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Crop Pests Prediction Method Using Regression and Machine Learning Technology: Survey
2014
Information Engineering Research Institute procedia
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This paper classifies and introduces SVM (Support Vector Machine), Multiple Linear Regression, Neural Network, and Bayesian Network based techniques, and describes some cases of their utilization. ...
This paper describes current trends in the prediction of crop pests using machine learning technology. With the advent of data mining, the field of agriculture is also focused on it. ...
Acknowledgement This work was supported by the IT R&D program of MSIP/KEIT [10044889, A development of the webbased system for the predication, surveillance and non-proliferation against blight disease ...
doi:10.1016/j.ieri.2014.03.009
fatcat:5dtatlqxmjgtjomsxkrm5pw3fu
Development of a Web-Based Prediction System for Wheat Stripe Rust
[chapter]
2013
IFIP Advances in Information and Communication Technology
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A web-based prediction system for wheat stripe rust was developed based on B/S (Browser/Server) mode in this study. ...
The web-based prediction system for wheat stripe rust developed in this study provided a convenient and fast way for the prediction of wheat stripe rust. ...
Development Platform of This System The web-based prediction system for wheat stripe rust was developed based on the .NET platform. ...
doi:10.1007/978-3-642-36124-1_39
fatcat:4lipor7mkna73psnbksrdjcegi
Prediction of Wheat Stripe Rust Occurrence with Time Series Sentinel-2 Images
2021
Agriculture
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Finally, the occurrence of wheat stripe rust in different time-periods is predicted using the support vector machine (SVM) method. ...
Wheat stripe rust has a severe impact on wheat yield and quality. An effective prediction method is necessary for food security. ...
Predicting and mapping of wheat stripe rust in Ningqiang County on (a) March 13; (b) March 28; (c) April 02; (d) April 22; and (e) April 27, based on the support vector machine (SVM) method using the optimal ...
doi:10.3390/agriculture11111079
fatcat:vrzqnn5665hdpduvuhwnaobi7i
Combining Random Forest and XGBoost Methods in Detecting Early and Mid-Term Winter Wheat Stripe Rust Using Canopy Level Hyperspectral Measurements
2022
Agriculture
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Then, the disease severity detection model of early and mid-term winter wheat stripe rust was constructed using XGBoost method based on the optimal vegetation index combination. ...
For the evaluation and comparison of the initial results, three commonly used classification methods, namely, RF, backpropagation neural network (BPNN), and support vector machine (SVM), were utilized. ...
The support vector machine based on the application of inner product kernel function has strong robustness for small sample data, and this method is now widely applied to crop disease detecting and prediction ...
doi:10.3390/agriculture12010074
fatcat:vpqdmuddbrgqvimverk2xf2lfa
Integrating Remote Sensing and Meteorological Data to Predict Wheat Stripe Rust
2022
Remote Sensing
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This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY ...
the random forest (RF) and support vector machine (SVM). ...
For example, Ruan et al. used a sequence forward selection algorithm to select vegetation indices sensitive to stripe rust from Sentinel-2 remote sensing images, combined with the support vector machine ...
doi:10.3390/rs14051221
dblp:journals/remotesensing/RuanDHHYMGS22
fatcat:57bhq2lsazgorjl6nryw4vxejy
Research on the Method of Identifying the Severity of Wheat Stripe Rust Based on Machine Vision
2023
Agriculture
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This method first employs SLIC to segment subregions of wheat stripe rust, automatically constructs and augments a dataset of wheat stripe rust samples based on the segmented patches. ...
of wheat stripe rust. ...
Based on the wheat stripe rust severity grading standard in Table 1 , the severity of wheat stripe rust can be determined. ...
doi:10.3390/agriculture13122187
fatcat:gco5qstpyjdn5ajjn4bvys5wti
Prediction of Wheat Stripe Rust Based on Neural Networks
[chapter]
2012
IFIP Advances in Information and Communication Technology
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New methods based on neural networks were provided for the prediction of wheat stripe rust. ...
In order to figure out suitable prediction methods based on neural networks that could provide accurate prediction information with high stability, the predictions of wheat stripe rust by using backpropagation ...
Support vector machine (SVM), a new kind of machine learning method proposed by Vapnik [19] , could solve these problems with many advantages. ...
doi:10.1007/978-3-642-27278-3_52
fatcat:gxnodt4jnncxxbgazrfko3yyfi
Classification and Regression Models for Genomic Selection of Skewed Phenotypes: A Case for Disease Resistance in Winter Wheat (Triticum aestivum L.)
2022
Frontiers in Genetics
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Furthermore, a classification system based on support vector machine and ordinal Bayesian models with a 2-Class scale for SEV reached the highest class accuracy of 0.99. ...
The goal of this research was to compare classification and regression genomic selection models for skewed phenotypes using stripe rust SEV and IT in winter wheat. ...
ACKNOWLEDGMENTS The authors would like to acknowledge the Washington State University Winter Wheat Breeding Program personnel Gary Shelton and Kyall Hagemeyer for plot maintenance and data collection under ...
doi:10.3389/fgene.2022.835781
pmid:35281841
pmcid:PMC8904966
fatcat:v53i2kkt3bajbnevltchrif52m
Monitoring Wheat Stripe Rust Using Remote Sensing Technologies in China
[chapter]
2012
IFIP Advances in Information and Communication Technology
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Progress on remote sensing monitoring of wheat stripe rust in China was summarized from four aspects including remote sensing monitoring stripe rust of single wheat leaves and monitoring this disease using ...
wheat stripe rust were prospected. ...
This work was supported by National Natural Science Foundation of China (Grant No.: 30671341, 31071642). ...
doi:10.1007/978-3-642-27275-2_18
fatcat:7q5ob4wp7fddtauk2pv4joelzy
Plant Leaf Disease Detection Using Support Vector Machine
2019
Mustansiriyah Journal of Science
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extract a set of color and texture feature and used to great the knowledge base that used as training data for support vector machine classifier . ...
of these diseases requires people to expert in addition to a set of equipment and it is expensive in terms of time and money Therefore, the development of a computer based system that detection the diseases ...
Name of crop
Type of Case
Number of
Samples
Wheat
powdery mildew
70
Wheat
orange rust
79
Wheat
stripe rust
70
Wheat
healthy
24
Tomato
septoria leaf spot
71
Tomato
late blight
73
Tomato ...
doi:10.23851/mjs.v30i1.487
fatcat:yoiy2mlwcvaivn4skjgtszaeka
Identification and Severity Determination of Wheat Stripe Rust and Wheat Leaf Rust Based on Hyperspectral Data Acquired Using a Black-Paper-Based Measuring Method
2016
PLoS ONE
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Hyperspectral data of healthy leaves, leaves in incubation period and leaves in diseased period of wheat stripe rust and wheat leaf rust were collected under in-field conditions using a black-paper-based ...
stripe rust and the disease severity inversion models of leaf rust were built using quantitative partial least squares (QPLS) and support vector regression (SVR). ...
[18] applied support vector machine (SVM) to classify and identify the severity of wheat leaves, and high identification accuracy was obtained. Zhao et al. ...
doi:10.1371/journal.pone.0154648
pmid:27128464
pmcid:PMC4851363
fatcat:ktwzww6y2re7tkl4rrdil5v664
DetectionandClassificationof GrainCropsandLegumesDisease:ASurvey
2021
Sparklinglight Transactions on Artificial Intelligence and Quantum Computing
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This article presents the extensive literature on existing methodologies utilized for recognition and classification of leaves diseases. ...
The grain crops rice, wheat, maize, and legumes are suffering a lot due to some viral, bacterial, and fungal diseases. The pest and variety of diseases can bring a heavy loss to the global economy. ...
SVM-based Local 95.16% striiformis and (MCS) leaf blight [84] 2010 Powdery mildew Wheat sharp eyespot Wheat stripe PCA based Local 96.7% rust 93.3% 86.7% [85] 2014 Stripe rust, leaf rust and powdery mildew ...
doi:10.55011/staiqc.2021.1105
fatcat:icdaqmawhffcnmv5dyy2uqzpmm
Effects of UV-B Radiation on Near-Infrared Spectroscopy and Identification ofPuccinia striiformisf. sp.tritici
2014
Journal of Spectroscopy
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, the effects of UV-B radiation on near-infrared spectroscopy of the pathogen were investigated in spectral region 4000–10000 cm−1, and support vector machine models were built to identify UV-B radiation ...
Based on near-infrared spectra of three physiological races ofPuccinia striiformisf. sp.tritici(i.e., CYR31, CYR32, and CYR33) irradiated under four UV-B intensities (i.e., 0, 150, 200, and 250 μw/cm2) ...
support vector machine (LS-SVM). ...
doi:10.1155/2014/751458
fatcat:dxdjqbwk2jh4tp7q2jbyplchy4
Genome-Wide Association and Genomic Prediction for Stripe Rust Resistance in Synthetic-Derived Wheats
2022
Frontiers in Plant Science
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A meta-analysis based on a large number of existing GWAS would enhance the identification of new genes and loci for stripe rust resistance in wheat. ...
Stripe rust caused by Puccnina striiformis (Pst) is an economically important disease attacking wheat all over the world. ...
., relevance vector machines (RVM) and Gaussian Processes (GP), were used to build a GS prediction model. ...
doi:10.3389/fpls.2022.788593
pmid:35283883
pmcid:PMC8908430
fatcat:7n47dn4d4ffrhmzt7ro35744eq
Semantic Segmentation of Wheat Stripe Rust Images Using Deep Learning
2022
Agronomy
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To address the above issues, this study undertook a task of semantic segmentation of wheat stripe rust damage images using deep learning. ...
To address the problem of small available datasets, the first large-scale open dataset of wheat stripe rust images from Qinghai province was constructed through field and greenhouse image acquisition, ...
The experimental results showed that the proposed algorithm was superior compared to the traditional support vector machine, with the recognition accuracy reaching 94.16%. Su et al. ...
doi:10.3390/agronomy12122933
fatcat:biup7c4kevdqblv7xvk6jjj5lm
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