aceCoupling machine learning with a biophysical model for maturity date prediction of apple fruit across China's apple planting regions
Background Accurate prediction of apple fruit maturity date is essential for optimizing harvest timing, fruit quality and market value under climate change. However, process-based crop models often show limited performance when extrapolated across large spatial scales, whereas machine learning models lack physiological interpretability. To address these limitations, this study has developed a hybrid framework integrating the process-based STICS model with machine learning approaches across China's apple planting…
Hybrid STICS plus random forest integration improved accuracy and interpretability of apple fruit maturity date prediction to support harvest timing and climate adaptation across China's apple regions.
Model performance showed substantial regional variability and process-based models have known extrapolation limits at large spatial scales.
Evidence
- Peer-reviewedJournal of the Science of Food and Agriculture2026-08-01
How should this claim be treated?
Truvace Impact Record TRV-2026-0632, v1: “Coupling machine learning with a biophysical model for maturity date prediction of apple fruit across China's apple planting regions.” Truvace, 2026-08-03. /record/TRV-2026-0632 (accessed at citation time). sha256 243e01eccfc4b051…
Calibration history
Every change to this record since certification, in the open. None yet — the reading has held since it entered the record.
Certified into the record
How to verify without trusting this page
Fetch the canonical text of any version from /api/record/TRV-2026-0632 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.