Methods and systems for sub-field soil nutrient management

1. A computing system for generating improved soil nutrient 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 a geographic field boundary of a field;

obtain topographic data corresponding to the field boundary of the field;

generate, based on the topographic data, a digital elevation model including derivative data corresponding to slope data, soil wetness index data, and catchment area data;

determine, based on the derivative data, critical level data for the area of the land; and

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

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

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

3. The computer system of claim 1 , the memory having stored thereon instructions that when executed, cause the computer 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 computer system of claim 1 , wherein the model is the Tri-State Fertilizer Recommendation model.

5. The computer 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 computer system of claim 1 , the memory having stored thereon instructions that when executed, cause the computer 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 computer system of claim 1 , the memory having stored thereon instructions that when executed, cause the computer 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 generating soil nutrients recommendation data, the method comprising:

determining, via one or more processors, a geographic field boundary of a field;

obtaining, via one or more processors, topographic data corresponding to the field boundary of the field;

generating, based on the topographic data, a digital elevation model including derivative data corresponding to slope data, soil wetness index data, and catchment area data;

determining, based on the derivative data, critical level data for the area of the land; and

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

9. The computer-implemented method of claim 8 , wherein the soil nutrients recommendation 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 the 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:

obtain topographic data corresponding to the field boundary of the field;

generate, based on the topographic data, a digital elevation model including derivative data corresponding to slope data, soil wetness index data, and catchment area data;

determine, based on the derivative data, critical level data for the area of the land; and

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

16. The non-transitory computer readable medium of claim 15 , wherein the soil nutrients recommendation 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.