AUTOMATIC PREDICTION OF YIELDS AND RECOMMENDATION OF SEEDING RATES BASED ON WEATHER DATA

1 . A computer-implemented method of predicting yields and recommending seeding rates for subfields with informed risks, comprising:

receiving, by a processor, weather data for a first period consisting of a plurality of sub-periods for one or more subfields of a field;

for each of the plurality of sub-periods for the one subfield:

calculating a moisture stress indicator from the weather data;

predicting, for each of a list of seeding rates, a yield from the moisture stress indicator using a trained model;

selecting one of the list of seeding rates based on the list of predicted yields; and

identifying one of the predicted yields corresponding to the selected seeding rate;

determining, by the processor, a risk profile associated with a range of yields for the one subfield based on the predicted yields identified for the plurality of sub-periods;

transmitting data related to the risk profile to a device associated with the one subfield.

2 . The computer-implemented method of claim 1 ,

the weather data including observed soil moisture data for a certain subfield of the one or more subfields,

the calculating comprising estimating soil moisture data for a specific subfield of the one or more subfields from the weather data using a dynamic spatio-temporal model (DSTM).

3 . The computer-implemented method of claim 2 , the estimating comprising, solving, given precipitation data p, irrigation data irr, and observed soil moisture data for a subset of the one or more subfields, for α(s) and θ(s) and μ in:

Y t ( s )= w t ( s )+ e t ( s ),

w t ( s )= w t-1 ( s )+α( s )( p+irr ) t-1 −θ( s )( w t-1 ( s )−μ)+ u t ( s ),

Y t (s) being observed soil moisture at time t for the subfield s, w t (s) being an actual or estimated soil moisture at time t in a subfield s, μ being minimum saturation data, e t (s) being a random observation error, and u t (s) being a spatially correlated error of a real, unobserved soil moisture,

α(s) being an absorption rate and θ(s) being a water-out rate,

t corresponding to multiple points in an interval within the sub-period.

4 . The computer-implemented method of claim 3 , the estimating further comprising

computing, given precipitation data p, irrigation data irr, and w t (s) for at least one t of the specific subfield, soil moisture data for the specific subfield from:

w t ( s )= w t-1 ( s )+α( s )( p+irr ) t-1 −θ( s )( w t-1 ( s )−μ)+ u t ( s ).

5 . The computer-implemented method of claim 1 , the calculating further comprising:

determining soil moisture for the sub-period for the specific subfield from the weather data;

computing the moisture stress indicator as a percentage of the sub-period when the soil moisture for the one subfield is above a wet threshold or below a dry threshold within the sub-period.

6 . The computer-implemented method of claim 1 , further comprising

receiving soil chemistry data, soil topology data, or field imagery data for the first period for the one or more subfields,

the predicting being performed further based on the soil chemistry data, the soil topology data, or the field imagery data.

7 . The computer-implemented method of claim 6 ,

the soil chemistry data including data related to organic matter, cation exchange capacity, or pH scale,

the soil topology data including data related to elevation, slope, curvature, or aspect,

the field imagery data including satellite images or other aerial images.

8 . The computer-implemented method of claim 1 , further comprising

receiving training data for building the trained model,

the training data including, for each of a set of subfields for a certain period, soil moisture data, soil chemistry data, soil topology data, field imagery data, and soil seeding rate data at a point within the certain period and a corresponding yield.

9 . The computer-implemented method of claim 1 , the trained model being a random forest, a clustering algorithm, a neural network, or a logistic regression classifier.

10 . The computer-implemented method of claim 1 , the one seeding rate being an optimal seeding rate among the list of seeding rates corresponding to a highest predicted yield;

11 . The computer-implemented method of claim 1 , further comprising:

for each of the plurality of sub-periods for the one subfield:

adjusting the selected seeding rate to be closer to a selected or adjusted seeding rate for a neighboring subfield; and

determining an adjusted yield corresponding to the adjusted seeding rate,

the risk profile being determined based on the adjusted predicted yields for the plurality of subfields.

12 . The computer-implemented method of claim 1 , the determining further comprising:

computing quantiles of the plurality of predicted yields identified for the plurality of sub-periods,

the quantile where a predicted yield belongs indicating a risk associated with the predicted yield in the risk profile,

the risk being a percent chance that an actual yield is less than the predicted yield.

13 . The computer-implemented method of claim 1 , the determining further comprising:

for each of the plurality of sub-periods:

aggregating the plurality of identified predicted yields over the one or more subfields; and

aggregating the plurality of selected seeding rates over the one or more subfields;

computing quantiles of the plurality of aggregated predicted yields for the plurality of sub-periods,

the quantile where an aggregated predicted yield belongs indicating a risk associated with the aggregated predicted yield in the risk profile.

14 . The computer-implemented method of claim 13 , further comprising:

selecting a first aggregated predicted yield that belongs to a lower quantile and a second aggregated predicted yield that belongs to a higher quantile;

identifying a first aggregated seeding rate corresponding to the first aggregated predicted yield and a second aggregated seeding rate corresponding to the second aggregated predicted yield;

transmitting the first seeding rate with a lower risk associated with the first aggregated predicted yield and the second seeding rate with a higher risk associated with the second aggregated predicted yield to a device associated with the field.

15 . The computer-implemented method of claim 1 , further comprising:

calculating a recent moisture stress indicator for a recent sub-period later than the first period;

predicting, for each of a recent list of seeding rates, a yield from the recent moisture stress indicator using the trained model;

selecting an optimal seeding rate corresponding to the highest predicted yield among the list of predicted yields;

identifying a risk associated with the highest predicted yield from the risk profile;

the transmitting comprising sending the identified risk to the device associated with the one subfield.

16 . One or more non-transitory storage media storing instructions which, when executed by one or more computing devices, cause performance of a method of predicting yields and recommending seeding rates for subfields with informed risks, the method comprising:

receiving weather data for a first period consisting of a plurality of sub-periods for one or more subfields of a field;

for each of the plurality of sub-periods for the one subfield:

calculating a moisture stress indicator from the weather data;

predicting, for each of a list of seeding rates, a yield from the moisture stress indicator using a trained model; and

selecting one of the list of seeding rates based on the list of predicted yields;

identifying one of the predicted yields corresponding to the selected seeding rate;

determining a risk profile associated with a range of yields for the one subfield based on the predicted yields identified for the plurality of sub-periods;

transmitting data related to the risk profile to a device associated with the one subfield.

17 . The one or more non-transitory storage media of claim 15 ,

the weather data including observed soil moisture data for a certain subfield of the one or more subfields,

the calculating comprising estimating soil moisture data for a specific subfield of the one or more subfields from the weather data using a dynamic spatio-temporal model (DSTM).

18 . The one or more non-transitory storage media of claim 15 , the calculating further comprising:

determining soil moisture for the sub-period for the specific subfield from the weather data;

computing the moisture stress indicator as a percentage of the sub-period when the soil moisture for the one subfield is above a wet threshold or below a dry threshold within the sub-period.

19 . The one or more non-transitory storage media of claim 15 , the determining further comprising:

for each of the plurality of sub-periods:

aggregating the plurality of identified predicted yields over the one or more subfields; and

aggregating the plurality of selected seeding rates over the one or more subfields;

computing quantiles of the plurality of aggregated predicted yields for the plurality of sub-periods,

the quantile where an aggregated predicted yield belongs indicating a risk associated with the aggregated predicted yield in the risk profile.

20 . The one or more non-transitory storage media of claim 19 , the method further comprising:

selecting a first aggregated predicted yield that belongs to a lower quantile and a second aggregated predicted yield that belongs to a higher quantile;

identifying a first aggregated seeding rate corresponding to the first aggregated predicted yield and a second aggregated seeding rate corresponding to the second aggregated predicted yield;

transmitting the first seeding rate with a lower risk associated with the first aggregated predicted yield and the second seeding rate with a higher risk associated with the second aggregated predicted yield to a device associated with the field.