Research on Intelligent Control Technology for a Rail-Based High-Throughput Crop Phenotypic Platform Based on Digital Twins

Haishen Liu, Weiliang Wen, Wenbo Gou, Xianju Lu, Hanyu Ma, Lin Zhu, Minggang Zhang, Sheng Wu and Xinyu Guo ()
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Haishen Liu: Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Weiliang Wen: Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Wenbo Gou: Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Xianju Lu: Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Hanyu Ma: Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
Lin Zhu: Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
Minggang Zhang: Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Sheng Wu: Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Xinyu Guo: Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China

Agriculture, 2025, vol. 15, issue 11, 1-21

Abstract: Rail-based crop phenotypic platforms operating in open-field environments face challenges such as environmental variability and unstable data quality, highlighting the urgent need for intelligent, online data acquisition strategies. This study proposes a digital twin-based data acquisition strategy tailored to such platforms. A closed-loop architecture “comprising connection, computation, prediction, decision-making, and execution“ was developed to build DT-FieldPheno, a digital twin system that enables real-time synchronization between physical equipment and its virtual counterpart, along with dynamic device monitoring. Weather condition standards were defined based on multi-source sensor requirements, and a dual-layer weather risk assessment model was constructed using the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation by integrating weather forecasts and real-time meteorological data to guide adaptive data acquisition scheduling. Field deployment over 27 consecutive days in a maize field demonstrated that DT-FieldPheno reduced the manual inspection workload by 50%. The system successfully identified and canceled two high-risk tasks under wind-speed threshold exceedance and optimized two others affected by gusts and rainfall, thereby avoiding ineffective operations. It also achieved sub-second responses to trajectory deviation and communication anomalies. The synchronized digital twin interface supported remote, real-time visual supervision. DT-FieldPheno provides a technological paradigm for advancing crop phenotypic platforms toward intelligent regulation, remote management, and multi-system integration. Future work will focus on expanding multi-domain sensing capabilities, enhancing model adaptability, and evaluating system energy consumption and computational overhead to support scalable field deployment.

Keywords: digital twin; rail-based phenotypic platform; virtual physical synchronization; adaptive regulation; weather risk assessment; smart agriculture (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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