Methods and systems for sub-field soil nutrient management
A computing system includes a processor 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, obtain topographic data, generate a digital elevation model including derivative data, determine critical level data and generate soil nutrients recommendations data. A method includes determining a geographic field boundary, obtaining topographic data, generating a digital elevation model including derivative data, determining critical level data; and generating the soil nutrients recommendations data. obtaining, by one or more processors, topographic data within an area of land. A non-transitory computer readable medium includes program instructions that when executed by one or more processors, cause a computer to determine a geographic field boundary, obtain topographic data, generate a digital elevation model including derivative data, determine critical level data and generate soil nutrients recommendations data.
1 . A computing system for generating improved 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 a geographic field boundary of a field;
obtain topographic data corresponding to the geographic field boundary;
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, a moisture status that describes where water is moving within the geographic field boundary on a hexagrid-by-hexagrid basis;
determine, based on the derivative data, critical level data based on the moisture status, wherein the critical level data comprises a plurality of spatial critical levels each corresponding to a respective hexagrid within the geographic field boundary; and
generate, based on the critical level data, the soil nutrients recommendations data comprising application rates per hexagrid.
2 . The computing system of claim 1 , wherein the soil nutrients recommendations data corresponds to one or more of: (i) a fertilizer, (ii) a plant density, or (iii) a planting location.
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 geographic field boundary; and
determine the critical level data based on the plant data for the one or more plants.
4 . The computing system of claim 1 , wherein the soil nutrients recommendations data is generated using a 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 computing system to:
determine the critical level data using a machine learning model that receives the derivative data as input and predicts water movement within the geographic field boundary.
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 generating soil nutrients recommendations data, 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 geographic field boundary;
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, a moisture status that describes where water is moving within the geographic field boundary on a hexagrid-by-hexagrid basis;
determining, based on the derivative data, critical level data based on the moisture status, wherein the critical level data comprises a plurality of spatial critical levels each corresponding to a respective hexagrid within the geographic field boundary; and
generating, based on the critical level data, the soil nutrients recommendations data comprising application rates per hexagrid.
9 . The computer-implemented method of claim 8 , wherein the soil nutrients recommendations data corresponds to one or more of: (i) a fertilizer, (ii) a plant density, or (iii) a planting location.
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 geographic field boundary; and
determining the critical level data based on the plant data for the one or more plants.
11 . The computer-implemented method of claim 8 , wherein the soil nutrients recommendations data is generated using a 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 using a machine learning model that receives the derivative data as input and predicts water movement within the geographic field boundary.
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 a geographic field boundary of a field;
obtain topographic data corresponding to the geographic field boundary;
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, a moisture status that describes where water is moving within the geographic field boundary on a hexagrid-by-hexagrid basis;
determine, based on the derivative data, critical level data based on the moisture status, wherein the critical level data comprises a plurality of spatial critical levels each corresponding to a respective hexagrid within the geographic field boundary; and
generate, based on the critical level data, soil nutrients recommendations data comprising application rates per hexagrid.
16 . The non-transitory computer readable medium of claim 15 , wherein the soil nutrients recommendations data corresponds to one or more of: (i) a fertilizer, (ii) a plant density, or (iii) a planting location.
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 geographic field boundary; and
determine the critical level data 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 using a machine learning model that receives the derivative data as input and predicts water movement within the geographic field boundary.
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.