Evaluating synthetic vs. real data generation for AI-based selective weeding

Abstract

Synthetic data has the potential to reduce the cost for ML training in agriculture but poses its own set of problems compared to real data acquisition. In this work, we present two methods of training data acquisition for the application of machine vision algorithms in the use case of selective weeding. Results from ML experiments suggest that current methods for generating synthetic data in the field of agriculture cannot fully replace real data but may greatly reduce the quantity of real data required for model training.