A computer-implemented way of recommending agricultural activities is executed through an agricultural intelligence computer system in communicating with a memorycard. The method includes receiving a plurality of discipline definition information, retrieving a plurality of input information in the plurality of information networks, ascertaining a field region based on the field definition information, identifying a subset of the plurality of input information related to the field region, determining a plurality of field condition data based on the subset of the plurality of input information, identifying a plurality of field action options, determining a recommendation score for each of the plurality of field action choices based at least in part on the plurality of field state information, and supplying a recommended field action choice from the plurality of field action options based on the plurality of recommendation scores.


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The embodiments described herein relate generally to agricultural pursuits and, more particularly, systems and methods for managing and recommending agricultural activities in the field level based on crop-related information and field-conditiondata.

Agricultural production requires significant strategy and analysis. Oftentimes, agricultural growers (e.g., farmers or other people involved with agricultural cultivation) need to analyze a number of information to make strategic decisions monthsin progress of the period of harvest cultivation (i.e., growing season). In making such strategic choices, growers must consider some of the next decision constraints: fuel and resource costs, historical and projected weather tendencies, soilconditions, projected dangers posed by pests, disease and weather events, and projected market worth of agricultural commodities (i.e., plants ). Analyzing these decision constraints may assist a grower to predict crucial agricultural effects such as cropyield, energy usage, cost and resource usage, and farm profitability. Such investigation may inform a grower’s strategic decisions of determining crop farming forms, procedures, and timing.

Despite its significance, this type of strategy and analysis is difficult to accomplish for a variety of reasons. First, getting reliable information for the numerous factors of this grower is often difficult. Secondly, aggregating such informationinto a usable fashion is a time consuming task. Third, where information is available, it may not be precise enough to be helpful to determine strategy. By way of instance, weather information (historical or projected) is often generalized for a large region such as a countyor a state. In fact, weather may vary significantly at a much more granular level, such as an individual discipline. Additionally, terrain features may lead to weather data to vary significantly in even small regions.

Additionally, farmers often need to regularly make decisions during growing season. Such decisions may include adjusting when to crop, providing supplemental fertilizer, and also how to mitigate risks posed by pests, disease and weather. As aresult, growers need to always monitor a variety of aspects of their plants during the growing season including soil, weather, and crop conditions. Accurately tracking all such facets in a granular level is hard and time consuming. Thus,methods and systems for assessing crop-related data and providing field condition data and tactical recommendations for optimizing crop yield are needed.

IP reviewed by Plant-Grow agriculture technology news