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 critical level data, generate a spatial critical level map, generate soil nutrients recommendations data, and cause the spatial critical level map and the soil nutrients recommendations data to be displayed. A method includes determining critical level data, generating a spatial critical level map, generating soil nutrients recommendations data, and causing the spatial critical level map and the soil nutrients recommendations data to be displayed. A non-transitory computer readable medium includes program instructions that when executed by one or more processors, cause a computer to determine critical level data, generate a spatial critical level map, generate soil nutrients recommendations data, and cause the spatial critical level map and the soil nutrients recommendations data to be displayed.
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.