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Predicting rice blast disease: machine learning versus process-based models
2019
BMC Bioinformatics
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In this study, we compared four models for predicting rice blast disease, two operational process-based models (Yoshino and WARM) and two approaches based on machine learning algorithms (M5Rules and RNN ...
According to the authors, this is the first time process-based and machine learning modelling approaches for supporting plant disease management are compared. ...
Authors' contributions DN carried out the M5Rules method data pre-processing, analysis, modelling and interpretation, as well as the overall writing, preparation and revision of 1 IRIS Advanced Engineering ...
doi:10.1186/s12859-019-3065-1
fatcat:vffkfdgknjactik36qr4p4rjiu
Banana Plant Disease Classification Using Hybrid Convolutional Neural Network
2022
Computational Intelligence and Neuroscience
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for avoiding the disease in the initial stages, and the proposed technique shows 99% of accuracy that is compared with the related deep learning techniques. ...
Banana cultivation is one of the main agricultural elements in India, while the common problem of cultivation is that the crop has been influenced by several diseases, while the pest indications have been ...
A machine learning-based approach for detection of banana disease detection in the early stage using the SVM classifier is proposed [9] . ...
doi:10.1155/2022/9153699
pmid:35251158
pmcid:PMC8890843
fatcat:7rmtg7eomzaahj7udbw2rpl7ri
Looking Ahead in Rice Disease Research and Management
2004
Critical reviews in plant sciences
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We believe that a gene-based and a resource-based disease management approach should allow us to incorporate these new scientific developments. ...
We have to be efficient in utilizing genetic resources to develop durable resistance to rice diseases. ...
Machine Versus Manual Transplanting Japan developed a mechanical transplanting technology in the early seventies (Iwano, 2000) . ...
doi:10.1080/07352680490433231
fatcat:qrm2z7o5cjgybok4k4mzulbr74
Machine Learning-Based Presymptomatic Detection of Rice Sheath Blight Using Spectral Profiles
2020
Plant Phenomics
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Support vector machine (SVM) and random forest, two machine learning algorithms, were used to build and evaluate the accuracy of supervised classification-based disease predictive models. ...
These results suggest that machine learning models could be developed into tools to diagnose infected but asymptomatic plants based on spectral profiles at the early stages of disease development. ...
Support vector machine (SVM), a type of machine learning algorithm, is a supervised classification approach that has been used widely for detection, classification, and prediction of plant diseases [17 ...
doi:10.34133/2020/8954085
pmid:33313566
pmcid:PMC7706329
fatcat:vlvvrylthbbe3gccckteruv4py
Integrated strategies for durable rice blast resistance in sub-Saharan Africa
2021
Plant Disease
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of regionally-based blast resistance breeding programs. ...
Rice blast disease, caused by the fungus Magnaporthe oryzae, represents one of the major biotic constraints to rice production under small-scale farming systems of Africa, and developing durable disease ...
We are learning a lot through the process regarding testing of materials across different blast hotspots. ...
doi:10.1094/pdis-03-21-0593-fe
pmid:34253045
fatcat:afbz2crezvel5hith44p5tu4ni
RSLpred: an integrative system for predicting subcellular localization of rice proteins combining compositional and evolutionary information
2009
Proteomics
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The attainment of complete map-based sequence for rice (Oryza sativa) is clearly a major milestone for the research community. ...
Our proposed method, RSLpred, is an effort in this direction for genome-scale subcellular prediction of encoded rice proteins. ...
To overcome these limitations, many machine learning technique-based methods such as artificial neural networks and support vector machines (SVM) have been developed to predict the subcellular localization ...
doi:10.1002/pmic.200700597
pmid:19402042
fatcat:iud64h4fozbjzh32tim3344bqq
Machine Learning for High-Throughput Stress Phenotyping in Plants
2016
Trends in Plant Science
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However, extracting patterns and features from this large corpus of data requires the use of machine learning (ML) tools to enable data assimilation and feature identification for stress phenotyping. ...
Sophisticated data collection, storage, and processing are becoming ubiquitous, and newer areas of application are emerging constantly. One such relatively new domain is plant stress analytics. ...
SVM In predicting rice blast disease, SVM was used for the development of a weather-based prediction model [59] . ...
doi:10.1016/j.tplants.2015.10.015
pmid:26651918
fatcat:bkrddy6mxjgrfozkym2qfful3m
A Pipeline for Classifying Deleterious Coding Mutations in Agricultural Plants
2018
Frontiers in Plant Science
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Here, we developed a machine learning classifier based on a dataset of deleterious and neutral mutations in Arabidopsis thaliana by extracting 18 informative features that discriminate deleterious mutations ...
The deleterious mutations in plants have been solely predicted using sequence conservation methods rather than function-based classifiers due to lack of well-annotated mutational datasets in these organisms ...
The accuracy of our classifier based on Random Forest approach versus PolyPhen-2 was 87% versus 81% for rice and 93% versus 90% for pea. ...
doi:10.3389/fpls.2018.01734
pmid:30546376
pmcid:PMC6279870
fatcat:pkfwwrvofvbjdabsu4jozhwrzi
CYPUR-NN: Crop Yield Prediction Using Regression and Neural Networks
[article]
2020
arXiv
pre-print
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Simulations indicate that our model can predict paddy yield with high accuracy while concurrently detecting diseases that may exist and are oblivious to the human eye. ...
By incorporating regression models and neural networks (NN), one can produce highly satisfactory forecasting of paddy yield. ...
As practical usage of Artificial Intelligent (AI) based on Convolutional Neural Networks (CNN) application in a plethora of fields, shows that CNN based machine learning scheme is open to change and can ...
arXiv:2011.13265v1
fatcat:fuowqyky2zfxvgnjp47ixqnh5m
Combining Machine Learning and Homology-Based Approaches to Accurately Predict Subcellular Localization in Arabidopsis
2010
Plant Physiology
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To this end, we performed a comprehensive study in Arabidopsis and created an integrative support vector machine-based localization predictor called AtSubP (for Arabidopsis subcellular localization predictor ...
When used to predict seven subcellular compartments through a 5-fold cross-validation test, our hybrid-based best classifier achieved an overall sensitivity of 91% with high-confidence precision and Matthews ...
As a comprehensive study on the model plant Arabidopsis, we present here an integrative system, AtSubP, combining machine learning techniques and homology-based approaches to demonstrate the advantages ...
doi:10.1104/pp.110.156851
pmid:20647376
pmcid:PMC2938157
fatcat:p45dt3fkxzflto2bktbukhnhha
Application of a hierarchical enzyme classification method reveals the role of gut microbiome in human metabolism
2015
BMC Genomics
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Results: ECemble method uses an ensemble of machine-learning methods to accurately model and predict enzymes from protein sequences and also identifies the enzyme classes and subclasses at the finest resolution ...
Enzymes are known as the molecular machines that drive the metabolism of an organism; hence identification of the full enzyme complement of an organism is essential to build the metabolic blueprint of ...
An ensemble of five different machine-learning (ML) classifiers was used to build prediction models based on protein domains that include sequence or structure-derived features. ...
doi:10.1186/1471-2164-16-s7-s16
pmid:26099921
pmcid:PMC4474468
fatcat:kvfi5p3t4jartnxbetkrsyjsaq
Determining the Optimum Maturity of Maize Using Computational Intelligence Techniques
2020
American Journal of Neural Networks and Applications
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Results obtained indicated a 3.5% improvement classification accuracy of pre-trained ResNet50 over ANN model, providing a stimulus for further research on the subject area. ...
Although the challenge inherent in determining the optimum maturity of maize is by no means trivial, the practice was hitherto based on human perception, which is a function of experience over time. ...
The result shows a disease detection accuracy of 96.71% for rice blast, 97.55% for bacterial blight and 98.26% for sheath blight of rice as reported in [23] by Zhou et al. ...
doi:10.11648/j.ajnna.20200601.11
fatcat:af5cnjsx3vbs7i2mihrgswpxcy
Metabolomics: Current technologies and future trends
2006
Proteomics
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The search for specific mRNA, proteins, or metabolites that can serve as diagnostic markers has also increased, as has the fact that these biomarkers may be useful in following and predicting disease progression ...
Artificial neural networks (ANN) are very popular based machine learning methods, which in contrast to DA and PLS can learn non-linear as well as linear mappings [52] . ...
It was found that phosphatidic and phosphatidyl glycerol phospholipids were involved in rice blast disease when Brachypodium distachyon was infected by Magnaporthe grisea. ...
doi:10.1002/pmic.200600106
pmid:16888765
fatcat:p6efjmnm4zcl3elmo3e4a64k2a
Novel Insights into Rice Innate Immunity Against Bacterial and Fungal Pathogens
2014
Annual Review of Phytopathology
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Rice blast, caused by the fungal pathogen Magnaporthe oryzae, and bacterial blight, caused by the bacterial pathogen Xanthomonas oryzae pv. oryzae, are major constraints to rice production worldwide. ...
learning, psychology, sociology, and aspects of the physical sciences. ...
Where appropriate, insights into rice innate immunity learned from these pathogens are included. ...
doi:10.1146/annurev-phyto-102313-045926
pmid:24906128
fatcat:rnm7ktralzcixdmelkz57j4rpy
Identification of Orphan Genes in Unbalanced Datasets Based on Ensemble Learning
2020
Frontiers in Genetics
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The performances of the SMOTE-ENN-XGBoost model, which combined over-sampling and under-sampling algorithms with XGBoost, achieved higher predictive accuracy than the other balanced algorithms with XGBoost ...
models. ...
Computational technology and machine learning (ML) algorithms are widely used in the detection of orphan genes in big datasets. ...
doi:10.3389/fgene.2020.00820
pmid:33133122
pmcid:PMC7567012
fatcat:jke5a6vm4ba6xoqozhtk3qxp6e
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