Generating quasi-realistic synthetic training data for use with machine learning models

A system comprising one or more processors and memory storing instructions that, in response to execution of the instructions by the one or more processors, cause the one or more processors to:

• generate a plurality of labeled simulated images, each labeled simulated image depicting one or more simulated and labeled instances of a plant having a targeted plant trait,
• wherein the generation includes stochastically selection of features of the simulated instances of plants from a collection of plant assets associated with the targeted plant trait, wherein the collection of plant assets are obtained from one or more ground truth digital images captured previously by one or more vision sensors, and
• wherein the one or more ground truth digital images depict real-life instances of plants having the target plant trait;
• and
• once the plurality of labeled simulated images are generated, translate the plurality of labeled simulated images from a first domain to a second domain using a trained generator model of a generative adversarial network, wherein the translation includes generation of a plurality of quasi-realistic synthetic training images, wherein each quasi-realistic synthetic training image depicts one or more quasi-realistic and labeled instances of the targeted plant trait.

At least one non-transitory computer-readable medium comprising instructions that, in response to execution of the instructions by one or more processors, cause the one or more processors to perform the following operations:

• generating a plurality of labeled simulated images, each labeled simulated image depicting one or more simulated and labeled instances of a plant having a targeted plant trait,
• wherein the generating includes stochastically selecting features of the simulated instances of plants from a collection of plant assets associated with the targeted plant trait, wherein the collection of plant assets are obtained from one or more ground truth digital images captured previously by one or more vision sensors, and
• wherein the one or more ground truth digital images depict real-life instances of plants having the target plant trait;
• and
• subsequent to the generating, translating the plurality of labeled simulated images from a first domain to a second domain using a trained generator model of a generative adversarial network, wherein the translating includes generating a plurality of quasi-realistic synthetic training images, wherein each quasi-realistic synthetic training image depicts one or more quasi-realistic and labeled instances of the targeted plant trait.