Predicting crop diseases using data mining approaches: Classification

Ayub, Umair; Moqurrab, Syed Atif

doi:10.1109/icpesg.2018.8384523

Cited by 27 publications

(21 citation statements)

References 15 publications

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“…This method has been implemented in many different fields in recent years [50] including crop pest and disease prediction. Ayub [51] applied several data mining techniques such as random forest, support vector machine, neural network, k-nearest neighbors, decision tree, and Gaussian naïve bayes to predict grass grub damage and indicated that neural network and random forest performed slightly better than other classifiers. Balaban et al [52] used RF to predict the nymphal stage percentages of the sunn pest in the Middle Eastern region and showed that their models have accuracy over 99% and also offer credible confidence intervals.…”

Section: Introductionmentioning

confidence: 99%

Predicting Rice Pest Population Occurrence with Satellite-Derived Crop Phenology, Ground Meteorological Observation, and Machine Learning: A Case Study for the Central Plain of Thailand

et al. 2019

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The brown planthopper Nilaparvata lugens (BPH) is one of the most harmful insect pests in rice paddy fields, which causes considerable yield loss and consequent economic problems, particularly in the central plain of Thailand. Accurate and timely forecasting of pest population incidence would support farmers in planning effective mitigation. In this study, artificial neural network (ANN), random forest (RF) and classic linear multiple regression (MLR) analyses were applied and compared to forecast the BPH population using weather and host-plant phenology factors during the crop dry season from 2006 to 2016 in the central plain of Thailand. Data from satellite earth observation was used to monitor crop phenology factors affecting BPH population density. An ANN model with integrated ground-based meteorological variables and satellite-derived host plant variables was more accurate for short-term forecasting of the peak abundance of BPH when compared with RF and MLR, according to a reasonably validating dataset (RMSE of natural log-transformed (ln) BPH light trap catches = 1.686, 1.737, and 2.015, respectively). This finding indicates that the utilization of ground meteorological observations, satellite-derived NDVI time series, and ANN have the potential to predict BPH population density in support of integrated pest management programs. We expect the results from this study can be applied in conjunction with the satellite-based rice monitoring system developed by the Geo-Informatic and Space Technology Development Agency of Thailand (GISTDA; http://rice.gistda.or.th) to support an effective pest early warning system.

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Section: Introductionmentioning

confidence: 99%

Predicting Rice Pest Population Occurrence with Satellite-Derived Crop Phenology, Ground Meteorological Observation, and Machine Learning: A Case Study for the Central Plain of Thailand

et al. 2019

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“…Kaur and Kaur [69] employed k-means clustering and Deep Neural Network learning to detect seven orange diseases and to predict the names of diseases based on image data and weather features.…”

Section: Forecast Models Based On Distinct Types Of Data Coming From Various Heterogeneous Sourcesmentioning

confidence: 99%

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

Fenu

Malloci

2021

BDCC

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Every year, plant diseases cause a significant loss of valuable food crops around the world. The plant and crop disease management practice implemented in order to mitigate damages have changed considerably. Today, through the application of new information and communication technologies, it is possible to predict the onset or change in the severity of diseases using modern big data analysis techniques. 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 the naked eye) or at an early stage. We examine the specific approaches and methods adopted, pre-processing techniques and data used, performance metrics, and expected results, highlighting the issues encountered. The results of the study reveal that this practice is still in its infancy and that many barriers need to be overcome.

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“…Decision Trees, Random Forest, Gaussian Naïve Bayes, Support Vector Machine, Neural Networks, K-Nearest Neighbours, ensemble models of the above algorithms are used for the grass grub damage prediction. Combination of Decision Trees, Random forest and Support Vector Machine has produced better results [8]. The metrics considered are accuracy, mean accuracy, precision, recall and F-1 score.…”

Section: Related Workmentioning

confidence: 99%

Prediction of Black Sigatoka Disease in Banana Plants By Data Mining Classification Techniques using Scikit for Python

Devi S*,

Geetha

2020

IJITEE

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Agriculture has been evolving since humans started cultivating plants for food consumption. As the agriculture field evolves, the disease control measures too have evolved. Now in this modern era, disease in plants can be easily identified using computers. Data mining is the process of obtaining the useful information from the data. Before the electronic era, diseases in plants are identified just by seeing the symptoms of the plants. Similarly, we can identify the diseases in plants using data mining by supplying the disease symptoms data and classify them accordingly. The purpose of this paper is focusing on the prediction of the diseases from images of black sigatoka disease and uses the following methods: MultilayerPerceptrons, SVM,KNeighborsClassifier,K-NeighborsRegressor, Gaussian Process Regressor, Gaussian Process Classifier, GaussianNB, Decision Tree Classifier, Decision Tree Regressor, linear models such as Linear Regression, RidgeCV, Lasso, ElasticNet, Logistic RegressionCV, SGD Classifier, Perceptron and Passive Aggressive Classifier and ensemble models of the above classifiers. The results are compared, and multilayer perceptron model is seen to give better results for individual classifiers and ensemble of week classifiers gives better results when ensembled. In future, a new hybrid algorithm would be used from the above algorithms for attaining better accuracy. The scikit is a library used for classification, clustering, regression, dimensionality reduction,model selection and preprocessing. Our paper discusses various classifiers used in scikit-learn library for Python and their ensembling is done. This can be applied to all the classification tasks. Classification is done for classifying the black sigatoka disease in banana from healthy leaves.This disease is the most vulnerable one among banana plants.

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Predicting crop diseases using data mining approaches: Classification

Cited by 27 publications

References 15 publications

Predicting Rice Pest Population Occurrence with Satellite-Derived Crop Phenology, Ground Meteorological Observation, and Machine Learning: A Case Study for the Central Plain of Thailand

Predicting Rice Pest Population Occurrence with Satellite-Derived Crop Phenology, Ground Meteorological Observation, and Machine Learning: A Case Study for the Central Plain of Thailand

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

Prediction of Black Sigatoka Disease in Banana Plants By Data Mining Classification Techniques using Scikit for Python

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