeason

Text

Yes

--

2013 -2019

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

Response Parameters ▼

Response Parameters

Parameter

Date Type

Required?

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. attributes.CellSize

Resolution

Resolution of result Geotiff file in meters.

rasterinfo. attributes.CoordinateSystem

-

Information about the projection of the raster.

rasterinfo. attributes.Extent

-

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

rasterinfo. attributes.Legend

List

Legend gives the following details for each range of values:
I. Area: Area covered
II. Count: # of pixels from the result raster in that range
III. CountAllPixels: Total # of pixels in the result raster
IV. Max: Max value in the range
V. Min: Minimum value in the range
VI. Mean: Mean value in the range
VII. Color: Hex color used for the crop type

rasterinfo.attributes.Matrix

List

Row and columns, containing attributes below.

rasterinfo.attributes.Max

Number

Maximum value from the result raster.

rasterinfo.attributes.Min

Number

Minimum value from the result raster.

rasterinfo.attributes.Mean

Number

Average value from the result raster.

rasterinfo.attributes.Percentile5

Number

5th percentile value from the result raster.

rasterinfo.attributes.Percentile95

Number

95th percentile value from the result raster.

rasterinfo. attributes.pngb64

Link

Base64png image of the result raster with legend entries.

Examples ▼

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]]]}}"

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 Example - application/json

{
  "feature_averages": [
    {"Key": "Greenweek18","Value": 1218.6882460676118},
    {"Key": "Redweek18","Value": 1276.7237154230897},
    { "Key": "SWIR2week18","Value": 50.21536849730397},
    {"Key": "SWIR1week18","Value": 2366.0455730402196},
    {"Key": "NIRweek18","Value": 2819.818161154887},
    {"Key": "Blueweek18","Value": 1599.2762531927453},
    {"Key": "Greenweek20","Value": 957.1687156481621},
    {"Key": "Redweek20","Value": 980.6943679720338},
    {"Key": "SWIR2week20","Value": 997.2153896961564},
    {"Key": "SWIR1week20","Value": 1559.769602306673},
    {"Key": "NIRweek20","Value": 2616.383912450711},
    {"Key": "Blueweek20","Value": 662.1803611724483
    {"Key": "Greenweek22", "Value": 865.8829308328359},
    {"Key": "Redweek22","Value": 895.5443958071401},
    {"Key": "SWIR2week22","Value": 1306.0237171209133},
    {"Key": "SWIR1week22","Value": 1848.4805423326445},
    {"Key": "NIRweek22","Value": 2665.3743359990017},
    {"Key": "Blueweek22","Value": 599.7110449794816},
    {"Key": "Greenweek24","Value": 4469.372969613223},
    {"Key": "Redweek24","Value": 4543.523878522247},
    {"Key": "SWIR2week24","Value": 3726.222820849983},
    {"Key": "SWIR1week24","Value": 6503.78555606734},
    {"Key": "NIRweek24","Value": 6454.815507608226},
    {"Key": "Blueweek24","Value": 3658.3995564435445
    {"Key": "Greenweek26","Value": 1508.828147157162},
    {"Key": "Redweek26","Value": 1522.5964741037603},
    {"Key": "SWIR2week26", "Value": 11.014840519379991},
    {"Key": "SWIR1week26","Value": 432.26650756358885},
    {"Key": "NIRweek26","Value": 3462.8701202282755},
    {"Key": "Blueweek26","Value": 1283.8910904532877},
    {"Key": "Greenweek28","Value": 897.9982080017721},
    {"Key": "Redweek28","Value": 931.8507324987523},
    {"Key": "SWIR1week28", "Value": 1571.3495378790276},
    {"Key": "SWIR2week28","Value": 1027.1511365545261},
    {"Key": "NIRweek28", "Value": 2740.6613500734948},
    {"Key": "Blueweek28","Value": 644.8779364279997
    {"Key": "Greenweek30","Value": 5864.923760534673},
    {"Key": "Redweek30","Value": 795.3165483552489},
    {"Key": "SWIR2week30","Value": 13.257439456487957},
    {"Key": "SWIR1week30","Value": 767.7595418620314},
    {"Key": "NIRweek30","Value": 5732.150236664299},
    {"Key": "Blueweek30","Value": 8131.229142339392},
    {"Key": "Greenweek32","Value": 731.9143641948485},
    {"Key": "Redweek32","Value": 807.0480643287201},
    {"Key": "SWIR2week32","Value": 10.802104931021402},
    {"Key": "SWIR1week32","Value": 472.45490145887067},
    {"Key": "NIRweek32","Value": 2515.4616126482197},
    {"Key": "Blueweek32","Value": 533.4108390157562},
    {"Key": "Greenweek34","Value": 896.549034849674},
    {"Key": "Redweek34","Value": 959.7777929988146},
    {"Key": "SWIR2week34","Value": 1502.5244706428862},
    {"Key": "SWIR1week34","Value": 2038.150860812607},
    {"Key": "NIRweek34","Value": 2408.0119739829343},
    {"Key": "Blueweek34","Value": 763.9157193529676},
    {"Key": "Greenweek36","Value": 918.9698085947933},
    {"Key": "Redweek36","Value": 984.9548189503837},
    {"Key": "SWIR2week36","Value": 1299.9700718861252},
    {"Key": "SWIR1week36","Value": 1977.4707248154064},
    {"Key": "NIRweek36","Value": 2628.78016105923},
    {"Key": "Blueweek36","Value": 696.3513398537269},
    {"Key": "GDD3","Value": 0.0},
    {"Key": "Precipitation3","Value": 67.55299999999998},
    {"Key": "GDD4","Value": 51.91649999999999},
    {"Key": "Precipitation4","Value": 34.26099999999999},
    {"Key": "GDD5","Value": 591.183},
    {"Key": "Precipitation5","Value": 120.82300000000002},
    {"Key": "GDD6","Value": 736.1180999999997},
    {"Key": "Precipitation6","Value": 196.54299999999992},
    {"Key": "GDD7","Value": 206.43119999999996},
    {"Key": "Precipitation7","Value": 109.73999999999998},
    {"Key": "GDD8","Value": 0.0},
    {"Key": "Precipitation8","Value": 0.0}
  ],
  "raster_filename": "result_cropidraster_20191127_202623_9621.tif",
  "rasterinfo": [
    {
      "attributes": {
        "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": "-93.7759549449, 41.6147830947, -93.77465494489999, 41.6513830947",
        "Legend": [
          {
            "Area": "1.44 %",
            "Count": 64,
            "CountAllPixels": 4446,
            "Max": 119.84002962820219,
            "Mean": 99.1953509709138,
            "Min": 78.5506723136254,
            "color": "#FF0000"
          },
          {
            "Area": "9.36 %",
            "Count": 416,
            "CountAllPixels": 4446,
            "Max": 161.12938694277898,
            "Mean": 140.48470828549057,
            "Min": 119.84002962820219,
            "color": "#FF6666"
          },
          {
            "Area": "89.2 %",
            "Count": 3966,
            "CountAllPixels": 4446,
            "Max": 202.41874425735574,
            "Mean": 181.77406560006736,
            "Min": 161.12938694277898,
            "color": "#FFFF66"
          }
        ],
        "Matrix": [
          366,
          13
        ],
        "Max": 202.41874425735574,
        "Mean": 168.44008744627573,
        "Min": 78.5506723136254,
        "OID": 0,
        "Percentile5": 152.23603227084163,
        "Percentile95": 172.45018523462116,
        "Variety": "NoVariety",
        "pngb64": "data:image/png;base64, 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"
      }
    }
  ]
}

# paths./.post.requestBody.description
{
    "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"
}

# paths./.get.description
The Ag-Analytics Planting Date GET API allows a user to easily "fetch" and download locally a resulting .tif from the Planting Date POST API. The Ag-Analytics® Planting Date model uses machine learning models to give an estimate of the date that a certain crop was planted. This is a back-filling model meaning that it is used for determination of planting date for past years in fields where the true planting data is not known.

Model Specifications
The planting date model relies on a several factors to determine the date. The current model inputs are information about location, soil characteristics, satellite imagery, and past weather data. By training a neural network on several thousand planting records obtained from farmers across the United States, the model achieves a high level of accuracy when tested against out-of-sample data. Future versions of the model will utilize greater amounts of acreage and more specific sets of variables as the driving factors are determined through research and testing. Further testing will determine the level of importance of each variable used to train the model, but the current variables are effective in estimation of planting date.

Location: the location of the field can provide insight into the geospatial dependence on factors like climate and other local farming practices.

Soil characteristics: data from both POLARIS and SSURGO are used as predictors of the planting date. The idea is that different soil types may correlate with the type of crops grown and when they are best planted.

Satellite imagery: by using the Ag-Analytics® Harmonized Landsat Sentinel Service, the model obtains a wide array of multispectral bands for a growing season. Currently, short-wave infrared 1 and 2, RBG, and near-infrared bands are included in an 18-week window.

Weather conditions: historical weather conditions are critical in determination of planting date because of the influence on field conditions. Extreme heat or cold and rain or drought can delay planting significantly, which changes the output of the model.

GET Request

Parameter | Description | Example | Request

filename

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

result_plantingdateraster
_2019212_145154_945.tif

https://ag-analytics.azure-api.net/plantingdate/?filename=
result_plantingdateraster_2019212_145154_945.tif

# paths./.get.parameters[].description
Filename of the raster from POST Planting Date.