Abstract: A phenotyping device, method and system for measuring phenotypic traits of one or more plants in a target canopy, wherein the includes a canopy spreader for spreading the target canopy, a camera unit, a means for controlling the angle and the distance of the camera unit in relation to the canopy spreader, an electronic control unit for controlling the camera unit. The phenotyping device, method and system enables recording the raw data from the target canopy during a data collection session including information of the plants that are not visible to a camera unit without spreading the canopy.

Abstract: A method of selecting a plant variety for cultivation in a target area includes selecting a selection score function; estimating values of a first set of environmental parameters for a predefined future period of time for the target area and receiving a set of phenotype information including phenotypic trait measurements for a first sub-set of a plurality of plant varieties and a set o environmental parameters for said first sub-set. Furthermore, the method includes determining a prediction model for the phenotypic traits; using the prediction model to output predictions for phenotypic traits for the plurality of chosen plant varieties; using the selection score function to compute selection score values; and selecting at least one plant variety to be cultivated in the target area from the plurality of chosen plant varieties, based the computed selection score values of the plurality of chosen plant varieties.

Abstract: A method for obtaining new plant growth data according to an experimentation objective, the method including defining an experimentation objective; defining a set of experimentation alternatives; obtaining a data set of experimentation alternative conditions; obtaining a trained plant growth model including plant growth model parameters; defining an experimental design utility function based on the experimentation objective; selecting an experimentation plan from the set of experimentation alternatives by using the trained plant growth model, the experimental design utility function, and the data set of experimentation alternative conditions; and performing the selected experimentation plan to obtain new plant growth data.

Abstract: A method of generating an annotated synthetic training data for training a machine learning module for processing an operational data set includes creating a first procedural model for the object, the first procedural model having a first set of parameters relating to the object; creating a second procedural model for the background, the second procedural model having a second set of parameters relating to the background; creating the task environment model pertaining to the machine learning task using the first and the second procedural models; creating a synthetic data set using the task environment model; and allocating at least one parameter of the first set of parameters as an annotation for the simulation data to generate the annotated synthetic training data.

Abstract: A method of selecting a plant variety for cultivation in a target area includes selecting a selection score function; estimating values of a first set of environmental parameters for a predefined future period of time for the target area and receiving a set of phenotype information including phenotypic trait measurements for a first sub-set of a plurality of plant varieties and a set environmental parameters for said first sub-set. Furthermore, the method includes determining a prediction model for the phenotypic traits; using the prediction model to output predictions for phenotypic traits for the plurality of chosen plant varieties; using the selection score function to compute selection score values; and selecting at least one plant variety to be cultivated in the target area from the plurality of chosen plant varieties, based the computed selection score values of the plurality of chosen plant varieties.