Wachter, PaulKruse, NiklasSchöning, Julius2024-04-082024-04-082024978-3-88579-738-8https://dl.gi.de/handle/20.500.12116/43916Artificial intelligence (AI) promises transformative impacts on society, industry, and agriculture, while being heavily reliant on diverse, quality data. The resource-intensive “data problem” has initialized a shift to synthetic data. One downside of synthetic data is known as the “reality gap”, a lack of realism. Hybrid data, combining synthetic and real data, addresses this. The paper examines terminological inconsistencies and proposes a unified taxonomy for real, synthetic, augmented, and hybrid data. It aims to enhance AI training datasets in smart agriculture, addressing the challenges in the agricultural data landscape. Utilizing hybrid data in AI models offers improved prediction performance and adaptability.enhybrid datasynthetic dataaugmented datasmart farmingreality gapText/Conference Paper1617-5468