Methods and systems for sub-field soil nutrient management

1 . A computing system for visualizing soil nutrients recommendations data, the computing system comprising:

one or more processors; and

a memory having instructions stored thereon that, when executed by the one or more processors, cause the computing system to:

determine, based on derivative data included in a digital elevation model corresponding to an area of land within a geographic field boundary, critical level data comprising a plurality of spatial critical levels, each spatial critical level corresponding to a grid cell within the geographic field boundary, wherein the derivative data corresponds to slope data, soil wetness index data and catchment area data;

generate, based on the critical level data, a spatial critical level map for the area of land;

generate, using a model that receives the critical level data as input, the soil nutrients recommendations data;

cause the spatial critical level map and the soil nutrients recommendations data to be displayed on a graphical user interface of an output device; and

generate an agricultural prescription based on the spatial critical level map and the soil nutrients recommendations data.

2 . The computing system of claim 1 , wherein the soil nutrients recommendations data includes one or more of:

a fertilizer recommendation, a plant density recommendation, or a planting location recommendation.

3 . The computing system of claim 1 , the memory having stored thereon instructions that when executed, cause the computing system to:

obtain plant data for one or more plants to be grown within the area of the land; and

determine the critical level data for the area of the land based on the plant data for the one or more plants.

4 . The computing system of claim 1 , wherein the model is the Tri-State Fertilizer Recommendation model.

5 . The computing system of claim 1 , wherein the topographic data is (i) obtained via one or more of LiDAR data sources or RTK data sources and (ii) represented as hexagrids.

6 . The computing system of claim 1 , the memory having stored thereon instructions that when executed, cause the computer computing system to:

determine the critical level data for the area of the field using a machine learning model that receives the derivative data as input and predicts water movement within the field.

7 . The computing system of claim 1 , the memory having stored thereon instructions that when executed, cause the computing system to:

generate a report including at least one identification of an area of the field based on the critical level data.

8 . A computer-implemented method for visualizing soil nutrients recommendations data, the method comprising:

determining, based on derivative data included in a digital elevation model corresponding to an area of land within a geographic field boundary, critical level data comprising a plurality of spatial critical levels, each spatial critical level corresponding to a grid cell within the geographic field boundary, wherein the derivative data corresponds to slope data, soil wetness index data, and catchment area data;

generating, based on the critical level data, a spatial critical level map for the area land;

generating, using a model that receives the critical level data as input, the soil nutrients recommendations data;

causing the spatial critical level map and the soil nutrients recommendations data to be displayed on a graphical user interface of an output device; and

generating an agricultural prescription based on the spatial critical level map and the soil nutrients recommendations data.

9 . The computer-implemented method of claim 8 , wherein the soil nutrients recommendations data includes one or more of:

a fertilizer recommendation, a plant density recommendation, or a planting location recommendation.

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

obtaining, by one or more processors, plant data for one or more plants to be grown within the area of the land; and

determining the critical level data for the area of the land is further based on the plant data for the one or more plants.

11 . The computer-implemented method of claim 8 , wherein the model is the Tri-State Fertilizer Recommendation model.

12 . The computer-implemented method of claim 8 , wherein the topographic data is (i) obtained via one or more of LiDAR data sources or RTK data sources and (ii) represented as hexagrids.

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

determining the critical level data for the area of the using a machine learning model that receives the derivative data as input and predicts water movement within the field.

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

generating a report including at least one identification of an area of the field based on the critical level data.

15 . A non-transitory computer readable medium containing program instructions that when executed by one or more processors, cause a computer to:

determine, based on derivative data included in a digital elevation model corresponding to an area of land within a geographic field boundary, critical level data comprising a plurality of spatial critical levels, each spatial critical level corresponding to a grid cell within the geographic field boundary, wherein the derivative data corresponds to slope data, soil wetness index data, and catchment area data;

generate, based on the critical level data, a spatial critical level map for the area of land;

generate, using a model that receives the critical level data as input, soil nutrients recommendations data;

cause the spatial critical level map and the soil nutrients recommendations data to be displayed on a graphical user interface of an output device; and

generate an agricultural prescription based on the spatial critical level map and the soil nutrients recommendations data.

16 . The non-transitory computer readable medium of claim 15 , wherein the soil nutrients recommendations data includes one or more of a fertilizer recommendation, a plant density recommendation, or a planting location recommendation.

17 . The non-transitory computer readable medium of claim 15 , including further instructions that, when executed by one or more processors, cause a computer to:

obtain plant data for one or more plants to be grown within the area of the land; and

determine the critical level data for the area of the land based on the plant data for the one or more plants.

18 . The non-transitory computer readable medium of claim 15 , wherein the topographic data is (i) obtained via one or more of LiDAR data sources or RTK data sources and (ii) represented as hexagrids.

19 . The non-transitory computer readable medium of claim 15 , including further instructions that, when executed by one or more processors, cause a computer to:

determine the critical level data for the area of the field using a machine learning model that receives the derivative data as input and predicts water movement within the field.

20 . The non-transitory computer readable medium of claim 15 , including further instructions that, when executed by one or more processors, cause a computer to:

generate a report including at least one identification of an area of the field based on the critical level data.