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Cruz Antony, J.
- Optimized Binning Technique in Decision Tree Model for Predicting The Helicoverpa armigera (Hubner) Incidence on Cotton
Abstract Views :245 |
PDF Views:114
Authors
Affiliations
1 ICAR-National Bureau of Agricultural Insect Resources, Bengaluru – 560024, Karnataka, IN
2 Department of Computer Science, Jain University, Bengaluru – 560011, Karnataka, IN
3 University of Agricultural Sciences, Agricultural Research Station, Raichur - 584102, Karnataka, IN
1 ICAR-National Bureau of Agricultural Insect Resources, Bengaluru – 560024, Karnataka, IN
2 Department of Computer Science, Jain University, Bengaluru – 560011, Karnataka, IN
3 University of Agricultural Sciences, Agricultural Research Station, Raichur - 584102, Karnataka, IN
Source
Journal of Biological Control, Vol 32, No 1 (2018), Pagination: 31-36Abstract
The data mining technique decision tree induction model is a popular method used for prediction and classification problems. The most suitable model in pest forewarning systems is decision tree analysis since pest surveillance data contains biotic, abiotic and environmental variables and IF-THEN rules can be easily framed. The abiotic factors like maximum and minimum temperature, rainfall, relative humidity, etc. are continuous numerical data and are important in climate-change studies. The decision tree model is implemented after pre-processing the data which are suitable for analysis. Data discretization is a pre-processing technique which is used to transform the continuous numerical data into categorical data resulting in interval as nominal values. The most commonly used binning methods are equal-width partitioning and equal-depth partitioning. The total number of bins created for the variable is important because either large number of bins or small number of bins affects the accuracy in results of IF-THEN rules. Hence, optimized binning technique based on Mean Integrated Squared Error (MISE) method is proposed for forming accurate IF-THEN rules in predicting the pest Helicoverpa armigera incidence on cotton crop based on decision tree analysis.Keywords
Bin Optimization, Decision Tree, Discretization, Helicoverpa armigera, If-Then Rules, Pest Prediction.Full Text
References
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- Leonardo T, Miriam EP. 2002. The distribution and movement of cotton bollworm, Helicoverpa armigera Hübner (Lepidoptera: Noctuidae) larvae on cotton. Philippine J Sci, 131: 91–98.
- Pratheepa M, Meena K, Subramaniam KR, Venugopalan R, Bheemanna H. 2011. A decision tree analysis for predicting the occurrence of the pest, Helicoverpa armigera and its natural enemies on cotton based on economic threshold level. Curr Sci. 100(2): 238–246.
- Shimazaki H, Shinomoto S. 2007. A method of selecting the binsize of a Time Histogram. Neural Comput.19(6): 1503–1527.
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- Zhao H, Ram S. 2004. Constrained cascade generalization of decision trees. IEEE Trans Knowledge Data Engineering. 16(6): 727–739. Available from: https://dl.acm.org/citation.cfm?id=1437601 https://doi.org/10.1109/TKDE.2004.3
- A Bayesian Classification Approach for Predicting Gesonia gemma Swinhoe Population on Soybean Crop in Relation to Abiotic Factors Based on Economic Threshold Level
Abstract Views :256 |
PDF Views:126
Authors
Affiliations
1 Division of Genomic Resources, ICAR-National Bureau of Agricultural Insect Resources, Bengaluru – 560024, Karnataka, IN
1 Division of Genomic Resources, ICAR-National Bureau of Agricultural Insect Resources, Bengaluru – 560024, Karnataka, IN
Source
Journal of Biological Control, Vol 32, No 1 (2018), Pagination: 68-73Abstract
Predicting of insect pest population with accuracy and speed when given large data set will make a major contribution to the success of integrated pest management. Naïve Bayesian classification has been proposed for predicting the insect pest Gesonia gemma Swinhoe on soybean crop. The Naïve Bayesian classifier works based on Bayes’ theorem and can predict class probabilities that a given tuple from the dataset belongs to a particular class. The dataset includes abiotic factors as features along with the class feature (pest incidence) are separated as training data and testing data, then the model was built on the training set by finding the probability for each of its features in relation with the class feature. The Naïve Bayesian classification from the trained model, best fits the testing data with 90% accuracy, thus the proposed approach can be very useful in predicting the pest G. gemma on soybean crop.Keywords
Abiotic, Bayesian Classification, Gesonia gemma, Naïve Population Dynamics, Soybean.Full Text
References
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