Field-specific sclerotinia risk assessment

A computer-implemented method for training a data model to output a disease probability value, the disease probability value being indicative of a probability of an infestation of crop plants of a specifiable field with Sclerotinia sp. fungi and being determined from a field condition data set, comprising the steps:

• providing program structure of a data model configured to output a disease probability value determined from a field condition data set, wherein said disease probability value is indicative of a probability of an infestation of crop plants of a specifiable field with said Sclerotinia sp. fungi;
• generating multiple training data sets, each training data set including one field condition data set and an associated disease indication probability value, wherein each field condition data set is indicative of a condition of a specifiable field and comprises at least one of the following field condition indicators:
• a) a seeding rate indicator indicative of a seeding rate of the field,
• b) a tillage depth indicator indicative of a tillage depth of the field,
• c) a disease history indicator indicative of a history of said Sclerotinia sp. fungi of the field,
• d) a crop rotation history indicator indicative of a crop rotation history of the field,
• e) a planting date indicator indicative of a planting date of the field, and/or
• f) a field geolocation indicator indicative of centroid coordinates of the field;
• training the data model by processing the generated training data sets, wherein a match between a disease probability value outputted in response to a query and the disease indication probability value associated with the queried field condition data set is used as a training objective.

A field condition data set indicative of a condition of a specifiable field, wherein the field condition data set comprises at least one of the following field condition indicators:

• a) a seeding rate indicator indicative of a seeding rate of the field,
• b) a tillage depth indicator indicative of a tillage depth of the field,
• c) a disease history indicator indicative of a history of said Sclerotinia sp. fungi of the field,
• d) a crop rotation history indicator indicative of a crop rotation history of the field,
• e) a planting date indicator indicative of a planting date of the field, and/or
• f) a field geolocation indicator indicative of centroid coordinates of the field,
• wherein the field condition data set optionally further comprises at least one of the following field condition indicators:
• g) a row spacing indicator indicative of a crop row spacing of the field,
• h) a soil texture indicator indicative of a soil texture of the field,
• i) a crop varietal indicator indicative of a variety and/or maturity rating of the crop plants of the field,
• k) a precipitation indicator indicative of a liquid accumulation of the field on a daily basis,
• l) at least one air temperature indicator indicative of a characteristic value from an air temperature course of the field on a daily basis, the characteristic value optionally being a minimum air temperature of the field on a daily basis, a mean air temperature of the field on a daily basis, and/or a maximum air temperature of the field on a daily basis,
• m) at least one relative humidity indicator indicative of a characteristic value from a relative humidity course of the field on a daily basis, the characteristic value optionally being a minimum relative humidity of the field on a daily basis, a mean relative humidity of the field on a daily basis, and/or a maximum relative humidity of the field on a daily basis,
• n) a wind speed indicator indicative of an average wind speed of the field on a daily basis,
• o) a solar radiation indicator indicative of a solar radiation flux of the field on a daily basis,
• p) a soil moisture indicator indicative of a moisture in an upper soil layer of the field on a daily basis,
• q) a crop species indicator indicative of a current crop species of the field,
• r) a crop phenology indicator indicative of a growth stage according to a phenology model of the crop plants of the field on a daily basis,
• s) a canopy density indicator indicative of a canopy density of the field, and/or
• t) a crop biomass indicator indicative of a stand density of the field.

A computer-implemented method for determining a disease probability value, that is indicative of a probability of an infestation of crop plants of a specifiable field with Sclerotinia sp. fungi, comprising:

• generating a field condition data set indicative of a condition of a specifiable field, wherein a field condition data set comprises at least one of the following field condition indicators:
• a) a seeding rate indicator indicative of a seeding rate of the field,
• b) a tillage depth indicator indicative of a tillage depth of the field,
• c) a disease history indicator indicative of a history of said Sclerotinia sp. fungi of the field,
• d) a crop rotation history indicator indicative of a crop rotation history of the field,
• e) a planting date indicator indicative of a planting date of the field, and/or
• f) a field geolocation indicator indicative of centroid coordinates of the field;
• providing a trained data model, which is configured to output a disease probability value determined from a field condition data set, wherein said disease probability value is indicative of a probability of an infestation of crop plants of a specifiable field with said Sclerotinia sp. fungi,
• wherein the data model is obtainable by previous training with multiple training data sets, each training data set including one of said field condition data sets and an associated disease indication probability value;
• and
• determining by the data model a disease probability value from the generated field condition data set.