Accurate monitoring of rice phenology is critical for crop management, cultivars breeding, and yield estimating. Previously, research for phenology detection relied on time-series data and orthomosaic and manually plotted regions, which are difficult to automate. This study presented a novel approach for extracting and mapping phenological traits directly from the unmanned aerial vehicle (UAV) photograph sequence. First, a multi-stage rice field segmentation dataset containing four growth stages and 2600 images, namely PaddySeg, was built. Moreover, an efficient Ghost Bilateral Network (GBiNet) was proposed to generate trait masks. To locate the trait of each pixel, we introduced direct geo-locating (DGL) and incremental sparse sampling (ISS) techniques to eliminate redundant computation. According to the results on PaddySeg, the proposed GBiNet with 91.50% mean-Intersection-over-Union (mIoU) and 41 frames-per-second (FPS) speed outperformed the baseline model (90.95%, 36 FPS), while the fastest GBiNet_t reached 62 FPS which was 1.7 times faster than the baseline model, BiSeNetV2. Additionally, the measured average DGL deviation was less than 1% of the relative height. Finally, the mapping of rice phenology was achieved by interpolation on trait value–location pairs. The proposed approach demonstrated great potential for automatic rice phenology stage surveying and mapping.

Digital twin applications offered transformative potential by enabling real-time monitoring and robotic simulation through accurate virtual replicas of physical assets. The key to these systems is 3D reconstruction with high geometrical fidelity. However, existing methods struggled under field conditions, especially with sparse and occluded views. This study developed the DATR framework for apple tree reconstruction from sparse views. DATR automatically generates tree masks from field images using onboard sensors and foundation models, which filter background information from multi-modal data for reconstruction. Leveraging the multi-modality, a diffusion model generates novel views and a large reconstruction model produces implicit neural fields. Geometric priors enable scale retrieval to align output with real-world dimensions. Both models were trained using realistic synthetic apple trees generated by a Real2Sim data generator. The framework was evaluated on both field and synthetic datasets. The field dataset includes six apple trees with field-measured ground truth, while the synthetic dataset featured structurally diverse trees. The evaluation results indicated that our DATR framework surpassed existing 3D reconstruction techniques on both datasets, providing a favorable balance of accuracy and efficiency. It achieved domain-trait estimation performance on par with industrial-grade stationary laser scanners, while increasing scan throughput by approximately 1000×, thereby underscoring its strong promise for scalable agricultural digital twin systems.

Powdery mildew is a major fungal disease of grapevines, yet its field quantification remains constrained by subjective visual assessments, inconsistent scoring protocols, and the high cost of annotated datasets. Large multi-modal models that integrate vision and language offer new opportunities for scalable phenotyping, but applying them to agricultural imagery is challenging due to subtle symptoms and domain shifts across environments. This study presents an automated phenotyping pipeline that combines active illumination imaging with a unified Segment Anything Model-Contrastive Language-Image Pretraining (SAM-CLIP) framework for powdery mildew segmentation and vine-level severity quantification.The PM-SAM-CLIP model achieved 58.43% image-level mean Intersection over Union (mIoUI), substantially outperforming baselines. For canopy segmentation, Canopy-SAM-CLIP reached 95.48% mIoUI, providing a stable foundation for severity normalization. Applied to independent field datasets without fine-tuning, the pipeline demonstrated strong zero-shot generalization, with over 98% of vines scored within one category of expert ratings. Multi-date analyses further produced spatial and temporal infection maps that captured vineyard-scale heterogeneity and disease progression.To assess biological relevance, a downstream quantitative trait locus (QTL) analysis was performed using image-derived severity metrics. This analysis identified a major resistance locus on chromosome~13, consistent with QTL mapped from human scouting scores and aligned with previously reported loci, supporting the validity of the automated phenotypes.Overall, this work demonstrates that large multi-modal models adapted through vision-language integration can generate accurate, reproducible, and scalable phenotypes for plant disease assessment. The proposed pipeline reduces human subjectivity and provides high-quality quantitative traits that support downstream genotype-phenotype analyses in viticulture.