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HTPRootSlides:A high-throughput phenotyping platform for crop root germination dynamic screening 认领 引用 被引量：1
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作者 Wenzhe Deng Jiafei Zhang +7 位作者 Jing Huang Hao Li Zhiguo Han Luyao Wang Xueying Guan Hongqing Ling Tingting Wu Weijuan Hu 《The Crop Journal》 SCIE CSCD 2026年第2期662-672,共11页
Root phenotyping is crucial for advancing our understanding of plant development and adaptation.However,existing platforms often face challenges in balancing high-throughput capacity with longterm,high-frequency monit... Root phenotyping is crucial for advancing our understanding of plant development and adaptation.However,existing platforms often face challenges in balancing high-throughput capacity with longterm,high-frequency monitoring.To overcome this limitation,we present HTPRootSlides,an integrated root phenotyping platform designed for dynamic and scalable trait analysis.Its design features a circulating zone that accommodates 141 specialized root boxes for high-throughput operation synchronously.Root boxes follow a continuous S-shaped trajectory step by step,facilitating repetitive imaging for high-throughput,time-series data acquisition.To address challenges such as water vapor condensation and fine root entanglement,we developed a dedicated segmentation algorithm,achieving 89.56%accuracy in root isolation.Combining morphological and skeleton-based feature extraction techniques,the platform ensures comprehensive and efficient phenotypic trait quantification.We validated HTPRootSlides by dynamically monitoring root development in four staple crops(soybean,maize,wheat,and rice)during early-stage germination(<14 d).The results demonstrate the capability of HTPRootSlides for high-frequency,high-precision and large-scale root phenotyping(<1 h with 141 root boxes per run),offering researchers a powerful tool to investigate root dynamics and optimize crop performance through trait selection. 展开更多
关键词 Root phenotyping High-throughput imaging Trait extraction Seedling development HTPRootSlides
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CornPheno:Phenotyping corn ear kernels in the wild via point query transformer 认领 引用
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作者 Xin Li Pinzhe Li +6 位作者 Xinzhe Wang Yaning Zhu Tianqi Hu Binghui Xu Yongshuai Zhang Zhiguo Han Hao Lu 《Plant Phenomics》 SCIE EI CSCD 2025年第4期178-193,共16页
Corn is a globally important economic crop.Certain trait parameters of corn ears kernels per ear are essential indicators for corn breeding.However,acquiring these parameters faces two challenges:i)manual measurement ... Corn is a globally important economic crop.Certain trait parameters of corn ears kernels per ear are essential indicators for corn breeding.However,acquiring these parameters faces two challenges:i)manual measurement is labor-intensive and error-prone,and ii)vision-based corn phenotyping machines require fixed image capturing environment and are cost-prohibitive.To address these limitations,we introduce CornPheno,a user-friendly,low-end,smartphone-based approach capable of executing corn ear phenotyping in the wild.CornPheno high-lights three corn ear parameters:kernels per ear,rows per ear,and kernels per row.Technically,inspired by crowd localization in computer vision,we first extract kernels per ear based on a Corn data-trained Point quEry Transformer(CornPET).CornPET generates interpretable per-kernel point predictions and supports subsequent row detection.To detect rows,we introduce a novel point-based corn row detection approach,termed uNicoRN,featured by squeezed clusteriNg and bI-direCtional pOint seaRchiNg,to phenotype rows per ear and kernels per row.With adaptive geometric modeling,our approach is robust to partial rows,curved rows,and missing ker-nels.To promote the use of CornPheno,we have integrated it into OpenPheno,a WeChat-based mini-program,and made it open-access for corn breeders.We hope our approach can provide the community with a user-friendly and cost-effective way to facilitate corn breeding. 展开更多
关键词 Plant phenotyping Corn ears Corn kernels Field-based phenotyping Plant counting
The blessing of Depth Anything:An almost unsupervised approach to crop segmentation with depth-informed pseudo labeling 认领 引用 被引量：2
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作者 Songliang Cao Binghui Xu +6 位作者 Wei Zhou Letian Zhou Jiafei Zhang Yuhui Zheng Weijuan Hu Zhiguo Han Hao Lu 《Plant Phenomics》 SCIE EI CSCD 2025年第1期49-65,共17页
We present Depth-Informed Crop Segmentation(DepthCropSeg),an almost unsupervised crop segmentation approach without manual pixel-level annotations.Crop segmentation is a fundamental vision task in agriculture,which be... We present Depth-Informed Crop Segmentation(DepthCropSeg),an almost unsupervised crop segmentation approach without manual pixel-level annotations.Crop segmentation is a fundamental vision task in agriculture,which benefits a number of downstream applications such as crop growth monitoring and yield estimation.Over the past decade,image-based crop segmentation approaches have shifted from classic color-based paradigms to recent deep learning-based ones.The latter,however,rely heavily on large amounts of data with high-quality manual annotation such that considerable human labor and time are spent.In this work,we leverage Depth Anything V2,a vision foundation model,to produce high-quality pseudo crop masks for training segmentation models.We compile a dataset of 17,199 images from six public plant segmentation sources,generating pseudo masks from depth maps after normalization and thresholding.After a coarse-to-fine manual screening,1378 images with reliable masks are selected.We compare four semantic segmentation models and enhance the top-performing one with depth-informed two-stage self-training and depth-informed post-processing.To evaluate the feasibility and robustness of DepthCropSeg,we benchmark the segmentation performance on 10 public crop segmentation testing sets and a self-collect dataset covering in-field,laboratory,and unmanned aerial vehicle(UAV)scenarios.Experimental results show that our DepthCropSeg approach can achieve crop segmentation performance comparable to the fully supervised model trained with manually annotated data(86.91 vs.87.10).For the first time,we demonstrate almost unsupervised,close-to-full-supervision crop segmentation successfully. 展开更多
关键词 Crop segmentation Plant phenotyping Depth anything Segment anything Efficient labeling
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