This invention relates to a horticulture additive comprising: (1) a wetting agent, wherein the wetting agent is a multi-branched polymer comprising at least one of an oxygen-containing and a nitrogen-containing polyfunctional base compound having at least three branches attached thereto, and (2) a humectant. The horticulture additive is advantageous for increasing plant survival of plants treated therewith when exposed to reduced watering conditions.

This invention relates to a horticulture additive comprising: (1) a wetting agent, wherein the wetting agent is a multi-branched polymer comprising at least one of an oxygen-containing and a nitrogen-containing polyfunctional base compound having at least three branches attached thereto, and (2) a humectant. The horticulture additive is advantageous for increasing plant survival of plants treated therewith when exposed to reduced watering conditions.

Described are methods of grain production with field conditioned pollen. A method of the present invention includes growing one or more designated female plants that include both female and male components, collecting fresh pollen from designated male plants from a different genetic background, and subjecting the fresh pollen to field conditions, which may regulate pollen moisture content. The field conditioning conditions may include a relative humidity ranging from about 50% to about 100%, a temperature ranging from about −10-10° C., and an air pressure ranging from about 15 kPa to about 150 kPa. These conditions may result in pollen having a moisture content of about 40% to about 58%. The one or more designated female plants are intentionally cross-pollinated with the field conditioned pollen and then grown to maturity such that the grain may be harvested therefrom. Also provided is a method of preventing undesirable pollination in grain production.

Described are methods of grain production with field conditioned pollen. A method of the present invention includes growing one or more designated female plants that include both female and male components, collecting fresh pollen from designated male plants from a different genetic background, and subjecting the fresh pollen to field conditions, which may regulate pollen moisture content. The field conditioning conditions may include a relative humidity ranging from about 50% to about 100%, a temperature ranging from about −10-10° C., and an air pressure ranging from about 15 kPa to about 150 kPa. These conditions may result in pollen having a moisture content of about 40% to about 58%. The one or more designated female plants are intentionally cross-pollinated with the field conditioned pollen and then grown to maturity such that the grain may be harvested therefrom. Also provided is a method of preventing undesirable pollination in grain production.

Systems and methods for neural network forecasting for tiered hydroponic farming configurations are disclosed. In embodiments, a computer-implemented method includes: determining, by a computing device, ecology factors associated with a plant of interest; determining, by the computing device, beneficial interdependencies of the ecology factors associated with the plant of interest using an artificial neural network system; and generating, by the computing device, an ecology framework for the plant of interest within a cultivation system based on the beneficial interdependencies of the ecology factors, wherein the ecology framework reflects ecological conditions desired to promote healthy growth of the plant of interest.

One variation of a method for interpreting pressures in plants includes: accessing a first image of a first set of sentinel plants in a field; accessing a second image of a second set of sentinel plants in the field, recorded during a first period; interpreting a first pressure of a stressor in the first set based on features extracted from the first image, captured during the first period; interpreting a second pressure in the second set based on features extracted from the second image; deriving a model associating pressure at the first set and pressure at the second set based on the first pressure and the second pressure; interpreting a third pressure in the first set based on features extracted from a third image captured during a second period; and predicting a fourth pressure in the second set during the second period based on the third pressure and the model.

An electro-mechanical device cuts plant stems at a desired angle after a stem is inserted into a stem shaft and a sensor triggers activation of a blade along a blade path at a desired angle. A portable device powered by batteries can be used with plantings where they are growing or a powered device can be used for line production or high volume work.

An electro-mechanical device cuts plant stems at a desired angle after a stem is inserted into a stem shaft and a sensor triggers activation of a blade along a blade path at a desired angle. A portable device powered by batteries can be used with plantings where they are growing or a powered device can be used for line production or high volume work.

An agricultural plot evaluation method, computer program product and system are provided. A processor receives a plot for evaluation. A processor determines one or more characteristics associated with the plot for evaluation. A processor identifies a plurality of similar plots based on at least one of the one or more characteristics associated with the plot for evaluation. A processor generates a prediction model for one or more features of the plurality of similar plots. A processor determines a score for the plot for evaluation based, at least in part, on the prediction model for one or more features of the plurality of similar plots.

An agricultural plot evaluation method, computer program product and system are provided. A processor receives a plot for evaluation. A processor determines one or more characteristics associated with the plot for evaluation. A processor identifies a plurality of similar plots based on at least one of the one or more characteristics associated with the plot for evaluation. A processor generates a prediction model for one or more features of the plurality of similar plots. A processor determines a score for the plot for evaluation based, at least in part, on the prediction model for one or more features of the plurality of similar plots.