Self-Supervised Data Generation for Precision Agriculture: Blending Simulated Environments with Real Imagery
This addresses data scarcity for precision agriculture practitioners, but it is incremental as it adapts existing simulation and blending methods to a specific domain.
The paper tackles the problem of scarce labeled data and covariate shifts in precision agriculture by proposing a system that generates realistic synthetic data using a vineyard simulator and a cut-and-paste technique, resulting in considerable performance improvements for a state-of-the-art detector in table grapes cultivation.
In precision agriculture, the scarcity of labeled data and significant covariate shifts pose unique challenges for training machine learning models. This scarcity is particularly problematic due to the dynamic nature of the environment and the evolving appearance of agricultural subjects as living things. We propose a novel system for generating realistic synthetic data to address these challenges. Utilizing a vineyard simulator based on the Unity engine, our system employs a cut-and-paste technique with geometrical consistency considerations to produce accurate photo-realistic images and labels from synthetic environments to train detection algorithms. This approach generates diverse data samples across various viewpoints and lighting conditions. We demonstrate considerable performance improvements in training a state-of-the-art detector by applying our method to table grapes cultivation. The combination of techniques can be easily automated, an increasingly important consideration for adoption in agricultural practice.