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Intelligent Recommendation and Matching Method for Agricultural Knowledge Based on Context-Aware Models

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Journal of beijing Institute of technology 2023年第3期32卷 341-351页

作者： Chang Liu Huarui Wu Huaji Zhu Yisheng Miao Jingqiu Gu Chunjiang Zhao National Engineering research center for information technology in agriculture Beijing 100097China information technology research center Beijing Academy of Agriculture and Forestry SciencesBeijing 100097China Key Laboratory of Digital Village technology Ministry of Agriculture and Rural AffairsBeijing 100097China

The personalized recommendation of the cloud platform for agricultural knowledge and agricultural intelligent service is one of the core technologies for the development of smart agriculture.Revealing the implicit laws and dynamic characteristics of agricultural knowledge demand is a key problem to be solved urgently.In order to enhance the matching ability of knowledge recommendation and service in human-computer interaction of cloud platform,the mechanism of agricultural knowledge intelligent recommendation service integrated with context-aware model was analyzed.By combining context data acquisition,data analysis and matching,and personalized knowledge recommendation,a framework for agricultural knowledge recommendation service is constructed to improve the ability to extract multidimensional information features and predict sequence data.Using the cloud platform for agricultural knowledge and agricultural intelligent service,this research aims to deliver interesting video service content to users in order to solve key problems faced by farmers,including planting technology,disease control,expert advice,etc.Then the knowledge needs of different users can be met and user satisfaction can be improved.

关键词： situational awareness agricultural knowledge intelligent recommendation service match

MVS-Pheno:A Portable and Low-Cost Phenotyping Platform for Maize Shoots Using Multiview Stereo 3D Reconstruction

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Plant Phenomics 2020年第1期2卷 40-56页

作者： Sheng Wu Weiliang Wen Yongjian Wang Jiangchuan Fan Chuanyu Wang Wenbo Gou Xinyu Guo Beijing Research Center for Information Technology in Agriculture Beijing 100097China National Engineering research center for information technology in agriculture Beijing 100097China beijing Key Lab of Digital Plant Beijing 100097China

Plant phenotyping technologies play important roles in plant research and agriculture.Detailed phenotypes of individual plants can guide the optimization of shoot architecture for plant breeding and are useful to analyze the morphological differences in response to environments for crop cultivation.Accordingly,high-throughput phenotyping technologies for individual plants grown in field conditions are urgently needed,and MVS-Pheno,a portable and low-cost phenotyping platform for individual plants,was developed.The platform is composed of four major components:a semiautomatic multiview stereo(MVS)image acquisition device,a data acquisition console,data processing and phenotype extraction software for maize shoots,and a data management system.The platform’s device is detachable and adjustable according to the size of the target shoot.Image sequences for each maize shoot can be captured within 60-120 seconds,yielding 3D point clouds of shoots are reconstructed using MVS-based commercial software,and the phenotypic traits at the organ and individual plant levels are then extracted by the software.The correlation coefficient(R^(2))between the extracted and manually measured plant height,leaf width,and leaf area values are 0.99,0.87,and 0.93,respectively.A data management system has also been developed to store and manage the acquired raw data,reconstructed point clouds,agronomic information,and resulting phenotypic traits.The platform offers an optional solution for high-throughput phenotyping of field-grown plants,which is especially useful for large populations or experiments across many different ecological regions.

关键词： breeding Stereo yielding

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 cultivation.However,high-throughput and accurate phenotypic measurement could accelerate the breeding and improve the existing cultivation management technology.In 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 Centre.The 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 breeding.The 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 sensors.Furthermore,it combines an RGB camera,a 6-band multispectral camera,a thermal infrared camera,a 3-dimensional laser radar,and a deep camera.Special software is developed to control motions and sensors and to design run lines.Using 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 sequence.Moreover,the LQ-FieldPheno has the characteristics of multiple data acquisition,vital timeliness,remarkable expansibility,high-cost performance,and flexible customization.The LQ-FieldPheno has been operated in the 2020 maize growing season,and the collected point cloud data are used to estimate the maize plant height.Compared 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 parameters.The proposed platform could contribute to the research of crop phenotype,remote sen

关键词： IoT networks Platform

A method for weighing broiler chickens using improved amplitude-limiting filtering algorithm and BP neural networks

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information Processing in agriculture 2021年第2期8卷 299-309页

作者： Weihong Ma Qifeng Li Jiawei Li Luyu Ding Qinyang Yu Beijing Research Center for Information Technology in Agriculture Beijing 100097China National Engineering research center for information technology in agriculture Beijing 100097China Key Laboratory of Agri-informatics Ministry of AgricultureBeijing 100097China beijing Engineering research center of agriculture Internet of Things Beijing 100097China

Broiler chickens are traditionally weighed by steelyard or platform scale,which is timeconsuming and labor-intensive.Broiler chickens usually exhibit stress-related behavior during weighing.The 3D camera-based weighing system for broiler chickens can only weigh the broiler chicken in the monitoring area.Usually,it makes poor weight prediction due to poor segmentation especially when the broiler chicken is flapping its wings.To solve these issues,we developed one simple and low-cost weighing system with high stability and accuracy.A validity value extraction method from dynamic weighing was proposed.Then,an improved amplitude-limiting filtering algorithm and a BP neural networks model were developed to avoid accidental interference.The BP neural networks model used daily weight gain,day-age,average velocity,and the weight data after filtering algorithm as the input layer.The weighing system was tested in a commercial beijing Fatty Chickens house with beijing Fatty Chickens.We tested thirteen groups of beijing Fatty Chickens of different weights,from 500 g to 1800 g in intervals of 100 g,using the three different methods:no filtering algorithm or BP neural networks,only the improved amplitude-limiting filtering algorithm and a hybrid of the improved amplitude-limiting filtering algorithm and BP neural networks.The results showed that the hybrid algorithm had a better performance in minimizing the error,lowering from the original 6%down to 3%.The accurate weight data was transmitted to the remote service platform for further decision-making,such as activity analysis,feeding management,and health alerts.

关键词： Weighing of broiler chickens Improved amplitude-limiting filtering algorithm BP neural networks Dynamic weighing

Design and experiment on scion cutting mechanism of grafting robot for cucurbit

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International Journal of Agricultural and Biological Engineering 2020年第5期13卷 99-106页

作者： Liping Chen Kai Jiang Qian Zhang Wenzhong Guo Wengang Zheng beijing research center of Intelligent Equipment for agriculture Beijing 100097China beijing research center of information technology for agriculture Beijing 100097China

The scion cutting mechanism of the traditional grafting machine cannot correct the bending degree of the scion seedling stem so that the cutting angle of the scion seedling is not standard and the survival rate of the grafted seedling is affected.In this study,cucumber seedlings and watermelon seedlings were taken as experimental objects.Based on the analysis of the geometric parameters and mechanical characteristics of scion seedlings,a scion cutting mechanism with holding seedling applied to cucumber seedlings and watermelon was designed and developed.The main structure and operating parameters of V-shaped holding seedling block and cutting unit were determined,and holding seedling and cutting performance tests were carried out.The test results showed that compared with the common arc clamp,the V-shaped holding seedling block had a better guiding and centering effect on the scion seedling,could better correct the bending state of the scion seedling stem,and the success rate of holding seedling of the V-shaped holding block reached 98.33%,which was increased by 13.33%compared with the arc-shaped clamp,and the seedling injury rate was only 1.67%.The upper and lower two-point positioning of scion stem by clamping hand and holding seedling block can improve the cutting performance effectively.The cutting accuracy rate of the scion was 97.75%,and the cutting success rate was 98.67%.The cutting performance was closely related to the holding seedling effect.The research results can provide a reference for the design of cutting mechanisms of melon grafting robots.

关键词： grafting robot cutting mechanism holding seedling block scion seedlings cucurbit

Design and optimization on rootstock cutting mechanism of grafting robot for cucurbit

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International Journal of Agricultural and Biological Engineering 2020年第5期13卷 117-124页

作者： Kai Jiang Qian Zhang Liping Chen Wenzhong Guo Wengang Zheng beijing research center of Intelligent Equipment for agriculture Beijing 100097China beijing research center of information technology for agriculture Beijing 100097China

Cutting mechanisms in existing grafting machines are unable to completely cut through the rootstock growth point and can easily damage seedlings.During the mechanical operation of splice grafting,the cutting angle of the rootstock is an essential factor for ensuring the quality and survival rate of grafting seedlings and a stable process for grafting robots.Therefore,in this study,commonly used grafting rootstocks,e.g.,cucurbita moschata,and calabash gourd were used as research objects for studying and analyzing the cutting angle of a splice grafting method.The morphological and structural parameters of the rootstock and scion were measured,and a structural model of the internal cavity of the rootstock was constructed using an image analysis method.The critical cutting angles for the cucurbita moschata and calabash gourd seedlings were obtained.According to the analysis,the grafting cutting angles for cucumber seedlings matching with cucurbita moschata seedlings were 20°and 25°,respectively,and the fitting rate of the cutting surface of the rootstock and scion was 99.04%.A cutting mechanism for the rootstock growth point and geometric model of the cutting operation were established,and the structural parameters of the mechanism and cutting angle adjustment were optimized.A cutting performance test showed that the success rate of the pressing the cotyledons of cucurbita moschata seedlings was 96.67%,and the success rate of cutting was 98%.The cutting accuracy was 96.8%,and the cutting surface fitting rate of the rootstock and scion was 98.61%.The latter differed by 0.43%from the theoretical rate but met the requirements for the splice grafting method.Thus,this study can provide a reference for the design of a cutting mechanism for a grafting robot.

关键词： grafting robot rootstock cavity cutting angle matched grafting parameter optimization cucurbit

A deep learning-integrated phenotyping pipeline for vascular bundle phenotypes and its application in evaluating sap flow in the maize stem

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The Crop Journal 2022年第5期10卷 1424-1434页

作者： Jianjun Du Ying Zhang Xianju Lu Minggang Zhang Jinglu Wang Shengjin Liao Xinyu Guo Chunjiang Zhao beijing Key Lab of Digital Plant Research Center of Information TechnologyBeijing Academy of Agriculture and Forestry SciencesBeijing 100097China

Plant vascular bundles are responsible for water and material transportation, and their quantitative and functional evaluation is desirable in plant research. At the single-plant level, the number, size, and distribution of vascular bundles vary widely, posing a challenge to automatically and accurately identifying and quantifying them. In this study, a deep learning-integrated phenotyping pipeline was developed to robustly and accurately detect vascular bundles in Computed Tomography(CT) images of stem internodes. Two semantic indicators were used to evaluate and identify a suitable feature extraction network for semantic segmentation models. The epidermis thickness of maize stem was evaluated for the first time and adjacent vascular bundles were improved using an adaptive watershed-based approach. The counting accuracy(R^(2)) of vascular bundles was 0.997 for all types of stem internodes, and the measured accuracy of size traits was over 0.98. Combining sap flow experiments, multiscale traits of vascular bundles were evaluated at the single-plant level, which provided an insight into the water use efficiency of the maize plant.

关键词： Deep learning Maize stem Phenotyping Semantic segmentation Vascular bundle

Non-uniform clustering routing protocol of wheat farmland based on effective energy consumption

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International Journal of Agricultural and Biological Engineering 2021年第3期14卷 163-170页

作者： Yisheng Miao Chunjiang Zhao Huarui Wu College of Computer Science Beijing University of TechnologyBeijing 100124China National Engineering research center for information technology in agriculture Beijing 100097China Beijing Research Center for Information Technology in Agriculture Beijing Academy of Agriculture and Forestry SciencesBeijing100097China

Wireless sensor network(WSN)can achieve real-time data collection and transmission of environment,soil,meteorology,crop physiology and other information in agriculture.The data provided by WSN could be used for decision making and management,which is very important in precision agriculture.Wheat farmland wireless sensor network has the characteristics of wide coverage area,long planting period,inconvenient energy supply,and serious impact of crop environment on wireless signal transmission.Routing protocol is an important method to achieve long-term WSN monitoring by selecting an appropriate path with low energy consumption for data transmission.According to the phenomenon of uneven environment and channel parameters caused by intensive crop growth in farmland,a non-uniform clustering routing protocol based on effective energy consumption(UCEEC)was proposed in this work.The method combined with the characteristics of multi-path fading of farmland environment signals.The idea of image segmentation was introduced.Nodes with high similarity were divided into a cluster area by the dissimilarity between nodes in order to improve the intracluster communication performance.Meanwhile,a multi-hop path selection method between cluster-heads based on the estimation of two-hop effective energy consumption is designed.The energy consumption cost factor is calculated by the effective energy consumption and the average energy consumption within the cluster to achieve the minimum and balance of the overall energy consumption of the network.Simulation results show that,compared with the existing Maximum Residual Energy Based Routing(MREBR)protocol,minimum Energy Consumption Based Routing(MEC)routing protocols,UCEEC improves the energy balance effect between nodes,prolongs the network life cycle,and realizes efficient energy utilization of wireless sensor network data collection in the complex environment of wheat field.

关键词： farmland wireless sensor networks multi-path fading channel routing protocol energy optimization

Discovery of leaf region and time point related modules and genes in maize(Zea mays L.)leaves by Weighted Gene Co-expression Network analysis(WGCNA)of gene expression profiles of carbon metabolism

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Journal of Integrative agriculture 2019年第2期18卷 350-360页

作者： WANG Jing-lu ZHANG Ying PAN Xiao-di DU Jian-jun MA Li-ming GUO Xin-yu beijing Key Lab of Digital Plant/Beijing Research Center for Information Technology in Agriculture/National Engineering research center for information technology in agriculture/beijing Academy of agriculture and Forestry Sciences Beijing 100097P.R.China

Maize(Zea mays L.) yield depends not only on the conversion and accumulation of carbohydrates in kernels, but also on the supply of carbohydrates by leaves. However, the carbon metabolism process in leaves can vary across different leaf regions and during the day and night. Hence, we used Weighted Gene Co-expression Network analysis(WGCNA) with the gene expression profiles of carbon metabolism to identify the modules and genes that may associate with particular regions in a leaf and time of day. There were a total of 45 samples of maize leaves that were taken from three different regions of a growing maize leaf at five time points. Robust Multi-array Average analysis was used to pre-process the raw data of GSE85963(accession number), and quality control of data was based on Pearson correlation coefficients. We obtained eight co-expression network modules. The modules with the highest significance of association with LeafRegion and TimePoint were selected. Functional enrichment and gene-gene interaction analyses were conducted to acquire the hub genes and pathways in these significant modules. These results can support the findings of similar studies by providing evidence of potential module genes and enriched pathways associated with leaf development in maize.

关键词： WGCNA maize leaf gene expression gene modules pathways