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A deep learning-based micro-CT image analysis pipeline for nondestructive quantification of the maize kernel internal structure

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植物表型组学（英文） 2025年第1期7卷 225-238页

作者： Juan Wang Si Yang Chuanyu Wang Weiliang Wen Ying Zhang Gui Liu Jingyi Li Xinyu Guo Chunjiang Zhao College of Information Shanghai Ocean UniversityShanghai201306China Information Technology Research Center Beijing Academy of Agriculture and Forestry SciencesBeijing100097China Beijing Key Lab of Digital Plants National Engineering Research Center for Information Technology in AgricultureBeijing100097China Information Technology Research Center Beijing Academy of Agriculture and Forestry SciencesBeijing100097China Beijing Key Lab of Digital Plants National Engineering Research Center for Information Technology in AgricultureBeijing100097China College of Information Shanghai Ocean UniversityShanghai201306China College of Computer Science and Engineering Southwest Minzu UniversityChengduSichuan610225China College of Information Shanghai Ocean UniversityShanghai201306China Information Technology Research Center Beijing Academy of Agriculture and Forestry SciencesBeijing100097China

Identifying and segmenting the vitreous and starchy endosperm of maize kernels is essential for texture ***,the complex internal structure of maize kernels presents several *** CT(computed tomog-raphy)images,the pixel intensity differences between the vitreous and starchy endosperm regions in maize kernel CT images are not distinct,potentially leading to low segmentation accuracy or ***,the blurred edges between the vitreous and starchy endosperm make segmentation difficult,often resulting in jagged segmentation *** propose a deep learning-based CT image analysis pipeline to examine the internal structure of maize ***,CT images are acquired using a multislice CT *** improve the efficiency of maize kernel CT imaging,a batch scanning method is *** kernels are accurately segmented from batch-scanned CT images using the Canny ***,we modify the conventional architecture for high-quality segmentation of the vitreous and starchy endosperm in maize *** conventional U-Net is modified by integrating the CBAM(convolutional block attention module)mechanism in the encoder and the SE(squeeze-and-excitation attention)mechanism in the decoder,as well as by using the focal-Tversky loss function instead of the Dice loss,and the boundary smoothing term is weighted as an additional loss term,named *** experimental results show that the CSFTU-Net model significantly improves the ability of segmenting vitreous and starchy ***,a segmented mask-based method is proposed to extract phenotype parameters of maize kernel texture,including the volume of the kernel(V),volume of the vitreous endosperm(VV),volume of starchy endosperm(SV),and ratios over their respective total kernel volumes(W/V and SV/V).The proposed pipeline facilitates the nondestructive quantification of the internal structure of maize kernels,offering valuable insights for maize breeding and processing.

关键词： Maize kernel Vitreous endosperm Starchy endosperm Semantic segmentation Mirco-CT

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Identifying key factors influencing maize stalk lodging resistance through wind tunnel simulations with machine learning algorithms

Artificial Intelligence in Agriculture

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Artificial Intelligence in agriculture 2025年第2期15卷 316-326页

作者： Guanmin Huang Ying Zhang Shenghao Gu Weiliang Wen Xianju Lu Xinyu Guo Information Technology Research Center Beijing Academy of Agriculture and Forestry SciencesBeijing Key Laboratory of Digital PlantChina National Engineering Research Center for Information Technology in AgricultureBeijing 100097China

Climate change has intensified maize stalk lodging,severely impacting global maize *** numerous traits influence stalk lodging resistance,their relative importance remains unclear,hindering breeding *** study introduces an combining wind tunnel testing with machine learning algorithms to quantitatively evaluate stalk lodging resistance *** extensive field experiments and literature review,we identified and measured 74 phenotypic traits encompassing plant morphology,biomass,and anatomical characteristics in maize *** analysis revealed a median linear correlation coefficient of 0.497 among these traits,with 15.1%of correlations exceeding *** component analysis showed that the first five components explained 90%of the total variance,indicating significant trait *** feature engineering and gradient boosting regression,we developed a high-precision wind speed-ear displacement prediction model(R^(2)=0.93)and identified 29 key traits critical for stalk lodging *** analysis revealed plant height as the most influential factor(sensitivity coefficient:−3.87),followed by traits of the 7th internode including epidermis layer thickness(0.62),pith area(−0.60),and lignin content(0.35).Our methodological framework not only provides quantitative insights into maize stalk lodging resistance mechanisms but also establishes a systematic approach for trait *** findings offer practical guidance for breeding programs focused on enhancing stalk lodging resistance and yield stability under climate change conditions,with potential applications in agronomic practice optimization and breeding strategy development.

关键词： Maize Stalk lodging Wind tunnel testing Machine learning algorithms Indicator identification

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La^(3+)@BC500-S_(2)O_(8)^(2−)system for removal of sulfonamide antibiotics in water

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Journal of Environmental Sciences 2024年第12期146卷 140-148页

作者： Bing Wang Dongsu Bi Juan Wang Hengbo Qiao Yalei Zhang Zheng Shen School of Chemical and Environmental Engineering Shanghai Institute of TechnologyShanghai 201418China State Key Laboratory of Pollution Control and Resources Reuse Key Laboratory of Yangtze River Water Environment of MOENational Engineering Research Center of Protected AgricultureShanghai Engineering Research Center of Protected AgricultureTongji UniversityShanghai 200092China

Sulfonamide antibiotics(SAs)widely used have potentially negative effects on human beings and *** and advanced oxidation methods have been exten-sively applied in SAs wastewater *** this study,compared with Al^(3+)@BC500 and Fe^(3+)@BC500,La^(3+)@BC500 for activating persulfate(S2O82−)had the best effect removal per-formance of sulfadiazine(SDZ)and sulfamethoxazole(SMX).Morphology,acidity,oxygen-containing functional groups,and loading of La^(3+)@BC500 were analyzed by techniques,in-cluding EA,BET,XRD,XPS,*** results show that with the increase of La^(3+)loading,the surface characteristics of biochar gradually changed from CaCO_(3)to LaCO_(3)*** EPR technology,it is proved that LaCO_(3)OH on the surface of La^(3+)@BC500 can not only acti-vate S2O82−to generate SO_(4)^(−)•,but also to produce•*** the optimization experiment,the optimal dosage of La^(3+)is between 0.05 and 0.2(mol/L)/*** had a good removal effect at pH(5-9),but SMX had a good removal effect only at pH=*** potential also proves that the material is more stable under acidic *** removal process of SDZ is more in accord with pseudo-first-order kinetics(R^(2)=0.9869),while SMX is more in line with pseudo-second order kinetics(R^(2)=0.9926).

关键词： Sulfadiazine Sulfamethoxazole La^(3+)@BC500 Persulfate

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Application of Internet of Things to agriculture—The LQ-FieldPheno Platform:A High-Throughput Platform for Obtaining Crop Phenotypes in Field

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research 2023年第3期6卷 17-33页

作者： Jiangchuan Fan Yinglun Li Shuan Yu Wenbo Gou Xinyu Guo Chunjiang Zhao Bejing Key Laboratory of Digital Plant National Engineering Research Center for Information Technology in AgricultureBeijing 100097China Beijing Research Center for Information Technology in Agriculture.Beijing Academy of Agriculture and Forestry Sciences Bejing 100097China China NationalEngineering Research Center for Information Technology in Agriculture(NERCITA) Beijing 100097China

The lack of efficient crop phenotypic measurement methods has become a bottleneck in the field of breeding and precision ***,high-throughput and accurate phenotypic measurement could accelerate the breeding and improve the existing cultivation management *** view of this,this paper introduces a high-throughput crop phenotype measurement platform named the LQ-FieldPheno,which was developed by China National Agricultural Information engineering technology research *** proposed platform represents a mobile phenotypic high-throughput automatic acquisition system based on a field track platform,which introduces the Internet of Things(IoT)into agricultural *** proposed platform uses the crop phenotype multisensor central imaging unit as a core and integrates different types of equipment,including an automatic control system,upward field track,intelligent navigation vehicle,and environmental ***,it combines an RGB camera,a 6-band multispectral camera,a thermal infrared camera,a 3-dimensional laser radar,and a deep *** software is developed to control motions and sensors and to design run *** wireless sensor networks and mobile communication wireless networks of IoT,the proposed system can obtain phenotypic information about plants in their growth period with a high-throughput,automatic,and high time ***,the LQ-FieldPheno has the characteristics of multiple data acquisition,vital timeliness,remarkable expansibility,high-cost performance,and flexible *** LQ-FieldPheno has been operated in the 2020 maize growing season,and the collected point cloud data are used to estimate the maize plant *** with the traditional crop phenotypic measurement technology,the LQ-FieldPheno has the advantage of continuously and synchronously obtaining multisource phenotypic data at different growth stages and extracting different plant *** proposed platform could contrib

关键词： IoT networks Platform

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Comprehensive review on 3D point cloud segmentation in plants

Artificial Intelligence in Agriculture

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Artificial Intelligence in agriculture 2025年第2期15卷 296-315页

作者： Hongli Song Weiliang Wen Sheng Wu Xinyu Guo Information Technology Research Center Beijing Academy of Agriculture and Forestry SciencesBeijing 100097China Beijing Key Lab of Digital Plant National Engineering Research Center for Information Technology in AgricultureBeijing 100097China College of Information Engineering Northwest A&F UniversityYangling 712100China

Segmentation of three-dimensional(3D)point clouds is fundamental in comprehending unstructured structural and morphological *** plays a critical role in research related to plant phenomics,3D plant modeling,and functional-structural plant *** technologies for plant point cloud segmentation(PPCS)have advanced rapidly,there has been a lack of a systematic overview of the development *** paper presents an overview of the progress made in 3D point cloud segmentation research in *** starts by discussing the methods used to acquire point clouds in plants,and analyzes the impact of point cloud resolution and quality on the segmentation *** then introduces multi-scale point cloud segmentation in *** paper summarizes and analyzes traditional methods for PPCS,including the global and local *** paper discusses the progress of machine learning-based segmentation on plant point clouds through supervised,unsupervised,and integrated *** also summarizes the datasets that for PPCS using deep learning-oriented methods and explains the advantages and disadvantages of deep learning-based methods for projection-based,voxel-based,and point-based approaches ***,the development of PPCS is discussed and *** learning methods are predicted to become dominant in the field of PPCS,and 3D point cloud segmentation would develop towards more automated with higher resolution and precision.

关键词： Plant Three-dimensional Point cloud Segmentation Multi-scale Deep learning

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Revolutionizing Crop Breeding:Next-Generation Artificial Intelligence and Big Data-Driven Intelligent Design

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工程（英文） 2025年第1期44卷 245-255页

作者： Ying Zhang Guanmin Huang Yanxin Zhao Xianju Lu Yanru Wang Chuanyu Wang Xinyu Guo Chunjiang Zhao Information Technology Research Center Beijing Academy of Agriculture and Forestry SciencesBeijing 100097China National Engineering Research Center for Information Technology in Agriculture Beijing 100097China Beijing Key Laboratory of Digital Plant Beijing Academy of Agriculture and Forestry SciencesBeijing 100097China Beijing Key Laboratory of Maize DNA Fingerprinting and Molecular Breeding Maize Research CenterBeijing Academy of Agriculture and Forestry SciencesBeijing 100097China

The security of the seed industry is crucial for ensuring national food ***,developed coun-tries in Europe and America,along with international seed industry giants,have entered the Breeding 4.0 *** era integrates biotechnology,artificial intelligence(AI),and big data information *** contrast,China is still in a transition period between stages 2.0 and 3.0,which primarily relies on conven-tional selection and molecular *** the context of increasingly complex international situations,accurately identifying core issues in China's seed industry innovation and seizing the frontier of interna-tional seed technology are strategically *** efforts are essential for ensuring food security and revitalizing the seed *** paper systematically analyzes the characteristics of crop breeding data from artificial selection to intelligent design *** explores the applications and development trends of AI and big data in modern crop breeding from several key *** include high-throughput phenotype acquisition and analysis,multiomics big data database and management system construction,AI-based multiomics integrated analysis,and the development of intelligent breeding soft-ware tools based on biological big data and AI *** on an in-depth analysis of the current status and challenges of China's seed industry technology development,we propose strategic goals and key tasks for China's new generation of AI and big data-driven intelligent design *** sugges-tions aim to accelerate the development of an intelligent-driven crop breeding engineering system that features large-scale gene mining,efficient gene manipulation,engineered variety design,and system-atized *** study provides a theoretical basis and practical guidance for the development of China's seed industry technology.

关键词： Crop breeding Next-generation artificial intelligence Multiomics big data Intelligent design breeding

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research on the stroke control algorithm of agricultural push rod motor

Research on the stroke control algorithm of agricultural pus...

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2024 International Conference on Electronics, Electrical and Information engineering, ICEEIE 2024

作者： Liu, Yong Wang, Yunsheng Hu, Wenwen Xu, Shipu Zhou, Zhiwen An, Mingming Guo, Xiaokun Jiang, Wenfei Institute of Agricultural Science and Technology Information Shanghai Academy of Agricultural Sciences Shanghai201403 China Shanghai Engineering and Technology Research Center for Digital Agriculture Shanghai201403 China

ISBN: (数字)9781510686809

ISBN: (纸本)9781510686793

This paper studies and realizes a low-cost stroke control algorithm for agricultural push rod motor. The algorithm is based on the hall counting principle. By adding two hall sensors to the ordinary agricultural push rod motor, the motor can output two square wave signals when it is running. When the motor is running, each revolution will trigger a hall sensor to output a pulse signal. The two signals have a phase difference of 90 degrees. Through the phase relationship of the two signals, the forward and reverse state of the motor can be judged, and the pulse signal can be counted by adding or subtracting. The algorithm realizes the automatic control of motor stroke according to the user set stroke percentage, hall count value and total stroke hall count value. This algorithm has the advantages of stable and reliable stroke control, low cost, suitable for high humidity and dusty environment in agriculture, and can be applied to the on-site automatic control of related equipment in facility agriculture and field agriculture, as well as remote precision control based on Agricultural Internet of things. © 2024 SPIE.

关键词： Hall effect transducers

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Standard Framework Construction of technology and Equipment for Big Data in Crop Phenomics

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engineering 2024年第11期42卷 175-184页

作者： Weiliang Wen Shenghao Gu Ying Zhang Wanneng Yang Xinyu Guo Information Technology Research Center Beijing Academy of Agriculture and Forestry SciencesBeijing 100097China Beijing Key Lab of Digital Plant National Engineering Research Center for Information Technology in AgricultureBeijing 100097China National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research Huazhong Agricultural UniversityWuhan 430070China

Crop phenomics has rapidly progressed in recent years due to the growing need for crop functional geno-mics,digital breeding,and smart *** this advancement,the lack of standards for the cre-ation and usage of crop phenomics technology and equipment has become a bottleneck,limiting the industry’s high-quality *** paper begins with an overview of the crop phenotyping indus-try and presents an industrial mapping of technology and equipment for big data in crop *** analyzes the necessity and current state of constructing a standard framework for crop ***,this paper proposes the intended organizational structure and goals of the standard *** details the essentials of the standard framework in the research and development of hardware and equipment,data acquisition,and the storage and management of crop phenotyping ***,it discusses promoting the construction and evaluation of the standard framework,aiming to provide ideas for developing a high-quality standard framework for crop phenotyping.

关键词： Crop phenomics Big data Phenotyping technology and equipment Standard framework Industrial mapping

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Three-Dimensional Modeling of Maize Canopies Based on Computational Intelligence

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Plant Phenomics 2024年第2期6卷 349-363页

作者： Yandong Wu Weiliang Wen Shenghao Gu Guanmin Huang Chuanyu Wang Xianju Lu Pengliang Xiao Xinyu Guo Linsheng Huang National Engineering Research Center for Agro-Ecological Big Data Analysis&Application Anhui UniversityHefei 230601China Information Technology Research Center Beijing Academy of Agriculture and Forestry SciencesBeijing 100097China Beijing Key Lab of Digital Plant National Engineering Research Center for Information Technology in AgricultureBeijing 100097China Nongxin Science&Technology(Beijing)Co. LtdBeijing 100097China

The 3-dimensional(3D)modeling of crop canopies is fundamental for studying functional-structural plant *** studies often fail to capture the structural characteristics of crop canopies,such as organ overlapping and resource *** address this issue,we propose a 3D maize modeling method based on computational *** initial 3D maize canopy is created using the t-distribution method to reflect characteristics of the plant architecture.

关键词： intelligence modeling based canopies computational dimensional maize three

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A review on enhancing agricultural intelligence with large language models

Artificial Intelligence in Agriculture

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Artificial Intelligence in agriculture 2025年第4期15卷 671-685页

作者： Li, Hongda Wu, Huarui Li, Qingxue Zhao, Chunjiang College of Information and Elect1rical Engineering China Agricultural University Beijing100083 China National Engineering Research Center for Information Technology in Agriculture Beijing100101 China Information Technology Research Center Beijing Academy of Agriculture and Forestry Sciences Beijing100097 China Key Laboratory of Digital Village Technology Ministry of Agriculture and Rural Affairs Beijing100125 China

This paper systematically explores the application potential of large language models (LLMs) in the field of agricultural intelligence, focusing on key technologies and practical pathways. The study focuses on the adaptation of LLMs to agricultural knowledge, starting with foundational concepts such as architecture design, pre-training strategies, and fine-tuning techniques, to build a technical framework for knowledge integration in the agricultural domain. Using tools such as vector databases and knowledge graphs, the study enables the structured development of professional agricultural knowledge bases. Additionally, by combining multimodal learning and intelligent question-answering (Q&A) system design, it validates the application value of LLMs in agricultural knowledge services. Addressing core challenges in domain adaptation, including knowledge acquisition and integration, logical reasoning, multimodal data processing, agent collaboration, and dynamic knowledge updating, the paper proposes targeted solutions. The study further explores the innovative applications of LLMs in scenarios such as precision crop management and market dynamics analysis, providing theoretical support and technical pathways for the development of agricultural intelligence. Through the technological innovation of large language models and their deep integration with the agricultural sector, the intelligence level of agricultural production, decision-making, and services can be effectively enhanced. © 2025 The Authors

关键词： Knowledge acquisition

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