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

Abstract

Implementations are described herein for automatically generating quasi-realistic synthetic training images that are usable as training data for training machine learning models to perceive various types of plant traits in digital images. In various implementations, multiple labeled simulated images may be generated, each depicting simulated and labeled instance(s) of a plant having a targeted plant trait. In some implementations, the generating may include stochastically selecting features of the simulated instances of plants from a collection of plant assets associated with the targeted plant trait. The collection of plant assets may be obtained from ground truth digital image(s). In some implementations, the ground truth digital image(s) may depict real-life instances of plants having the target plant trait. The plurality of labeled simulated images may be processed using a trained generator model to generate a plurality of quasi-realistic synthetic training images, each depicting quasi-realistic and labeled instance(s) of the targeted plant trait.