One variation of a method for automating transfer of plants within an agricultural facility includes: dispatching a loader to autonomously deliver a first moduledefining a first array of plant slots at a first density and loaded with a first set of plants at a first growth stagefrom a first grow location within an agricultural facility to a transfer station within the agricultural facility; dispatching the loader to autonomously deliver a second moduledefining a second array of plant slots at a second density less than the first density and empty of plantsto the transfer station; recording a module-level optical scan of the first module; extracting a viability parameter of the first set of plants from features detected in the module-level optical scan; and if the viability parameter falls outside of a target viability range, rejecting transfer of the first set of plants from the first module.

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. 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 an 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. 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 an mathematically adjusts the compared values to compute an optimal treatment algorithm.

A system for observing agricultural samples includes a chassis suspended on an elevated cable or rail, an actuator disposed within the chassis for moving the chassis forward and backward along the elevated cable or rail, a camera mounted on or within the chassis and configured to acquire image data of an area below the elevated cable or rail including an agricultural sample, and a processor disposed within the chassis for receiving image data from the camera, autonomously controlling the actuator to move the chassis along the elevated cable or rail, and assessing a condition of the agricultural sample from the received image data.

A system for observing agricultural samples includes a chassis suspended on an elevated cable or rail, an actuator disposed within the chassis for moving the chassis forward and backward along the elevated cable or rail, a camera mounted on or within the chassis and configured to acquire image data of an area below the elevated cable or rail including an agricultural sample, and a processor disposed within the chassis for receiving image data from the camera, autonomously controlling the actuator to move the chassis along the elevated cable or rail, and assessing a condition of the agricultural sample from the received image data.

An automated seed sampler system includes an orientation system configured to orient a seed, and a sampling station configured to remove tissue from the oriented seed. In addition, a method for removing tissue from seeds includes positioning multiple seeds together in a desired orientation in a seed transport subsystem, and removing tissue from the oriented seeds while the seeds are in the seed transport subsystem.

An automated seed sampler system includes an orientation system configured to orient a seed, and a sampling station configured to remove tissue from the oriented seed. In addition, a method for removing tissue from seeds includes positioning multiple seeds together in a desired orientation in a seed transport subsystem, and removing tissue from the oriented seeds while the seeds are in the seed transport subsystem.

A method for generating digital models of potential crop yield based on planting date, relative maturity, and actual production history is provided. In an embodiment, data representing historical planting dates, relative maturity values, and crop yield is received by an agricultural intelligence computer system. Based on the historical data, the system generates spatial and temporal maps of planting dates, relative maturity, and actual production history. Using the maps, the system creates a model of potential yield that is dependent on planting date and relative maturity. The system may then receive actual production history data for a particular field. Using the received actual production history data, a particular planting date, and a particular relative maturity value, the agricultural intelligence computer system computes a potential yield for a particular field.

A method for generating digital models of potential crop yield based on planting date, relative maturity, and actual production history is provided. In an embodiment, data representing historical planting dates, relative maturity values, and crop yield is received by an agricultural intelligence computer system. Based on the historical data, the system generates spatial and temporal maps of planting dates, relative maturity, and actual production history. Using the maps, the system creates a model of potential yield that is dependent on planting date and relative maturity. The system may then receive actual production history data for a particular field. Using the received actual production history data, a particular planting date, and a particular relative maturity value, the agricultural intelligence computer system computes a potential yield for a particular field.

A method of detecting a potato virus in a crop image depicting at least one potato plant includes storing the crop image in a memory, identifying a first region of the crop image depicting potato plant leaves, identifying a plurality of edges within the first region, determining whether an image segment of the crop image within the first region satisfies one or more leaf creasing criteria symptomatic of leaf creasing caused by the virus based on the edges that are located within the image segment, determining whether the image segment satisfies one or more color criteria symptomatic of discoloration caused by the virus, and determining whether the segment displays symptoms of potato virus based on whether the image segment satisfies one or more of the leaf creasing criteria and the color criteria. A system and computer readable medium are also disclosed.