Journal of Zhejiang University SCIENCE B最新文献

{"title":"A novel A-to-G mutation in circBDP1 alters adipocyte proliferation and differentiation and affects bovine carcass traits.","authors":"Enhui Jiang, Chiyuan Zhang, Zhuoyuan He, Yuli Zhang, Yuta Yang, Chuanying Pan, Fugui Jiang, Enliang Song, Sihuan Zhang, Xianyong Lan","doi":"10.1631/jzus.B2500084","DOIUrl":"https://doi.org/10.1631/jzus.B2500084","url":null,"abstract":"<p><p>Fat deposition in cattle, influenced by factors such as genetics, age, sex, and nutrition, plays a crucial role in determining growth rates, beef yield, and meat quality. Among these, genetics is the predominant factor (Kim et al., 2020; Xing et al., 2025). Research indicates a high heritability for visceral (36%) and subcutaneous (57%) fat (Fox et al., 2012). Gene mutations that affect adipocyte function are significant contributors to variations in fat deposition and meat quality (Mannen, 2011; Mwangi et al., 2019; Sun et al., 2023; Dementieva et al., 2024). Therefore, investigating genetic variations in fat-related genes is essential for enhancing cattle performance.</p>","PeriodicalId":17797,"journal":{"name":"Journal of Zhejiang University SCIENCE B","volume":"27 8","pages":"918-922"},"PeriodicalIF":5.7,"publicationDate":"2026-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13474197/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148766610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel drill-milling technology for robot-assisted implant osteotomy preparation: simulation and an ex vivovalidation study.","authors":"Chaofan Li, Kangjie Cheng, Chenhao Yu, Russell Wang, Fudong Zhu, Yunfeng Liu","doi":"10.1631/jzus.B2400560","DOIUrl":"https://doi.org/10.1631/jzus.B2400560","url":null,"abstract":"<p><strong>Objectives: </strong>Excessive frictional heat generated at the drill-bone interface during implant osteotomy can compromise osseointegration and lead to implant failure. This study's objective was to use a helical milling technique for dental implant osteotomy preparation to mitigate thermal damage to the bone.</p><p><strong>Methods: </strong>This study introduces a novel helical milling technique designed to minimize thermal damage to bone during dental implant osteotomy preparations. Finite element simulations were conducted to compare the thermal distribution and cutting stress of a conventional twist drill and the newly designed helical drill. The experimental validation was performed ex vivo on animal bone using a robot-assisted osteotomy system.</p><p><strong>Results: </strong>The finite element simulations revealed that the helical drill produced a maximum cutting stress of 128.9 MPa, higher than the 121.0 MPa generated by the twist drill, indicating improved cutting efficiency. The ex vivo study demonstrated that the helical milling technique maintained the drilling site temperature below 38.7 ℃, which was significantly lower than the clinically critical threshold of 47 ℃ and the 61 ℃ recorded with the twist drill. Furthermore, the helical milling process facilitated efficient bone chip removal, reducing thermal buildup.</p><p><strong>Conclusions: </strong>These findings suggest that the helical milling tool and technique optimize robot-assisted osteotomy and effectively mitigate frictional heat generation at the drill-bone interface. This innovation holds promise for enhancing osseointegration success rates in dental implant procedures, offering a clinically viable solution to a long-standing challenge in implantology.</p>","PeriodicalId":17797,"journal":{"name":"Journal of Zhejiang University SCIENCE B","volume":"27 8","pages":"906-917"},"PeriodicalIF":5.7,"publicationDate":"2026-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13474195/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148766600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning-driven evaluation of protein kinase D3 as a co-diagnostic biomarker in hepatocellular carcinoma.","authors":"Jing Li, Yifan Zhao, Yicheng Ma, Bei Xie, Li Huang, Haitang Yang, Xingyuan Ma, Haohua Deng, Shuaiyang Wang, Chanjuan Sun, Pengfei Cao, Linjing Li","doi":"10.1631/jzus.B2500607","DOIUrl":"10.1631/jzus.B2500607","url":null,"abstract":"<p><p>To elucidate the diagnostic value and clinical relevance of protein kinase D3 (PRKD3) in hepatocellular carcinoma (HCC), we analyzed data retrieved from The Cancer Genome Atlas (TCGA) database, which revealed high expression of <i>PRKD3</i> in HCC tissues. Subsequently, we collected a total of 392 clinical plasma samples from healthy individuals, patients with cirrhosis or decompensated cirrhosis, and patients with HCC. Plasma PRKD3 levels were then determined across HCC patients and individuals at high risk of developing the disease. The results revealed significantly elevated PRKD3 concentrations in patients with cirrhosis, decompensated cirrhosis, and HCC compared to healthy controls (<i>P</i><0.01). The areas under the receiver operating characteristic (ROC) curve for these three groups were 0.8107, 0.7899, and 0.7177, respectively. To further evaluate the efficacy of PRKD3 as an adjunctive diagnostic biomarker for HCC, we employed a panel of machine learning algorithms as primary classifiers, including extra trees (ET), gradient boosting (GB), random forest (RF), and support vector machine (SVM). A multi-parameter joint diagnostic model was constructed by combining PRKD3 expression data with a set of clinical parameters, including gender, age, total bilirubin (TBIL), alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), albumin (ALB), alpha-fetoprotein (AFP), and prothrombin induced by vitamin K absence-II (PIVKA-II). This integrated approach exhibited substantially improved diagnostic performance, achieving an accuracy of 0.861, sensitivity of 0.863, specificity of 0.925, and precision of 0.862. Collectively, these findings highlight the potential of PRKD3 as an integral component of a comprehensive diagnostic tool for the early identification of HCC.</p>","PeriodicalId":17797,"journal":{"name":"Journal of Zhejiang University SCIENCE B","volume":"27 7","pages":"788-805"},"PeriodicalIF":5.7,"publicationDate":"2026-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13420521/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148620566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Endocrine-disrupting chemicals in aquatic ecosystems and their impacts on aquatic organisms and humans with advances in detection and remediation.","authors":"Tariq Dildar, Yongcheng Xue, Wenxiao Cui, Mhd Ikhwanuddin, Muhammad Shafiq, Hongyu Ma","doi":"10.1631/jzus.B2500252","DOIUrl":"10.1631/jzus.B2500252","url":null,"abstract":"<p><p>The increasing anthropogenic burden, driven by population growth and intensified industrial and agricultural practices, has led to the widespread release of endocrine-disrupting chemicals (EDCs) into aquatic ecosystems, with significant implications for both environmental and human health. Many studies have reported the concentrations and toxicological effects of EDCs in aquatic environments, but few have addressed detection methods and remediation techniques. This review aims to highlight the sources, dynamics, and bioaccumulation of EDCs in aquatic ecosystems, along with their toxic effects on aquatic species and associated health risks in humans. Additionally, we provide an overview of advanced detection and remediation techniques. Our review found that EDCs, particularly phthalates and bisphenols, included in industrial effluents, domestic waste, and agricultural runoff, are frequently discharged into aquatic bodies through human activities. EDCs are associated with various toxic effects in aquatic organisms, such as bioaccumulation, transgenerational effects, reduced growth, immunotoxicity, DNA damage, and abnormal hormonal release, which impair reproductive development. Among the detection methods, biosensors, surface-enhanced Raman spectroscopy (SERS), and nuclear magnetic resonance (NMR) spectroscopy are promising tools for EDC detection relative to conventional analytical methods in aquatic systems. Emerging remediation techniques, such as hybrid activated carbon systems and N-doped carbon-based adsorbents, are recommended for their high removal efficiency. This review serves as a valuable resource for advancing research on EDC toxicity, detection, and remediation technologies.</p>","PeriodicalId":17797,"journal":{"name":"Journal of Zhejiang University SCIENCE B","volume":"27 6","pages":"561-589"},"PeriodicalIF":5.7,"publicationDate":"2026-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13294285/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148317890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrative analytical and statistical framework for optimization of multiplex qPCR detection of <i>TGFBI</i> mutations in refractive surgery candidates.","authors":"Yunfeng Gu, Liping Mao, Xiaoling Li, Kangxuan Sun, Qiuruo Jiang, Wenhui Wu, Yangyang Shen, Shihao Chen, Meiqin Zheng, Yi Xu","doi":"10.1631/jzus.B2500747","DOIUrl":"10.1631/jzus.B2500747","url":null,"abstract":"<p><p>Refractive surgery can unmask or accelerate transforming growth factor-β-induced (<i>TGFBI</i>)-related corneal dystrophies that are undetectable by routine slit-lamp examination, creating a clear need for a rapid, standardized, preoperative genetic screening. We developed a multiplex, allele-specific real-time quantitative polymerase chain reaction (qPCR) panel targeting five high-frequency <i>TGFBI</i> hotspots (R124C/L/H, R555W/Q) and built a statistics-driven analytical framework to optimize assay decisions. Receiver operating characteristic (ROC) analysis defined locus-specific cycle threshold (<i>C</i>_T) cut-offs that were harmonized to a single decision threshold (<i>C</i>_T=36) to simplify deployment. Analytical sensitivity was established by Probit modeling of serial two-fold dilutions, and confirmed by ≥20 replicates per level. In a 158-sample validation set (38 mutation-positive; 120 negative), qPCR agreed perfectly with Sanger sequencing (Cohen's kappa coefficient (<i>κ</i>)=1.0). Probit analysis yielded locus-specific limit of detection (LoD) values ranging from 0.035 to 0.200 ng/µL; at 0.200 ng/µL, the detection rate was over 95%. Repeatability and intermediate precision were high (<i>C</i>_T coefficient of variation (CV) 0.34%-1.21%). No cross-reactivity was observed against non-target <i>TGFBI</i> variants or other ophthalmic genes, and interference from blood, oral flora/rinse, or toothpaste produced small, bounded shifts (approximately -7.8% to +2.8%). Calibration with serial dilutions demonstrated linear <i>C</i>_T-log(copy) relationships suitable for routine quality control. Prospective screening of 10 055 refractive surgery candidates identified six <i>TGFBI</i> carriers (0.06%) harboring R124H (including one homozygote), R124L, R124C, or R555W mutation, all confirmed by Sanger sequencing. This study established a clinically applicable, statistically optimized multiplex qPCR platform that integrated ROC-derived cut-offs and Probit-defined LoD with rigorous evaluations of precision, specificity, and robustness, enabling large-scale population implementation. Positive screening results guide clinical decision-making through a standardized post-screening workflow, and the targeted hotspot screening strategy serves as a cost-effective first-tier high-throughput approach for preoperative risk assessment. The framework provides a transparent, reproducible path to standardize preoperative <i>TGFBI</i> screening and reduce iatrogenic risk in refractive surgery candidates.</p>","PeriodicalId":17797,"journal":{"name":"Journal of Zhejiang University SCIENCE B","volume":"27 6","pages":"645-655"},"PeriodicalIF":5.7,"publicationDate":"2026-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13294284/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148317960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Emerging point-of-care technologies for bacterial pathogen detection.","authors":"Wen Zhang, Yundong Hang, Sisi Zhan, Weiyao Song, Binxiao Li, Nan Chen, Min Lv","doi":"10.1631/jzus.B2500782","DOIUrl":"10.1631/jzus.B2500782","url":null,"abstract":"<p><p>Bacterial infections remain a significant threat to public health worldwide, driving an urgent need for rapid, accurate, and field-deployable diagnostic techniques. Point-of-care testing (POCT) has emerged as a transformative strategy, providing timely detection, operational simplicity, and portability. Recent studies have aimed at enhancing sensitivity, specificity, multiplexing capability, and automation through the integration of molecular diagnostics with microfluidics and lab-on-chip technologies, alongside the development of low-cost, portable devices equipped with smartphone-based readout and cloud connectivity for real-time surveillance in resource-limited settings. Nonetheless, evidence-based frameworks for selecting optimal detection targets-such as genomic sequences, conserved protein epitopes, or viable whole cells-and matching them to appropriate POCT modalities remain notably underrepresented in the literature. This review systematically summarizes recent advances in POCT strategies for bacterial detection, categorized according to three major types of detection targets, including cellular phenotypic characteristics, surface antigens, and nucleic acids. We discuss the principles, advantages, limitations, and representative applications of key POCT platforms, which include microscopy-based visualization, immunoassays, isothermal amplification, clustered regularly interspaced short palindromic repeats (CRISPR)-CRISPR-associated protein (Cas) systems, and microfluidic biosensors. Critical challenges, such as sample pretreatment, detection sensitivity, and operational simplicity, have been partially addressed through recent innovations. Finally, we outline the main future research directions focused on the development of integrated, automated, and intelligent POCT systems for clinical deployment.</p>","PeriodicalId":17797,"journal":{"name":"Journal of Zhejiang University SCIENCE B","volume":"27 7","pages":"677-697"},"PeriodicalIF":5.7,"publicationDate":"2026-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13420505/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148620573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Embedding of ripening topology into one-stage detection for tomato cluster phenotyping.","authors":"Bingquan Chu, Ruiyuan Wu, Haijun Zhang, Haochuan Qin, Zishun Peng, Fengle Zhu, Yong He","doi":"10.1631/jzus.B2500647","DOIUrl":"10.1631/jzus.B2500647","url":null,"abstract":"<p><p>The automated assessment of tomato ripeness is vital for modern greenhouse operations, yet challenges remain due to variable environmental conditions. To provide a solution, we propose rank-aware You Only Look Once (YOLO), a novel detection framework that incorporates the biological prior of top-to-bottom ripening within fruit clusters. This is achieved through two key innovations: an efficient position-aware head for regressing relative height for fruits and a dynamic margin-aware ranking loss (DM-RankLoss) that enforces the correct spatial sequence. Evaluated on a 3500-image dataset from a solar greenhouse, our plug-and-play module could boost the mean average precision (mAP) at intersection over union (IoU) threshold of 0.50 (mAP₅₀) of multiple YOLO architectures by up to 5.66 pecentage points. The model effectively learns the cluster topology, achieving a height-mean absolute error (H-MAE) of 0.107 (normalized) and a pairwise ranking accuracy (PRA) of 84.59%, while it reduces the parameter count by over 10% compared to the baseline for efficient deployment. Visualizations confirm that the model leverages spatial context to resolve color ambiguities. Our work offers a sensor-free, accurate, and efficient solution for in situ phenotyping in agricultural robotics.</p>","PeriodicalId":17797,"journal":{"name":"Journal of Zhejiang University SCIENCE B","volume":"27 5","pages":"466-481"},"PeriodicalIF":5.7,"publicationDate":"2026-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13183487/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147973299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing rapeseed biomass and yield estimation with ensemble learning and synergistic multidimensional features.","authors":"Yanni Zhang, Xiaoyu Chai, Jinpeng Hu, Yaxiao Niu, Lizhang Xu","doi":"10.1631/jzus.B2500830","DOIUrl":"10.1631/jzus.B2500830","url":null,"abstract":"<p><p>Accurate rapeseed yield and biomass estimation at the meter scale prior to harvest is crucial for precision harvesting. However, there is a scarcity of structured research on the estimation of rapeseed biomass yield. This study aims to address this gap by focusing on rapeseed in Jiangsu Province. Multispectral and RGB images captured by unmanned aerial vehicles (UAVs) were taken during key growth stages (budding, flowering, and podding stages). Using the extracted multidimensional features, we developed biomass-yield estimation models using four machine learning techniques. Subsequently, we employed ensemble learning with multidimensional, multi-stage data and used Shapley additive explanation (SHAP) for feature contribution analysis, thereby constructing a framework for predicting rapeseed harvest characteristics with high estimation accuracy and interpretability. Our analysis indicates that spectral‒texture is the most effective feature combination for biomass estimation, whereas the optimal combination for yield estimation includes three-dimensional (3D) spectral‒textural‒structural features. The synergy of these features, coupled with an ensemble learning model, significantly enhanced the accuracy of rapeseed biomass-yield estimation (biomass: coefficient of determination (<i>R</i>²)=0.72, relative root mean square error (rRMSE)=14.35%; yield: <i>R</i>²=0.68, rRMSE=13.67%). The proposed model also achieved stable prediction results across the variety‒density interaction. Overall, this study presents an accurate and generalizable approach for estimating rapeseed biomass yield across various planting patterns, offering new insights for precision harvesting.</p>","PeriodicalId":17797,"journal":{"name":"Journal of Zhejiang University SCIENCE B","volume":"27 5","pages":"499-516"},"PeriodicalIF":5.7,"publicationDate":"2026-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13183485/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147973373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RCTUnet: a deep learning model for crop-residue-soil image segmentation and crop residue cover extraction.","authors":"Ting Li, Yang Liu, Haikuan Feng, Meiyan Shu, Hao Yang, Yuanyuan Fu, Xin Xu, Yinghao Lin, Hongbo Qiao, Wei Guo, Xinming Ma, Lei Shi, Jibo Yue","doi":"10.1631/jzus.B2500451","DOIUrl":"10.1631/jzus.B2500451","url":null,"abstract":"<p><p>Accurate quantification of crop residue cover (CRC) is crucial for monitoring and evaluating conservation tillage practices, yet it poses a significant image segmentation challenge. The subtle visual distinctions between fragmented residue and soil, compounded by variable illumination and shadows in field imagery, often lead to poor segmentation performance. To overcome these limitations, we introduce RCTUnet, a novel deep learning architecture designed for robust crop-residue-soil segmentation and precise CRC estimation. RCTUnet's architecture synergistically integrates three key components: (1) a ResNet50 backbone for deep, multi-scale feature extraction; (2) a convolutional block attention module (CBAM) to adaptively focus on salient residue features across both channel and spatial dimensions; and (3) a transformer-based global context fusion module (GCFM) to model long-range spatial dependencies, which is critical for interpreting heterogeneous residue patterns. We evaluated RCTUnet on a dataset of 1220 field-acquired images spanning four typical crop rotations. Experimental results show that, compared to traditional models: (1) RCTUnet achieves significantly higher crop-residue-soil segmentation accuracy than classic models including Unet, Unet++, DeepLabV3, segmentation network (SegNet), and fully convolutional network (FCN), with improvements of 3.24%, 3.42%, 4.88%, 8.28%, and 6.05% in overall accuracy, respectively; (2) RCTUnet yields superior residue-soil segmentation performance, with increases in residue recall of 7.67%, 7.37%, 14.09%, 27.05%, and 16.91%, respectively; (3) RCTUnet shows enhanced CRC estimation accuracy, achieving a root mean square error (RMSE) of 4.875, representing a 45.5% improvement over Unet (RMSE=8.941). These results demonstrate the efficacy of our hybrid approach, which combines deep hierarchical features, dual-domain attention, and global context modeling. RCTUnet provides a robust and reliable tool for automated CRC assessment, advancing the capabilities of in-field agricultural monitoring.</p>","PeriodicalId":17797,"journal":{"name":"Journal of Zhejiang University SCIENCE B","volume":"27 5","pages":"517-536"},"PeriodicalIF":5.7,"publicationDate":"2026-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13183486/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147973322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing multi-scale plant phenotyping for precision agriculture and sustainable crop production.","authors":"Xuping Feng, Zhenhai Li, Kun Wang","doi":"10.1631/jzus.B2610001","DOIUrl":"10.1631/jzus.B2610001","url":null,"abstract":"<p><p>Plant phenotyping captures the integrated structural and functional traits of crops across cellular, tissue, organ, whole-plant, and population scales. It represents the outward expression of genotype-environment interactions and provides essential technological support for precision breeding, smart agriculture, and sustainable crop production. As farming shifts from experience-based to data-driven decision-making, the efficient acquisition and integrated analysis of phenotypic information at multiple spatial scales has emerged as a major research frontier at the intersection of agronomy, plant science, and agricultural engineering.</p>","PeriodicalId":17797,"journal":{"name":"Journal of Zhejiang University SCIENCE B","volume":"27 5","pages":"431-436"},"PeriodicalIF":5.7,"publicationDate":"2026-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13183481/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147973319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}