检索结果-南通市图书馆

Improved yield prediction of Ratoon Rice Using Unmanned Aerial Vehicle-Based Multi-Temporal Feature Method

Rice science 2023年第3期30卷 247-256,I0039-I0042页

作者： ZHOU Longfei MENG Ran YU Xing LIAO Yigui HUANG Zehua LÜZhengang XU Binyuan YANG Guodong PENG Shaobing XU Le College of Resources and Environment Huazhong Agricultural UniversityWuhan 430070China National Key Laboratory of Crop Genetic Improvement/Hubei Hongshan Laboratory/Key Laboratory of Crop Ecophysiology and Farming System in the Middle Reaches of the Yangtze River Ministry of Agriculture and Rural Affairs/College of Plant Science and TechnologyHuazhong Agricultural UniversityWuhan 430070China College of Agriculture Northeast Agricultural UniversityHarbin 150006China Harbin Institute of Technology Institute for Artificial Intelligence Co. LtdHarbin 150000China

Pre-harvest yield prediction of ratoon rice is critical for guiding crop interventions in precision ***,the unique agronomic practice(i.e.,varied stubble height treatment)in rice ratooning could lead to inconsistent rice phenology,which had a significant impact on yield prediction of ratoon ***-temporal unmanned aerial vehicle(UAV)-based remote sensing can likely monitor ratoon rice productivity and reflect maximum yield potential across growing seasons for improving the yield prediction compared with previous ***,in this study,we explored the performance of combination of agronomic practice information(API)and single-phase,multi-spectral features[vegetation indices(VIs)and texture(Tex)features]in predicting ratoon rice yield,and developed a new UAV-based method to retrieve yield formation process by using multi-temporal features which were effective in improving yield forecasting accuracy of ratoon *** results showed that the integrated use of VIs,Tex and API(VIs&Tex+API)improved the accuracy of yield prediction than single-phase UAV imagery-based feature,with the panicle initiation stage being the best period for yield prediction(R^(2) as 0.732,RMSE as 0.406,RRMSE as 0.101).More importantly,compared with previous multi-temporal UAV-based methods,our proposed multi-temporal method(multi-temporal model VIs&Tex:R^(2) as 0.795,RMSE as 0.298,RRMSE as 0.072)can increase R^(2) by 0.020-0.111 and decrease RMSE by 0.020-0.080 in crop yield *** study provides an effective method for accurate pre-harvest yield prediction of ratoon rice in precision agriculture,which is of great significance to take timely means for ensuring ratoon rice production and food security.

关键词： ratoon rice yield prediction unmanned aerial vehicle multi-temporal feature agronomic practice stubble height

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Multimodal Machine Learning Based Crop Recommendation and yield prediction Model

引用

Intelligent Automation & Soft Computing 2023年第4期36卷 313-326页

作者： P.S.S.Gopi M.Karthikeyan Department of Computer and Information Science Faculty of ScienceAnnamalai UniversityChidambaram608002India

Agriculture plays a vital role in the Indian *** recommen-dation for a speciﬁc region is a tedious process as it can be affected by various variables such as soil type and climatic *** the same time,crop yield prediction was based on several features like area,irrigation type,temperature,*** recent advancements of artiﬁcial intelligence(AI)and machine learning(ML)models pave the way to design effective crop recommendation and crop pre-diction *** this view,this paper presents a novel Multimodal Machine Learning Based Crop Recommendation and yield prediction(MMML-CRYP)*** proposed MMML-CRYP model mainly focuses on two processes namely crop recommendation and crop *** the initial stage,equilibrium optimizer(EO)with kernel extreme learning machine(KELM)technique is employed for effectual recommendation of ***,random forest(RF)tech-nique was executed for predicting the crop yield *** reporting the improved performance of the MMML-CRYP system,a wide range of simulations were carried out and the results are investigated using benchmark ***-mentation outcomes highlighted the signiﬁcant performance of the MMML-CRYP approach on the compared approaches with maximum accuracy of 97.91%.

关键词： Agriculture crop recommendation yield prediction machine learning artiﬁcial intelligence

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

TrG2P:A transfer-learning-based tool integrating multi-trait data for accurate prediction of crop yield

引用

Plant Communications 2024年第7期5卷 16-27页

作者： Jinlong Li Dongfeng Zhang Feng Yang Qiusi Zhang Shouhui Pan Xiangyu Zhao Qi Zhang Yanyun Han Jinliang Yang Kaiyi Wang 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 Department of Agronomy and Horticulture University of Nebraska-LincolnLincolnNE 68583USA Center for Plant Science Innovation University of Nebraska-LincolnLincolnNE 68583USA These authors contributed equally to this article

yield prediction is the primary goal of genomic selection(GS)-assisted crop *** yield is a complex quantitative trait,making predictions from genotypic data is *** learning can produce an effective model for a target task by leveraging knowledge from a different,but related,source domain and is considered a great potential method for improving yield prediction by integrating multi-trait ***,it has not previously been applied to genotype-to-phenotype prediction owing to the lack of an efficient implementation *** therefore developed TrG2P,a transfer-learning-based ***2P first employs convolutional neural networks(CNN)to train models using non-yield-trait phenotypic and genotypic data,thus obtaining pre-trained ***,the convolutional layer parameters from these pre-trained models are transferred to the yield prediction task,and the fully connected layers are retrained,thus obtaining fine-tuned ***,the convolutional layer and the first fully connected layer of the fine-tuned models are fused,and the last fully connected layer is trained to enhance prediction *** applied TrG2P to five sets of genotypic and phenotypic data from maize(Zea mays),rice(Oryza sativa),and wheat(Triticum aestivum)and compared its model precision to that of seven other popular GS tools:ridge regression best linear unbiased prediction(rrBLUP),random forest,support vector regression,light gradient boosting machine(LightGBM),CNN,DeepGS,and deep neural network for genomic prediction(DNNGP).TrG2P improved the accuracy of yield prediction by 39.9%,6.8%,and 1.8%in rice,maize,and wheat,respectively,compared with predictions generated by the best-performing comparison *** work therefore demonstrates that transfer learning is an effective strategy for improving yield prediction by integrating information from non-yield-trait *** attribute its enhanced prediction accuracy to the valuable information ava

关键词： crop genotype to phenotype transfer learning yield prediction multi-trait

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

An efficient prediction framework for multi-parametric yield analysis under parameter variations

引用

Frontiers of Information Technology & Electronic Engineering 2016年第12期17卷 1344-1359页

作者： Xin LI Jin SUN Fu XIAO Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing University of Posts and Telecommunications Nanjing 210013 China Technology Innovation Center Jiangsu Academy of Safety Science and Technology Nanjing 210042 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing 210094 China

Due to continuous process scaling, process, voltage, and temperature （PVT） parameter variations have become one of the most problematic issues in circuit design. The resulting correlations among performance metrics lead to a significant parametric yield loss. Previous algorithms on parametric yield prediction are limited to predicting a single-parametric yield or performing balanced optimization for several single-parametric yields. Consequently, these methods fail to predict the multiparametric yield that optimizes multiple performance metrics simultaneously, which may result in significant accuracy loss. In this paper we suggest an efficient multi-parametric yield prediction framework, in which multiple performance metrics are considered as simultaneous constraint conditions for parametric yield prediction, to maintain the correlations among metrics. First, the framework models the performance metrics in terms of PVT parameter variations by using the adaptive elastic net （AEN） method. Then the parametric yield for a single performance metric can be predicted through the computation of the cumulative distribution function （CDF） based on the multiplication theorem and the Markov chain Monte Carlo （MCMC） method. Finally, a copula-based parametric yield prediction procedure has been developed to solve the multi-parametric yield prediction problem, and to generate an accurate yield estimate. Experimental results demonstrate that the proposed multi-parametric yield prediction framework is able to provide the designer with either an accurate value for parametric yield under specific performance limits, or a multi-parametric yield surface under all ranges of performance limits.

关键词： yield prediction Parameter variations Multi-parametric yield Performance modeling Sparse representation

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

An improved shape shifting method of critical area extraction

引用

Journal of Semiconductors 2014年第2期35卷 155-162页

作者：朱椒娇罗小华陈立生叶翼严晓浪 Institute of VLSI Design Zhejiang University

As die size and complexity increase, accurate and efficient extraction of the critical area is essential for yield prediction. Aiming at eliminating the potential integration errors of the traditional shape shifting method, an improved shape shifting method is proposed for Manhattan layouts. By mathematical analyses of the relevance of critical areas to defect sizes, the critical area for all defect sizes is modeled as a piecewise quadratic polynomial function of defect size, which can be obtained by extracting critical area for some certain defect sizes. Because the improved method calculates critical areas for all defect sizes instead of several discrete values with traditional shape shifting method, it eliminates the integration error of the average critical area. Experiments on industrial layouts show that the improved shape shifting method can improve the accuracy of the average critical area calculation by 24.3% or reduce about 59.7% computational expense compared with the traditional method.

关键词： critical area extraction shape shifting method Voronoi diagram mathematical modeling layout analysis yield prediction

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

prediction of Multicomponent Reaction yields Using Machine Learning

引用

Chinese Journal of Chemistry 2021年第12期39卷 3231-3237页

作者： Xing-Yong Zhu Chuan-Kun Ran Ming Wen Gui-Ling Guo Yuan Liu Li-Li Liao Yi-Zhou Li Meng-Long Li Da-Gang Yu Key Laboratory of Green Chemistry&Technology of Ministry of Education College of ChemistrySichuan UniversityChengduSichuan 610064China College of Cybersecurity Sichuan UniversityChengduSichuan 610064China Beijing National Laboratory for Molecular Sciences Beijing 100190China

prediction of reaction yields using machine learning(ML)can help chemists select high-yielding reactions and provide prior experience before wet-lab experimenting to improve ***,the exploration of a multicomponent organic reaction features many complex variables and limited number of experimental data,which are challenging for the application of ***,we perform yield prediction for the synthesis of 2-oxazolidones via Cu-catalyzed radical-type oxy-alkylation of allylamines and herteroaryl-methylamines with CO_(2),which is a three-component *** physicochemical descriptors as features to launch ML modelling,we find that XGBoost shows significantly improved performance over linear models and these features are effective for the yield ***,out-of-sample prediction indicates the application potential of the *** study demonstrates great potential of regression-modelling-based ML in organic synthesis even with complex factors and a general small size of reaction data,which are generated from the classical research pattern of method for the inquiry of multicomponent reactions.

关键词： Machine,learning yield prediction Radical reactions Density functional calculations Multicomponent reactions

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

An adaptive Mealy machine model for monitoring crop status

引用

Journal of Integrative Agriculture 2017年第2期16卷 252-265页

作者： Berk Ustundag Agricultural and Environmental Informatics Application & Research Center/Faculty of Informatics and Computer Engineering Istanbul Technical University Istanbul 34469 Turkey

Variation in phenological stage is the major nonlinearity in monitoring, modeling and various estimations of agricultural systems. Indices are used as a common means of evaluating agricultural monitoring data from remote sensing and terrestrial observation systems, and many of these indices have linear characteristics. The analysis of and relationships between indices are dependent on the type of plant, but they are also highly variable with respect to its phenologicat stage. For this reason, variations in the phenologica! stage affect the performance of spatiotemporal crop status monitoring. We hereby propose an adaptive event-triggered model for monitoring crop status based on remote sensing data and terrestrial observations. In the proposed model, the estimation of phenological stage is a part of predicting crop status, and spatially distributed remote sensing parameters and temporal terrestrial monitoring data are used together as inputs in a state space system model. The temporal data are segmented with respect to the phenological stage-oriented timing of the spatial data, so instead of a generalized discrete state space model, we used logical states combined with analog inputs and adaptive trigger functions, as in the case of a Mealy machine model. This provides the necessary nonlinearity for the state transi- tions. The results showed that observation parameters have considerably greater significance in crop status monitoring with respect to conventional agricultural data fusion techniques.

关键词： plant phenology monitoring yield prediction finite automata Mealy machine remote sensing

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Reducing deep learning network structure through variable reduction methods in crop modeling

引用

Artificial Intelligence in Agriculture 2021年第1期5卷 196-207页

作者： Babak Saravi A.Pouyan Nejadhashemi Prakash Jha Bo Tang Department of Biosystems and Agricultural Engineering Michigan State UniversityEast LansingMI 48824USA Department of Plant Soil and Microbial Sciences Michigan State UniversityEast LansingMI 48824USA Feed the Future Innovation Lab for Collaborative Research on Sustainable Intensification Kansas State UniversityManhattanKS 66506USA Department of Electrical and Computer Engineering Mississippi State UniversityMS 39762USA

Crop models are widely used to predict plant growth,water input requirements,and ***,existing models are very complex and require hundreds of variables to perform *** to these shortcomings,large-scale applications of crop models are *** order to address these limitations,reliable crop models were developed using a deep neural network(DNN)–a new approach for predicting crop *** addition,the number of required input variables was reduced using three common variable selection techniques:namely Bayesian variable selection,Spearman's rank correlation,and Principal Component Analysis Feature *** reduced-variableDNN modelswere capable of estimating future crop yields for 10,000,000 differentweather and irrigation scenarios while maintaining comparable accuracy levels to the original model that used all input *** establish clear superiority of the methodology,the results were also compared with a very recent feature selection algorithm called min-redundancy max-relevance(mRMR).The results of this study showed that the Bayesian variable selection was the best method for achieving the aforementioned ***,the final Bayesian-based DNN model with a structure of 10 neurons in 5 layers performed very similarly(78.6%accuracy)to the original DNN cropmodel with 400 neurons in 10 layers,even though the size of the neural network was reduced by *** effort can help promote sustainable agricultural intensifications through the large-scale application of crop models.

关键词： Deep learning Artificial intelligent Variable reduction Crop modeling yield prediction Irrigation

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

yield performance estimation of corn hybrids using machine learning algorithms

引用

Artificial Intelligence in Agriculture 2021年第1期5卷 82-89页

作者： Farnaz Babaie Sarijaloo Michele Porta Bijan Taslimi Panos M.Pardalos Department of Industrial and Systems Engineering University of FloridaGainesvilleFLUSA Department of Computer Engineering University of ParmaParmaItaly

Estimation of yield performance for crop products is a topic of interest in *** breeding programs,we cannot test all possible hybrids created by crossing two parents(inbred and tester)since it would be too time consuming and *** this paper,we exploit different machine learning algorithms including decision tree,gradient boosting machine,random forest,adaptive boosting,XGBoost and neural network to predict the yield of corn hybrids using data provided in the 2020 Syngenta Crop *** participants were asked to predict the yield of missing hybrids which were not tested *** results show that the prediction obtained by XGBoost is more accurate than other models with a root mean square error equal to ***,we use XGBoost model to estimate the yield performance for untested combinations of inbreds and *** this approach,we identify hybrids with high predicted yield that can be bred to increase corn production.

关键词： yield prediction Data analysis Corn hybrids Machine learning Agricultural data

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

A reliable method for predicting bioethanol yield of different varieties of sweet potato by dry matter content

引用

Grain & Oil Science and Technology 2020年第3期3卷 110-116页

作者： Xinwei Wang Shuangqi Tian Haiwei Lou Renyong Zhao College of Food Science and Technology Henan University of TechnologyZhengzhou 450001China

In this study,we analyzed the potential of using dry matter content for determining ethanol yield of sweet potatoes as one of the raw materials for bioethanol *** tested dry matter content,total starch content,crude protein content,glucose content,fructose content,sucrose content and fermentation indicators of 29 sweet potato varieties in Henan *** analysis between main component contents of sweet potato and the fermentation indicators were carried *** results showed that there was strong linear correlation between dry matter content and bioethanol yield(R^2=0.935).In order to prove the conclusion,we also tested dry matter content and ethanol yield of another24 sweet potato *** on the dry matter content and linear correlations,we predicted the ethanol *** performed correlation analysis between the predicted values and the measured values of bioethanol yield of the 24 sweet potato varieties,and found highly significant positive correlation between the predicted values and the measured *** results confirmed the reliability of using dry matter content for bioethanol production prediction for sweet potatoes.

关键词： Sweet potato Bioethanol Dry matter content yield prediction

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：