ArticleFrontiers in plant science2024
Synthetic data at scale: a development model to efficiently leverage machine learning in agriculture.
Article in Frontiers in plant science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Thermal image segmentation in weedy fields via synthetic RGB-trained models and GAN-based cross-modality alignment.Plant phenomics (Washington, D.C.) · 2026Article
- A Survey of Environmental Perception for Unmanned Ground Agricultural Machinery in Field Environments.Sensors (Basel, Switzerland) · 2026Review
- Deep Aramaic: Towards a synthetic data paradigm enabling machine learning in epigraphy.PloS one · 2024Article
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Authors and funding
6 authors.
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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 (
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