A Three-Year Multimodal Holistic Dataset For Horticultural Tomato Cultivation.

Abstract

Existing tomato datasets often focus on short-term experiments or lack integrated environmental and agronomic data. We present Horti-M3-Tomato, a comprehensive three-year dataset collected in Northeast China's greenhouse, including high-resolution RGB images, environmental sensor data (recorded every 30 minutes), soil conditions, and detailed agronomic records such as yield data and management practices. Spanning three growing seasons (2023-2025), the dataset integrates temporal imaging, environmental monitoring, soil data, and manual phenotypic and yield records. Horti-M3-Tomato supports research on growth dynamics, genotype-environment interactions, and provides a benchmark for AI-based phenotyping and precision horticulture. The dataset is openly available for further research in controlled-environment agriculture.

Code and data availability

The paper's Horti-M3-Tomato dataset (images, sensor data, phenotypic/yield records) and preprocessing code are deposited on Zenodo (10.5281/zenodo.17217565), but that URL is not among the allowed_urls, so no actionable paper-specific asset can be listed. The only allowed URL matching a dataset (Kaggle tomato leaf-dise)

No evidence-backed public reproduction asset is currently recorded.