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Comparison of Methods for Forecasting Yellow Rust in Winter Wheat at Regional Scale [chapter]

Chenwei Nie, Lin Yuan, Xiaodong Yang, Liguang Wei, Guijun Yang, Jingcheng Zhang
2015 IFIP Advances in Information and Communication Technology  
Yellow rust (YR) is one of the most destructive diseases of wheat. To prevent the prevalence of the disease more effectively, it is important to forecast it at an early stage.  ...  However, given the YR usually occurs in a vast area, it is necessary to develop a large-scale disease forecasting model for prevention.  ...  Results and Discussion Conclusions A total of four methods including BNT, BP, SVM and FLDA were examined and compared in developing a forecasting model of yellow rust disease across vast area in this  ... 
doi:10.1007/978-3-319-19620-6_50 fatcat:63f7canllba5xhvc6ozccgokpe

Predict Growth Stages of Wheat Crop using Digital Image Processing

2020 International journal of recent technology and engineering  
For finding the growth stages of wheat digital image processing technique is used. RGB model, HSI model, mean value of green colour, hue and saturation images use for examining wheat crop.  ...  This study provide a solution to finding the wheat crop growth stages, Once the growing stages are established, farmers can take suitable and measured steps to improve the production of wheat or other  ...  [21] developed a system to identify yellow rust infected and healthy winter wheat under field circumstances. And artificial neural network is used for detecting yellow.  ... 
doi:10.35940/ijrte.e6201.018520 fatcat:k5jztcxxlbbq3i2futlo2bvn34

Sino–EU Earth Observation Data to Support the Monitoring and Management of Agricultural Resources

Stefano Pignatti, Raffaele Casa, Giovanni Laneve, Zhenhai Li, Linyi Liu, Pablo Marzialetti, Nada Mzid, Simone Pascucci, Paolo Cosmo Silvestro, Massimo Tolomio, Deepak Upreti, Hao Yang (+2 others)
2021 Remote Sensing  
A k coefficient higher than 0.84 was achieved for yellow rust, thus assuring a monitoring accuracy, and for the diseased samples k was higher than 0.87.  ...  with a higher accuracy with respect to open loop model runs: 1.14 t·ha−1 vs 4.42 t·ha−1 RMSE for assimilation and open loop, respectively.  ...  develop a model for wheat yellow rust monitoring.  ... 
doi:10.3390/rs13152889 fatcat:vgdeldhvmnhv3gzjqhlhlc2amu

Early Visual Detection of Wheat Stripe Rust Using Visible/Near-Infrared Hyperspectral Imaging

Zhifeng Yao, Yu Lei, Dongjian He
2019 Sensors  
provides a new method for the early detection of wheat stripe rust.  ...  In order to detect wheat stripe rust at an early stage, a visual detection method based on hyperspectral imaging is proposed in this paper.  ...  Meanwhile, over-fitting was avoided by using a Bayesian regularization training algorithm. The network was trained for at least 20,000 epochs.  ... 
doi:10.3390/s19040952 fatcat:jsdyy5kdqfh4ln6qjybyissb54

Application of artificial neural network for wheat yield forecasting

Gailya Aubakirova, Victor Ivel, Yuliya Gerassimova, Sayat Moldakhmetov, Pavel Petrov
2022 Eastern-European Journal of Enterprise Technologies  
A given model of yield forecasting using an artificial neural network connects the wheat crop with the amount of productive moisture in the soil, soil fertility, weather, and factors in the presence of  ...  Thus, it is assumed that the developed model for forecasting wheat yields based on data can be easily adapted for other crops and places and will allow the adoption of the right strategies to ensure food  ...  To build a model for forecasting yields, predictors were selected that have the greatest impact on wheat yields.  ... 
doi:10.15587/1729-4061.2022.259653 doaj:d2c5b891fabf4b7097237bfa7404de3a fatcat:2t3smj7ltreojprf75h27at7nu

Genetic Characterization of Puccinia striiformis f. sp. tritici Populations from Different Wheat Cultivars Using Simple Sequence Repeats

Shuhe Wang, Chaofan Gao, Qiuyu Sun, Qi Liu, Cuicui Wang, Fangfang Guo, Zhanhong Ma
2022 Journal of Fungi  
The results could be helpful in designing more effective management strategies for stripe rust in wheat production.  ...  The unweighted pair group method with arithmetic mean (UPGMA) phylogenetic tree, Bayesian clustering analysis, and minimum spanning network for the MLGs revealed two major genetic clusters based on geographical  ...  The evaluation of wheat cultivars for resistance to Pst followed the Rules for Resistance Evaluation of Wheat to Diseases and Insect Pests-Part 1: Rule for Resistance Evaluation of Wheat to Yellow Rust  ... 
doi:10.3390/jof8070705 pmid:35887461 pmcid:PMC9319641 fatcat:w2uf3n4uqjc2lhzxe5ocg5akbm

Forecasting Plant and Crop Disease: An Explorative Study on Current Algorithms

Gianni Fenu, Francesca Maridina Malloci
2021 Big Data and Cognitive Computing  
In this paper, we present an analysis and classification of research studies conducted over the past decade that forecast the onset of disease at a pre-symptomatic stage (i.e., symptoms not visible to  ...  Every year, plant diseases cause a significant loss of valuable food crops around the world.  ...  [73] employed a Bayesian network model with four meteorological variables and one phonological parameter to forecast yellow rust of winter wheat at a regional scale.  ... 
doi:10.3390/bdcc5010002 fatcat:7kxdjeutxrhutd6sbvpoijxcya

A survey on plant disease prediction using machine learning and deep learning techniques

UshaDevi G, Gokulnath BV
2020 Inteligencia Artificial  
Machine learning techniques like Random Forest, Bayesian Network, Decision Tree, Support Vector Machine etc. help in automatic detection of plant disease from visual symptoms in the plant.  ...  The major agricultural products in India are rice, wheat, pulses, and spices. As our population is increasing rapidly the demand for agriculture products also increasing alarmingly.  ...  Acknowledgements This is the place for acknowledgements. Referencias  ... 
doi:10.4114/intartif.vol23iss65pp136-154 fatcat:lc3qvowvbjhvvp5o5k32bqoxka

Machine Learning in Agriculture: A Comprehensive Updated Review

Lefteris Benos, Aristotelis C. Tagarakis, Georgios Dolias, Remigio Berruto, Dimitrios Kateris, Dionysis Bochtis
2021 Sensors  
In addition, maize and wheat as well as cattle and sheep were the most investigated crops and animals, respectively.  ...  A plethora of machine learning algorithms were used, with those belonging to Artificial Neural Networks being more efficient.  ...  tea leaves SVM, DT, RF, CNN CNN: Acc values: (1) tea red Scab: 0.7; (2) tea leaf blight: 1.0; (3)tea red leaf spot: 1.0 [190] Wheat Hyperspectral images from UAV Detection of yellow rust in wheat plots  ... 
doi:10.3390/s21113758 pmid:34071553 fatcat:moehdvs6efdpxpklidutmw2ary

Snow Coverage Mapping by Learning from Sentinel-2 Satellite Multispectral Images via Machine Learning Algorithms

Yucheng Wang, Jinya Su, Xiaojun Zhai, Fanlin Meng, Cunjia Liu
2022 Remote Sensing  
Snow coverage mapping plays a vital role not only in studying hydrology and climatology, but also in investigating crop disease overwintering for smart agriculture management.  ...  It is concluded that a U-Net-based deep-learning classifier with four informative spectral bands is suitable for snow coverage mapping.  ...  U-Net Training U-Net is a convolutional network architecture for fast and precise segmentation of images [34] , which has been applied for yellow rust disease mapping in [5, 32] .  ... 
doi:10.3390/rs14030782 doaj:4ba44bce6af541ba802718a5f4e5c17c fatcat:ngcwsfwh7feh7frg3wu2bhkkj4

Abstracts of Special Session Presentations at the 2000 APS Annual Meeting

2000 Phytopathology  
Probabilistic models for spring and winter wheat were deployed for 23 states via an internet delivery system.  ...  The Lr34/Yr18 and Lr46/Yr29 genes of wheat are thought to be race nonspecific because they provide adult-plant slow-rusting resistance to both leaf rust and yellow (stripe) rust.  ... 
doi:10.1094/phyto.2000.90.6.s91 pmid:18944404 fatcat:tnypmhcptffodflusrtyekokge

The Nexus Between Plant and Plant Microbiome: Revelation of the Networking Strategies

Olubukola Oluranti Babalola, Ayomide E. Fadiji, Ben J. Enagbonma, Elizabeth T. Alori, Modupe S. Ayilara, Ayansina S. Ayangbenro
2020 Frontiers in Microbiology  
The network is altered by the host plant species, which in turn influence the microbial interaction dynamics and co-evolution.  ...  These microbiomes are structured and form complex interconnected microbial networks that are important in plant health and ecosystem functioning.  ...  Bayesian network models are smart for their capacity to interpret complex stochastic processes (like networks among genes based on multiple expression measurements) and because they offer a clear method  ... 
doi:10.3389/fmicb.2020.548037 pmid:33013781 pmcid:PMC7499240 fatcat:jcnjlubbjvcibpjcya674b22ly

Sensing technologies for precision specialty crop production

W.S. Lee, V. Alchanatis, C. Yang, M. Hirafuji, D. Moshou, C. Li
2010 Computers and Electronics in Agriculture  
This paper presents a review of these sensing technologies and discusses how they are used for precision agriculture and crop management, especially for specialty crops.  ...  With the advances in electronic and information technologies, various sensing systems have been developed for specialty crop production around the world.  ...  rust of the sugar-bearing crops of beet and cane, mold and dwarf-mosaic virus in corn, virus yellows in sugar beet, barley yellow dwarf virus in winter wheat, barley yellow mosaic virus, take-all in winter  ... 
doi:10.1016/j.compag.2010.08.005 fatcat:tesilbgowvf3vplcwgbcevmhyy

Selection of Rye (Secale cereale L.) for Powdery Mildew and Leaf Rust Resistance Through Phenotyping, Target Sequencing, and Association Genetics

N.M. Vendelbo, Mogens Hovmøller, Annemarie Fejer Justesen, Ahmed Jahoor, Jihad Orabi
2021 Zenodo  
(Slagelse, Denmark) for assistance in the multiplication of rust samples, Johannes Hiller, Anette Deterding and Marlene Walbrodt at Nordic Seed Germany GmbH (Nienstädt, Germany) for seed multiplication  ...  and multiplication procedures of cereal Acknowledgements We thank Hanne Svenstrup at Nordic Seed A/S for her contribution to the genotypic data collection, Ellen Jørgensen at the Global Rust Reference  ...  Methodology for scoring of Prs SPI IT was adapted from Hovmøller., et al. (2017) about race typing of yellow rust in wheat.  ... 
doi:10.5281/zenodo.5820211 fatcat:mg5v7skd5jggpe7bhephu2c2nq

APS Abstracts Submitted for Presentation at the 2006 APS Annual Meeting

2006 Phytopathology  
A total RNA isolation method was developed for consistent RT-PCR detection of the virus directly from sweetpotato leaves, avoiding dsRNA extraction. HSP70 amplicons were cloned and sequenced.  ...  Sweetpotato feathery mottle virus (SPFMV), the other component of SPVD, was also detected in both cultivars. This is the first report of SPCSV in the USA.  ...  Wheat leaf rust, caused by Puccinia triticina, is a major disease of wheat in Central Asia.  ... 
doi:10.1094/phyto.2006.96.6.s1 pmid:18943394 fatcat:c7odyihjrne6tfkpxzgqisab5i
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