Self-Supervised Data Generation for Precision Agriculture: Blending Simulated Environments with Real Imagery

TL;DR

This paper introduces a system that generates realistic synthetic images from simulated vineyard environments to improve machine learning models in precision agriculture, addressing data scarcity and environmental variability.

Contribution

It presents a novel Unity-based vineyard simulation with a cut-and-paste technique for generating diverse, photo-realistic training data for detection algorithms in agriculture.

Findings

Significant performance improvements in detection accuracy.

Effective generation of diverse viewpoints and lighting conditions.

Automatable data synthesis process.

Abstract

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.