Abstract: Methods are provided for improving performance of a computing system used to model potential crop yield. In one example embodiment, a computer-implemented method includes generating a model of potential crop yield, as a function of planting date and relative maturity based, at least in part, on one or more relative maturity maps, one or more planting date maps, and one or more actual production history maps, and storing the model in a memory of the server computer system. The method also includes receiving, via an interface at a field manager computing device, a selection of a particular field and computing, from the model of potential crop yield, a potential yield for the particular field based, at least in part, on a planting date for the particular field, a relative maturity value, and values representing actual production history for the particular field.

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, over a network, field definition data and environmental data; determining soil data from at least the environmental data; determining, based on at least the soil data, carbon load data indicating carbon load present in the field during a planting date; receiving first input indicating a plurality of starter application criteria; based on the first input and the carbon load present in the field, generating application data that include at least an amount of the starter fertilizer that provides nutrients to seeds throughout a nutrient immobilization period between the planting date and a beginning of a microorganism mineralization period; generating control instructions for controlling an agricultural implement based on the application data.

Abstract: Systems and methods are provided for managing agricultural activities in a field region. In one example, a computer-implemented method includes identifying temperature grids for the field region, and identifying weather stations for the temperature grids. Each weather station is located at a weather station location in the temperature grids. The computer-implemented method also includes computing weight values based on the weather station locations of the weather stations, such that the weather stations that are more proximate to their respective grids have higher weights than weather stations that are less proximate to their respective grids, and receiving temperature readings from the weather stations. The computer-implemented method then includes computing a plurality of weighted temperatures based on the weight values, computing field condition data for the field region based on the weighted temperatures, and determining at least one field activity for the field region based on the field condition data.

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.

Abstract: A method for improving food-related personalized for a user including determining food-related preferences associated with a plurality of users to generate a user food preferences database; collecting dietary inputs from a subject matter expert (SME) at an SME interface associated with the user food preferences database; determining personalized food parameters for the user based on the user food-related preferences and the dietary inputs; receiving feedback associated with the personalized food parameters from the user; and updating the user food preferences database based on the feedback.

Abstract: Systems and methods for improving food-related personalization for a user including generating a recipe database including a set of recipe data structures; deriving a recipe vector representation of the recipe data structures; determining a set of user food preferences; extracting a set of recipe vector constraints from the set of user food preferences; determining a personalized food plan for the user, including automatically selecting a subset of the set of recipe data structures associated with recipe vector representations that satisfy the set of recipe vector constraints; determining fulfillment parameters for grocery items associated with the personalized food plan; and automatically facilitating fulfillment of grocery items associated with the personalized food plan based on the fulfillment parameters.

Abstract: Methods are provided for improving performance of a computing system used to model potential crop yield. In one example embodiment, a computer-implemented method includes generating a model of potential crop yield, as a function of planting date and relative maturity based, at least in part, on one or more relative maturity maps, one or more planting date maps, and one or more actual production history maps, and storing the model in a memory of the server computer system. The method also includes receiving, via an interface at a field manager computing device, a selection of a particular field and computing, from the model of potential crop yield, a potential yield for the particular field based, at least in part, on a planting date for the particular field, a relative maturity value, and values representing actual production history for the particular field.

Abstract: A method for generating digital models of potential crop yield based on planting date, relative maturity, and actual production history is provided. In an embodiment, data representing historical planting dates, relative maturity values, and crop yield is received by an agricultural intelligence computer system. Based on the historical data, the system generates spatial and temporal maps of planting dates, relative maturity, and actual production history. Using the maps, the system creates a model of potential yield that is dependent on planting date and relative maturity. The system may then receive actual production history data for a particular field. Using the received actual production history data, a particular planting date, and a particular relative maturity value, the agricultural intelligence computer system computes a potential yield for a particular field.

Abstract: A method and system for modeling trends in crop yields is provided. In an embodiment, the method comprises receiving, over a computer network, electronic digital data comprising yield data representing crop yields harvested from a plurality of agricultural fields and at a plurality of time points; in response to receiving input specifying a request to generate one or more particular yield data; determining one or more factors that impact yields of crops that were harvested from the plurality of agricultural fields; decomposing the yield data into decomposed yield data that identifies one or more data dependencies according to the one or more factors; generating, based on the decomposed yield data, the one or more particular yield data; generating forecasted yield data or reconstructing the yield data by incorporating the one or more particular yield data into the yield data.

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.

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, over a network, field definition data and environmental data; determining soil data from at least the environmental data; determining, based on at least the soil data, carbon load data indicating carbon load present in the field during a planting date; receiving first input indicating a plurality of starter application criteria; based on the first input and the carbon load present in the field, generating application data that include at least an amount of the starter fertilizer that provides nutrients to seeds throughout a nutrient immobilization period between the planting date and a beginning of a microorganism mineralization period; generating control instructions for controlling an agricultural implement based on the application data.

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.

Abstract: A method for generating digital models of potential crop yield based on planting date, relative maturity, and actual production history is provided. In an embodiment, data representing historical planting dates, relative maturity values, and crop yield is received by an agricultural intelligence computer system. Based on the historical data, the system generates spatial and temporal maps of planting dates, relative maturity, and actual production history. Using the maps, the system creates a model of potential yield that is dependent on planting date and relative maturity. The system may then receive actual production history data for a particular field. Using the received actual production history data, a particular planting date, and a particular relative maturity value, the agricultural intelligence computer system computes a potential yield for a particular field.

Abstract: Systems and method for computing yield values through a neural network from a plurality of different data inputs are disclosed. In an embodiment, a server computer system receives a particular dataset relating to one or more agricultural fields wherein the particular data set comprises particular crop identification data, particular environmental data, and particular management practice data. Using a first neural network, the server computer system computes a crop identification effect on crop yield from the particular crop identification data. Using a second neural network, the server computer system computes an environmental effect on crop yield from the particular environmental data. Using a third neural network, the server computer system computes a management practice effect on crop yield from the management practice data.

Abstract: A method for improving food-related personalized for a user including determining food-related preferences associated with a plurality of users to generate a user food preferences database; collecting dietary inputs from a subject matter expert (SME) at an SME interface associated with the user food preferences database; determining personalized food parameters for the user based on the user food-related preferences and the dietary inputs; receiving feedback associated with the personalized food parameters from the user; and updating the user food preferences database based on the feedback.

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.

Abstract: Systems and methods for improving food-related personalization for a user including generating a recipe database including a set of recipe data structures; deriving a recipe vector representation of the recipe data structures; determining a set of user food preferences; extracting a set of recipe vector constraints from the set of user food preferences; determining a personalized food plan for the user, including automatically selecting a subset of the set of recipe data structures associated with recipe vector representations that satisfy the set of recipe vector constraints; determining fulfillment parameters for grocery items associated with the personalized food plan; and automatically facilitating fulfillment of grocery items associated with the personalized food plan based on the fulfillment parameters.

Abstract: Systems and methods for improving food-related personalization for a user including generating a recipe database including a set of recipe data structures; deriving a recipe vector representation of the recipe data structures; determining a set of user food preferences; extracting a set of recipe vector constraints from the set of user food preferences; determining a personalized food plan for the user, including automatically selecting a subset of the set of recipe data structures associated with recipe vector representations that satisfy the set of recipe vector constraints; determining fulfillment parameters for grocery items associated with the personalized food plan; and automatically facilitating fulfillment of grocery items associated with the personalized food plan based on the fulfillment parameters.

Abstract: A method for improving food-related personalized for a user including determining food-related preferences associated with a plurality of users to generate a user food preferences database; collecting dietary inputs from a subject matter expert (SME) at an SME interface associated with the user food preferences database; determining personalized food parameters for the user based on the user food-related preferences and the dietary inputs; receiving feedback associated with the personalized food parameters from the user; and updating the user food preferences database based on the feedback.

Abstract: Systems and method for computing yield values through a neural network from a plurality of different data inputs are disclosed. In an embodiment, a server computer system receives a particular dataset relating to one or more agricultural fields wherein the particular data set comprises particular crop identification data, particular environmental data, and particular management practice data. Using a first neural network, the server computer system computes a crop identification effect on crop yield from the particular crop identification data. Using a second neural network, the server computer system computes an environmental effect on crop yield from the particular environmental data. Using a third neural network, the server computer system computes a management practice effect on crop yield from the management practice data.