Wenbo Gou

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.

The spatial and temporal dynamics of crop canopy structure are influenced by cultivar, environment, and crop management practices. However, continuous and automatic monitoring of crop canopy structure is still challenging. A three-dimensional (3D) time-series phenotyping study of maize canopy was conducted using a rail-driven high-throughput plant phenotyping platform (HTPPP) in field conditions. An adaptive sliding window segmentation algorithm was proposed to obtain plots and rows from canopy point clouds. Maximum height (H_max), mean height (H_mean), and canopy cover (CC) of each plot were extracted, and quantification of plot canopy height uniformity (CHU) and marginal effect (ME_H) was achieved. The results showed that the average mIoU, mP, mR, and mF₁ of canopy–plot segmentation were 0.8118, 0.9587, 0.9969, and 0.9771, respectively, and the average mIoU, mP, mR, and mF₁ of plot–row segmentation were 0.7566, 0.8764, 0.9292, and 0.8974, respectively. The average RMSE of plant height across the 10 growth stages was 0.08 m. The extracted time-series phenotypes show that CHU tended to vary from uniformity to nonuniformity and continued to fluctuate during the whole growth stages, and the ME_H of the canopy tended to increase negatively over time. This study provides automated and practical means for 3D time-series phenotype monitoring of plant canopies with the HTPPP.

Method for the real-time detection of tomato ripeness using a phenotype robot and RP-YolactEdge

Unmanned ground vehicles (UGV) have attracted much attention in crop phenotype monitoring due to their lightweight and flexibility. This paper describes a new UGV equipped with an electric slide rail and point cloud high-throughput acquisition and phenotype extraction system. The designed UGV is equipped with an autopilot system, a small electric slide rail, and Light Detection and Ranging (LiDAR) to achieve high-throughput, high-precision automatic crop point cloud acquisition and map building. The phenotype analysis system realized single plant segmentation and pipeline extraction of plant height and maximum crown width of the crop point cloud using the Random sampling consistency (RANSAC), Euclidean clustering, and k-means clustering algorithm. This phenotyping system was used to collect point cloud data and extract plant height and maximum crown width for 54 greenhouse-potted lettuce plants. The results showed that the correlation coefficient (R2) between the collected data and manual measurements were 0.97996 and 0.90975, respectively, while the root mean square error (RMSE) was 1.51 cm and 4.99 cm, respectively. At less than a tenth of the cost of the PlantEye F500, UGV achieves phenotypic data acquisition with less error and detects morphological trait differences between lettuce types. Thus, it could be suitable for actual 3D phenotypic measurements of greenhouse crops.