Synthetic data at scale: a development model to efficiently leverage machine learning in agriculture.

ABSTRACT: The rise of artificial intelligence (AI) and in particular modern machine learning (ML) algorithms during the last decade has been met with great interest in the agricultural industry. 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 paper, we present a development model 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

SUBMITTER: Klein J 

PROVIDER: S-EPMC11439777 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature