Described herein are systems and methods for capturing images of a field and performing agricultural data analysis of the images. In one embodiment, a computer system for monitoring field operations includes a database for storing agricultural image data including images of at least one stage of crop development that are captured with at least one of an apparatus and a remote sensor moving through a field. The computer includes at least one processing unit that is coupled to the database. The at least one processing unit is configured to execute instructions to analyze the captured images, to determine relevant images that indicate a change in at least one condition of the crop development, and to generate a localized view map layer for viewing the field at the at least one stage of crop development based on at least the relevant captured images.

A controlled growth system is provided herein. The controlled growth system includes a controlled growth environment, a controller, a sensor, and a computing system. The controlled growth environment is configured to grow a biologic. The controller is in communication with the controlled growth environment. The controller is configured to manage process parameters of the controlled growth environment. The sensor is configured to monitor the biologic during a growth process. The computing system is in communication with the sensor and the controller. The computing system is programmed to perform operations for achieving a desired final quality metric for the biologic.

A controlled growth system is provided herein. The controlled growth system includes a controlled growth environment, a controller, a sensor, and a computing system. The controlled growth environment is configured to grow a biologic. The controller is in communication with the controlled growth environment. The controller is configured to manage process parameters of the controlled growth environment. The sensor is configured to monitor the biologic during a growth process. The computing system is in communication with the sensor and the controller. The computing system is programmed to perform operations for achieving a desired final quality metric for the biologic.

The present disclosure describes a system, method, and non-transitory computer readable medium for analyzing soil samples. Accordingly, soil sample units may be obtained and provided to a server that generates raw data. Tho raw data is sent to a database, where it is downloaded. The raw data is subsequently organized into a sub-report for each nutrient or variable contained in the raw data. An average for each nutrient in the raw data and a number of additional factors related to the raw data may be calculated. The average and additional factors are used to determine bulk recommendations by comparing target data to an exchangeable measured value. Additionally, the factors are also used to determine challenges and solutions by comparing the average data to the target data for each nutrient. The system compares the raw data to the measured values and mathematically adjusts the compared values to compute an optimal treatment algorithm.

The present disclosure describes a system, method, and non-transitory computer readable medium for analyzing soil samples. Accordingly, soil sample units may be obtained and provided to a server that generates raw data. Tho raw data is sent to a database, where it is downloaded. The raw data is subsequently organized into a sub-report for each nutrient or variable contained in the raw data. An average for each nutrient in the raw data and a number of additional factors related to the raw data may be calculated. The average and additional factors are used to determine bulk recommendations by comparing target data to an exchangeable measured value. Additionally, the factors are also used to determine challenges and solutions by comparing the average data to the target data for each nutrient. The system compares the raw data to the measured values and mathematically adjusts the compared values to compute an optimal treatment algorithm.

A wheat variety designated A060013G1, the plants and seeds of wheat variety A060013G1, methods for producing a wheat plant produced by crossing the variety A060013G1 with another wheat plant, and hybrid wheat seeds and plants produced by crossing the variety A060013G1 with another wheat line or plant, and the creation of variants by backcrossing, mutagenesis or transformation of variety A060013G1 are disclosed. Methods for producing other wheat varieties or breeding lines derived from wheat variety A060013G1 and to wheat varieties or breeding lines produced by those methods are also provided.

Apparatus, systems and methods are provided for crop stand optimization. In some embodiments a field is planted at a first population prescription determined based on a first data set including forecasted weather. A subsequently determined second population prescription is determined based on a second data set determined after planting, and the planted crop is thinned according to the second population prescription. In some embodiments the crop is selectively thinned to remove plants having a plant removal index below a threshold.

Apparatus, systems and methods are provided for crop stand optimization. In some embodiments a field is planted at a first population prescription determined based on a first data set including forecasted weather. A subsequently determined second population prescription is determined based on a second data set determined after planting, and the planted crop is thinned according to the second population prescription. In some embodiments the crop is selectively thinned to remove plants having a plant removal index below a threshold.

A system for automating the growing of crops, such as grapevines. Combinations of data from sensors local to a vineyard, and from optional remote stations and sensors, is combined with a control system to accurately control the dispensing of water and chemicals such as insecticides, disease prevention fungicides and fertilizers. The materials are dispensed through a multiple channel conduit which allows conflicting, or incompatible, types of materials to be transported through a common assembly. Sensors are attached to the conduit so that the placement of sensors can occur simultaneously with the laying of the conduit. This approach also ensures correct placement and spacing of the sensors with respect to each plant, or plant area, to be monitored and treated.

A system for automating the growing of crops, such as grapevines. Combinations of data from sensors local to a vineyard, and from optional remote stations and sensors, is combined with a control system to accurately control the dispensing of water and chemicals such as insecticides, disease prevention fungicides and fertilizers. The materials are dispensed through a multiple channel conduit which allows conflicting, or incompatible, types of materials to be transported through a common assembly. Sensors are attached to the conduit so that the placement of sensors can occur simultaneously with the laying of the conduit. This approach also ensures correct placement and spacing of the sensors with respect to each plant, or plant area, to be monitored and treated.