Assessment of moisture content of stored crop, and modeling usage of in-bin drying to control moisture level based on anticipated atmospheric conditions and forecast time periods of energy usage to achieve desired rate of grain moisture change through forced-air ventilation

{
  "publication_number": "US9518753B2",
  "country": "US",
  "kind": "B2",
  "title": "Assessment of moisture content of stored crop, and modeling usage of in-bin drying to control moisture level based on anticipated atmospheric conditions and forecast time periods of energy usage to achieve desired rate of grain moisture change through forced-air ventilation",
  "abstract": "A modeling framework for evaluating the impact of weather conditions on farming and harvest operations applies real-time, field-level weather data and forecasts of meteorological and climatological conditions together with user-provided and/or observed feedback of a present state of a harvest-related condition to agronomic models and to generate a plurality of harvest advisory outputs for precision agriculture. A harvest advisory model simulates and predicts the impacts of this weather information and user-provided and/or observed feedback in one or more physical, empirical, or artificial intelligence models of precision agriculture to analyze crops, plants, soils, and resulting agricultural commodities, and provides harvest advisory outputs to a diagnostic support tool for users to enhance farming and harvest decision-making, whether by providing pre-, post-, or in situ-harvest operations and crop analyses.",
  "claims": [
    "1. A method comprising: ingesting, as input data, weather information, crop-specific information for a stored grains crop, and grain storage facility information, the weather information including recent and current field-level weather data and extended-range weather forecast data, the crop-specific information including at least one of crop type data, crop post-maturity dry-down characteristics, crop airflow characteristics, and targeted crop moisture or temperature thresholds, and the grain storage facility information including at least one of grain storage facility dimensions data and grain storage facility airflow data; modeling the input data in a plurality of data processing modules within a computing environment in which the plurality of data processing modules are executed in conjunction with at least one processor, the data processing modules configured to profile moisture content of the stored grains crop relative to impacts of time-varying weather conditions, in-bin drying characteristics, and existing crop moisture conditions on an equilibrium moisture content of a grains crop stored in a grain storage facility, by 1) diagnosing and predicting expected weather conditions impacting energy requirements for in-bin drying of the stored grains crop at a specific location, 2) applying the expected weather conditions, the crop-specific information, and the grain storage facility information to an agricultural model of one or more physical and empirical characteristics impacting a rate of forced-air ventilation of the stored grains crop to simulate time-varying moisture content of the stored grains crop at specific times and predict a rate of forced-air ventilation for the stored grains crop at the specific location over time, and 3) applying facility metadata representing actual and/or realized performance characteristics of the grain storage facility, the expected weather conditions, the crop-specific information, and the grain storage facility information to an artificial intelligence model of drying a harvested grain crop to automatically develop a specific energy usage cost model by building a comprehensive dataset of one or more physical and empirical characteristics impacting the rate of forced-air ventilation of the stored grains crop; and generating, as output data, a rate of forced-air ventilation to achieve a desired moisture content in a harvest output condition profile.",
    "2. The method of claim 1, further comprising applying the harvest output condition profile to a harvest decision support tool configured to provide one or more advisories representing the rate of forced-air ventilation to achieve a desired moisture content to a user.",
    "3. The method of claim 2, wherein the decision support tool generates an advisory representing a forecast of energy usage time periods to achieve a particular rate of grain moisture change through forced-air ventilation.",
    "4. The method of claim 3, wherein the decision support tool generates an advisory representing an indicator of recommended operation of in-bin storage fans to achieve the particular rate of grain moisture change through forced-air ventilation.",
    "5. The method of claim 4, wherein the decision support tool generates an advisory representing an indicator of expected energy costs to achieve the desired moisture content.",
    "6. The method of claim 1, wherein the modeling further comprises applying the weather information to one or more predictive numerical weather models to generate the prediction of expected weather conditions.",
    "7. The method of claim 1, wherein the agricultural model of one or more physical and empirical characteristics impacting a rate of forced-air ventilation of the stored grains crop includes at least one of deterministic and stochastic grain drying models based on at least one of characteristics of the drying process and statistical models that parameterize the drying characteristics of a specific crop to be dried.",
    "8. A system comprising: a computing environment including at least one computer-readable storage medium having program instructions stored therein and a computer processor operable to execute the program instructions to profile moisture content of a stored grains crop by performing a harvest advisory model within a plurality of data processing modules, the plurality of data processing modules including: a weather modeling module configured to predict expected weather conditions at a specific location by applying weather information including recent and current field-level weather data and extended-range weather forecast data to one or more predictive numerical weather models; one or more modules configured to 1) aggregate the expected weather conditions with crop-specific information and grain storage facility information, 2) simulate time-varying moisture content of the stored grains crop at specific times in an agricultural model of one or more physical and empirical characteristics impacting a rate of forced-air ventilation for the stored grains crop, and 3) automatically develop an artificial intelligence model of drying a harvested grain crop to analyze a specific energy usage cost model by building a comprehensive dataset of one or more physical and empirical characteristics impacting the rate of forced-air ventilation for the stored grains crop, wherein the comprehensive dataset for the agricultural model of one or more physical and empirical characteristics impacting the rate of forced-air ventilation for the stored grains crop is built by aggregating facility metadata representing actual and/or realized performance characteristics of the grain storage facility, the expected weather conditions, the crop-specific information, and the grain storage facility information; and a harvest condition prediction module configured to predict a rate of forced-air ventilation for the stored grains crop at the specific location over time for the stored grains crop at the specific location over time in a harvest output condition profile for planning energy consumption from usage of in-bin mechanical aeration components to control moisture content in a stored grains crop.",
    "9. The system of claim 8, wherein the harvest output condition profile is applied to a diagnostic support tool configured to provide one or more advisories representing the rate of forced-air ventilation to achieve a desired moisture content to a user.",
    "10. The system of claim 9, wherein the diagnostic support tool generates an advisory representing an indicator of recommended operation of in-bin storage fans to achieve a particular rate of grain moisture change through forced-air ventilation.",
    "11. The system of claim 10, wherein the diagnostic support tool generates an advisory representing a forecast of energy usage time periods to achieve the particular rate of grain moisture change.",
    "12. The system of claim 11, wherein the diagnostic support tool generates an advisory representing an indicator of expected energy costs to achieve the desired moisture content.",
    "13. The system of claim 8, wherein the crop-specific information includes at least one of crop type data, crop post-maturity dry-down characteristics, crop airflow characteristics, and targeted crop moisture or temperature thresholds, and the grain storage facility information includes at least one of grain storage facility dimensions data and grain storage facility airflow data.",
    "14. A method of evaluating moisture content of a stored grains crop, comprising: within a computing environment comprised of a computer processor and at least one computer-readable storage medium operably coupled to the computer processor and having program instructions stored therein, the computer processor being operable to execute the program instructions to profile moisture content of a stored grains crop in a harvest advisory model configured to perform the steps of: diagnosing and predicting expected weather conditions impacting the moisture content of the stored grains crop in a crop storage facility and energy requirements for in-bin drying of the stored grains crop at a specific location by applying weather information comprised of recent and current field-level weather data and extended-range weather forecast data to one or more predictive numerical weather models; simulating time-varying moisture content of the stored grains crop at specific times in an agricultural model of one or more physical and empirical characteristics impacting a rate of forced-air ventilation for the stored grains crop by aggregating the expected weather conditions with crop-specific information and grain storage facility information, the agricultural model comprising at least one of deterministic and stochastic grain drying models based on at least one of characteristics of a drying process and statistical models that parameterize drying characteristics of a specific crop to be stored; identifying differences between the simulation of time-varying moisture content and one or more sampled observations of actual moisture content representing existing moisture conditions from the grain storage facility at corresponding times; modifying the simulation based on the identified differences between the otherwise simulated time-varying moisture content and the one or more sampled observations of actual moisture content at the corresponding times to generate a predicted rate of forced-air ventilation for the stored grains crop at the grain storage facility over time; and applying facility metadata representing actual and/or realized performance characteristics of the grain storage facility and the expected weather conditions, the crop-specific information, and the grain storage facility information, to an artificial intelligence model of drying a harvested grain crop to automatically develop a specific energy usage cost model by building a comprehensive dataset of one or more physical and empirical characteristics impacting the rate of forced-air ventilation for the stored grains crop.",
    "15. The method of claim 14, further comprising generating one or more advisories of the predicted rate of forced-air ventilation in a harvest output condition profile.",
    "16. The method of claim 15, further comprising applying the harvest output condition profile to a diagnostic support tool configured to provide the one or more advisories representing the predicted rate of forced-air ventilation to achieve a desired moisture content to users performing harvest operations.",
    "17. The method of claim 16, wherein the diagnostic support tool generates an advisory representing an indicator of recommended operation of in-bin storage fans to achieve a particular rate of grain moisture change through forced-air ventilation.",
    "18. The method of claim 17, wherein the diagnostic support tool generates an advisory representing a forecast of energy usage time periods to achieve the particular rate of grain moisture change through forced-air ventilation.",
    "19. The method of claim 18, wherein the diagnostic support tool generates an advisory representing an indicator of expected energy costs to achieve the desired moisture content.",
    "20. The method of claim 14, the crop-specific information includes at least one of crop type data, crop post-maturity dry-down characteristics, crop airflow characteristics, and targeted crop moisture or temperature thresholds, and the grain storage facility information includes at least one of grain storage facility dimensions data and grain storage facility airflow data.",
    "21. The method of claim 16, wherein the one or more sampled observations of actual moisture content representing existing moisture conditions at corresponding times includes physical, empirical or observed agricultural information that comprises sampled actual moisture content from a bin where the grains crop is stored."
  ],
  "description_excerpt": "The present invention relates to harvest operations in precision agriculture. Specifically, the present invention relates to a system and method of applying real-time, field-level weather simulation and prediction to one or more agricultural models to generate a series of harvest advisory outputs in a tool for supporting farm operations management.\n\nHarvest operations for a variety of agricultural commodities are substantially influenced by environmental factors, such as the weather. While some weather conditions, such as precipitation, may create an obvious deterrent to harvest operations, more benign daily weather characteristics also play subtle yet significant roles. For example, many commodities require that the harvested product be at or below a product-dependent moisture threshold before they can be stably stored at ambient temperatures (at least without taking specific steps to keep the product stable, such as the maintenance of a constant airflow through the product). On the other extreme, delaying harvest for too long can result in the crop becoming overly dry, potentially exposing seeds to damage during the threshing process, or removing permissible water weight from the product. Such an occurrence of delayed harvest may result in lower crop revenue, since payments are often based on mass. Similarly, crop temperature thresholds may be a major consideration for long-term storage of some crops, for example tuberous crops such as potatoes and sugar beets, where cold conditions are advantageous.",
  "cpc": [
    "G06N 20/00",
    "A01B 79/005",
    "A01D 91/00",
    "A01F 12/58",
    "A01F 25/22",
    "A01G 1/001",
    "A01G 25/167",
    "F24F 11/006",
    "F24F 11/62",
    "G01W 1/10",
    "G05B 13/048",
    "G06F 30/20",
    "G06N 5/04",
    "G06N 99/00",
    "G06Q 50/02",
    "Y02A 40/10",
    "Y02A 90/10"
  ],
  "ipc": [
    "F24F 11/00",
    "G05B 13/04",
    "G06N 20/00",
    "G06Q 50/02",
    "A01B 79/00",
    "A01G 1/00",
    "A01G 25/16"
  ],
  "assignees": [
    "Iteris Inc"
  ],
  "inventors": [
    "John J. Mewes",
    "Dustin M. Salentiny"
  ],
  "filing_date": "2015-09-02",
  "publication_date": "2016-12-13",
  "grant_date": "2016-12-13",
  "priority_date": "2015-01-23",
  "application_number": "US-201514842853-A",
  "family_id": "54107005",
  "cited_by_count": 9,
  "citations": [
    "US5884225A",
    "US20140012732A1",
    "US20130173321A1"
  ]
}