Synthetic Data at Scale: A Paradigm to Efficiently Leverage Machine Learning in Agriculture

The rise of artificial intelligence (AI) and in particular modern machine learning (ML) algorithms has been one of the most exciting developments in agriculture within the last decade. While undisputedly powerful, their main drawback remains the need for sufficient and diverse training data. The collection of real datasets and their annotation are the main cost drivers of ML developments, and while promising results on synthetically generated training data have been shown, their generation is not without difficulties on their own. In this contribution, we present a paradigm for the iterative cost-efficient generation of synthetic training data. Its application is demonstrated by developing a low-cost early disease detector for tomato plants (Solanum lycopersicum) using synthetic training data. In particular, a binary classifier is developed to distinguish between healthy and infected tomato plants based on photographs taken by an unmanned aerial vehicle (UAV) in a greenhouse complex. The classifier is trained by exclusively using synthetic images which are generated iteratively to obtain optimal performance. In contrast to other approaches that rely on a human assessment of similarity between real and synthetic data, we instead introduce a structured, quantitative approach. We find that our approach leads to a more cost efficient use of ML-aided computer vision tasks in agriculture.