An agricultural implement for distributing fertilizer to a plurality of rows includes a fertilizer container, a plurality of fertilizer metering units, and a plurality of motors. The fertilizer containers are used for storing the fertilizer prior to the fertilizer being distributed to the soil. The plurality of fertilizer metering units each include a housing and a metering device positioned in the housing. The housing has an inlet configured to receive fertilizer from the fertilizer container and an outlet. The plurality of motors are each drivingly coupled to and configured to rotate at least one metering device to distribute fertilizer to a row of the plurality of rows.

An agricultural implement for distributing fertilizer to a plurality of rows includes a fertilizer container, a plurality of fertilizer metering units, and a plurality of motors. The fertilizer containers are used for storing the fertilizer prior to the fertilizer being distributed to the soil. The plurality of fertilizer metering units each include a housing and a metering device positioned in the housing. The housing has an inlet configured to receive fertilizer from the fertilizer container and an outlet. The plurality of motors are each drivingly coupled to and configured to rotate at least one metering device to distribute fertilizer to a row of the plurality of rows.

A system and method to distribute pesticides, fertilizers, water, and other materials on a farm with accuracy and precision is disclosed in order to combat the problems imposed on the environment due to over-fertilization and over use of pesticides. This system and method is a social networking control system in which multiple farms have independent grids of sensors capable of detecting the presence of pesticides, fertilizers, water, and other materials in the air, in the top-soil, and in the groundwater. These grids of sensors detect the location and concentration of these materials and reports them back to a social control system for analysis. The control system regulates the deposition of further chemicals through computer control of the chemical dispersal systems.

The present disclosure relates to the field of ecological restoration of a coal gangue hill, and in particular, to a solid waste-based porous material, a method for preparing the solid waste-based porous material, and a method of ecological restoration of the coal gangue hill by applying the solid waste-based porous material. A coal-based solid waste restoration material and mycorrhizal solid bacterial agent are mixed to restore the coal gangue hill, the coal-based solid waste restoration material is prepared by mixing coal-based solid waste porous materials, low-rank coal, and waste organic matter and adding a microbial quickly decomposition agent for aerobic fermentation and standing.

The present disclosure relates to the field of ecological restoration of a coal gangue hill, and in particular, to a solid waste-based porous material, a method for preparing the solid waste-based porous material, and a method of ecological restoration of the coal gangue hill by applying the solid waste-based porous material. A coal-based solid waste restoration material and mycorrhizal solid bacterial agent are mixed to restore the coal gangue hill, the coal-based solid waste restoration material is prepared by mixing coal-based solid waste porous materials, low-rank coal, and waste organic matter and adding a microbial quickly decomposition agent for aerobic fermentation and standing.

A computing system includes a processor and a non-transitory, computer-readable media including instructions that, when executed by the one or more processors, cause the computing system to access an initial machine data set; label the machine data set; process the labeled machine data set; and modify one or more parameters of the machine-learned model. A method includes accessing an initial machine data set; labeling the machine data set; processing the labeled machine data set; and modifying one or more parameters of the machine-learned model. A computing system for predicting a variety profile index includes a processor; and a non-transitory, computer-readable media including a trained machine-learned model; and instructions that, when executed by the one or more processors, cause the computing system to process a second machine data set to generate one or more predicted variety profile index values; and provide the one or more predicted variety profile index values.

A method including adjusting a changeable component of a seed planting machine when switching from a first variety of seed to a second variety of seed during planting, wherein the adjusting is based on a location of the planting machine.

A computer system and computer-implemented techniques for determining and presenting improved seeding rate recommendations for planting seeds in a field are provided. In an embodiment, a computer-implemented method includes receiving digital data representing planting parameters including seed type information and planting row width, and retrieving a set of seeding models based upon the planting parameters, where each of the seeding models includes a regression model defining a relationship between plant yield and seeding rate on a specific field. The method also includes generating an empirical mixture model as a composite distribution of the set of seeding models, generating a seeding rate distribution for the planting parameters based upon the empirical mixture model, and calculating a seeding rate recommendation based on the seed rate distribution. The method then also includes planting plant seeds in the specific field consistent with the seeding rate recommendation.

A computer system and computer-implemented techniques for determining and presenting improved seeding rate recommendations for planting seeds in a field are provided. In an embodiment, a computer-implemented method includes receiving digital data representing planting parameters including seed type information and planting row width, and retrieving a set of seeding models based upon the planting parameters, where each of the seeding models includes a regression model defining a relationship between plant yield and seeding rate on a specific field. The method also includes generating an empirical mixture model as a composite distribution of the set of seeding models, generating a seeding rate distribution for the planting parameters based upon the empirical mixture model, and calculating a seeding rate recommendation based on the seed rate distribution. The method then also includes planting plant seeds in the specific field consistent with the seeding rate recommendation.

Techniques for providing improvements in agricultural science by optimizing irrigation treatment placements for testing are provided, including analyzing a plurality of digital images of a field to determine vegetation density changes in a sector of the field. The techniques proceed by comparing a distribution of pixel characteristics in the digital images for each field sector to determine sectors in which minimal differences are present. Instructions for irrigation placements and testing may then be displayed or modified based on the results of the sector determinations.