Methods and systems for recommending agricultural activities

Abstract

A computer-implemented method for recommending agricultural activities is implemented by an agricultural intelligence computer system in communication with a memory. The method includes receiving a plurality of field definition data, retrieving a plurality of input data from a plurality of data networks, determining a field region based on the field definition data, identifying a subset of the plurality of input data associated with the field region, determining a plurality of field condition data based on the subset of the plurality of input data, identifying a plurality of field activity options, determining a recommendation score for each of the plurality of field activity options based at least in part on the plurality of field condition data, and providing a recommended field activity option from the plurality of field activity options based on the plurality of recommendation scores.

What is claimed is: 
     
         1 . A computer-implemented method for providing an improvement in identifying, using an agricultural intelligence computer system in communication with a processor, a memory and a database, disease risks causing disease damage to crop in agricultural fields and determined based on environmental data, seed-specific data, and field-specific data, the method comprising:
 receiving, by an agricultural intelligence computer system, a first set of data points including environmental information for an agricultural field;   receiving, by the agricultural intelligence computer system, a second set of data points including seed characteristic data for the agricultural field;   receiving, by the agricultural intelligence computer system, a third set of data points including field-specific data comprising planting data for the agricultural field;   receiving, by the agricultural intelligence computer system, a fourth set of data points including field-specific data comprising pesticide data for the agricultural field;   based on, at least in part, the first set of data points, the second set of data points, the third set of data points, and the fourth set of data points, identifying, by the agricultural intelligence computer system, one or more first disease risks posed to crop and that cause disease damage to the agricultural field.   
     
     
         2 . The computer-implemented method of  claim 1 , further comprising: based on, at least in part, the first set of data points, the second set of data points, the third set of data points, and the fourth set of data points, identifying, by the agricultural intelligence computer system, one or more second disease risks posed to crops and that cause economic damage to the agricultural field. 
     
     
         3 . The computer-implemented method of  claim 1 , wherein the environmental information includes information related to weather, precipitation, meteorology, crop phenology, and pest and disease reporting for the agricultural field;
 wherein the seed characteristic data include information related to seeds that are planted or will be planted in the agricultural field;   wherein the seed characteristic data include seed company data, seed cost data, seed population data, seed hybrid data, seed maturity level data, and seed disease resistance data;   wherein the seed disease resistance data include information related to resistance of seeds to particular diseases;   wherein the field-specific data include planting dates, seed type data, relative maturity of planted seed data, and seed population data;   wherein the pesticide data include pesticide application date, pesticide product type data, pesticide formulation data, pesticide usage rate data, pesticide acres tested data, pesticide amount sprayed data, and pesticide source data.   
     
     
         4 . The computer-implemented method of  claim 1 , further comprising:
 determining an initial crop moisture level;   receiving a plurality of daily high and low temperatures;   receiving a plurality of crop water usage;   determining a soil moisture level for a field region; and   recommending a plurality of crops for planting based on the determined soil moisture level.   
     
     
         5 . The computer-implemented method of  claim 1 , further comprising: receiving a plurality of pest risk data wherein each of the plurality of pest risk data includes a pest identifier and a pest location;
 receiving a plurality of crop identifiers associated with a plurality of crops;   receiving a plurality of pest spray information associated with the crop identifiers;   determining a pest risk assessment, of a plurality of pest risk assessments, associated with each of the plurality of crops; and   recommending a spray strategy based on the plurality of pest risk assessments.   
     
     
         6 . The computer-implemented method of  claim 1 , further comprising:
 receiving a plurality of historical agricultural activities associated with each of a field region from a user device; and   providing a recommended field activity option based at least in part on the plurality of historical agricultural activities.   
     
     
         7 . The computer-implemented method of  claim 1 , further comprising: utilizing a grid-based model to obtain localized field condition data. 
     
     
         8 . A networked agricultural intelligence system for providing an improvement in identifying, using an agricultural intelligence computer system in communication with a processor, a memory and a database, disease risks causing disease damage to crop in agricultural fields and determined based on environmental data, seed-specific data, and field-specific data, the networked agricultural intelligence system comprising:
 a plurality of data network computer systems;   an agricultural intelligence computer system comprising a processor and a memory in communication with said processor, said processor configured to perform:   receiving, by an agricultural intelligence computer system, a first set of data points including environmental information for an agricultural field;   receiving, by the agricultural intelligence computer system, a second set of data points including seed characteristic data for the agricultural field;   receiving, by the agricultural intelligence computer system, a third set of data points including field-specific data comprising planting data for the agricultural field;   receiving, by the agricultural intelligence computer system, a fourth set of data points including field-specific data comprising pesticide data for the agricultural field;   based on, at least in part, the first set of data points, the second set of data points, the third set of data points, and the fourth set of data points, identifying, by the agricultural intelligence computer system, one or more first disease risks posed to crop and that cause disease damage to the agricultural field.   
     
     
         9 . The networked agricultural intelligence system in accordance with  claim 8 , wherein the processor is further configured to perform: based on, at least in part, the first set of data points, the second set of data points, the third set of data points, and the fourth set of data points, identifying, by the agricultural intelligence computer system, one or more second disease risks posed to crops and that cause economic damage to the agricultural field. 
     
     
         10 . The networked agricultural intelligence system in accordance with  claim 8 , wherein the environmental information includes information related to weather, precipitation, meteorology, crop phenology, and pest and disease reporting for the agricultural field;
 wherein the seed characteristic data include information related to seeds that are planted or will be planted in the agricultural field;   wherein the seed characteristic data include seed company data, seed cost data, seed population data, seed hybrid data, seed maturity level data, and seed disease resistance data;   wherein the seed disease resistance data include information related to resistance of seeds to particular diseases;   wherein the field-specific data include planting dates, seed type data, relative maturity of planted seed data, and seed population data;   wherein the pesticide data include pesticide application date, pesticide product type data, pesticide formulation data, pesticide usage rate data, pesticide acres tested data, pesticide amount sprayed data, and pesticide source data.   
     
     
         11 . The networked agricultural intelligence system in accordance with  claim 8 , wherein the processor is further configured to perform: determining an initial crop moisture level;
 receiving a plurality of daily high and low temperatures;   receiving a plurality of crop water usage;   determining a soil moisture level for a field region; and   recommending a plurality of crops for planting based on the determined soil moisture level.   
     
     
         12 . The networked agricultural intelligence system in accordance with  claim 8 , wherein the processor is further configured to perform: receiving a plurality of pest risk data wherein each of the plurality of pest risk data includes a pest identifier and a pest location;
 receiving a plurality of crop identifiers associated with a plurality of crops;   receiving a plurality of pest spray information associated with the crop identifiers;   determining a pest risk assessment, of a plurality of pest risk assessments, associated with each of the plurality of crops; and   recommending a spray strategy based on the plurality of pest risk assessments.   
     
     
         13 . The networked agricultural intelligence system in accordance with  claim 8 , wherein the processor is further configured to perform: receiving a plurality of historical agricultural activities associated with each of a field region from a user device; and
 providing a recommended field activity option based at least in part on the plurality of historical agricultural activities.   
     
     
         14 . The networked agricultural intelligence system in accordance with  claim 8 , wherein the processor is further configured to: utilizing a grid-based model to obtain localized field condition data. 
     
     
         15 . Non-transitory computer-readable storage media for providing an improvement in recommending agricultural activities determined based on crop-related data and field condition data, the non-transitory computer-readable storage media storing computer-executable instructions embodied thereon, wherein, when executed by at least one processor, the computer-executable instructions cause the processor to perform:
 receiving, by an agricultural intelligence computer system, a first set of data points including environmental information for an agricultural field;   receiving, by the agricultural intelligence computer system, a second set of data points including seed characteristic data for the agricultural field;   receiving, by the agricultural intelligence computer system, a third set of data points including field-specific data comprising planting data for the agricultural field;   receiving, by the agricultural intelligence computer system, a fourth set of data points including field-specific data comprising pesticide data for the agricultural field;   based on, at least in part, the first set of data points, the second set of data points, the third set of data points, and the fourth set of data points, identifying, by the agricultural intelligence computer system, one or more first disease risks posed to crop and that cause disease damage to the agricultural field.   
     
     
         16 . The computer-readable storage media in accordance with  claim 15 , wherein the computer-executable instructions cause the processor to perform: based on, at least in part, the first set of data points, the second set of data points, the third set of data points, and the fourth set of data points, identifying, by the agricultural intelligence computer system, one or more second disease risks posed to crops and that cause economic damage to the agricultural field. 
     
     
         17 . The computer-readable storage media in accordance with  claim 15 , wherein the environmental information includes information related to weather, precipitation, meteorology, crop phenology, and pest and disease reporting for the agricultural field;
 wherein the seed characteristic data include information related to seeds that are planted or will be planted in the agricultural field;   wherein the seed characteristic data include seed company data, seed cost data, seed population data, seed hybrid data, seed maturity level data, and seed disease resistance data;   wherein the seed disease resistance data include information related to resistance of seeds to particular diseases;   wherein the field-specific data include planting dates, seed type data, relative maturity of planted seed data, and seed population data;   wherein the pesticide data include pesticide application date, pesticide product type data, pesticide formulation data, pesticide usage rate data, pesticide acres tested data, pesticide amount sprayed data, and pesticide source data.   
     
     
         18 . The computer-readable storage media in accordance with  claim 15 , wherein the computer-executable instructions cause the processor to perform: determining an initial crop moisture level;
 receiving a plurality of daily high and low temperatures;   receiving a plurality of crop water usage;   determining a soil moisture level for a field region; and   recommending a plurality of crops for planting based on the determined soil moisture level.   
     
     
         19 . The computer-readable storage media in accordance with  claim 15 , wherein the computer-executable instructions cause the processor to perform: receiving a plurality of pest risk data wherein each of the plurality of pest risk data includes a pest identifier and a pest location;
 receiving a plurality of crop identifiers associated with a plurality of crops;   receiving a plurality of pest spray information associated with the crop identifiers;   determining a pest risk assessment, of a plurality of pest risk assessments, associated with each of the plurality of crops; and   recommending a spray strategy based on the plurality of pest risk assessments.   
     
     
         20 . The computer-readable storage media in accordance with  claim 15 , wherein the computer-executable instructions cause the processor to perform: receiving a plurality of historical agricultural activities associated with each of a field region from a user device; and
 providing a recommended field activity option based at least in part on the plurality of historical agricultural activities.