Analysis of Data Mining Techniques for Agricultural Science

Khan, Er. Saiqa; Saemeen, Khan; Parveen, Darakshan; Jannat, Patel

doi:10.1109/icscet.2018.8537322

Cited by 3 publications

(1 citation statement)

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“…predicts cotton crop diseases using decision based on previous data. Umair Ayub et.al [5] stated analysis of different data mining classifiers on different feature sets to predict the grass grub damages .The classifiers used are Random Tree, Random forest ,Decision Tree ,Support vector machine ,Neural Network Naïve Bayes and K-Nearest Neighbors combination of Decision Tree ,Random Forest and Support Vector Machine has proven as best combination out of all testes combinations and specified deep learning and hybrid approaches can solve the crops related problem.The analysis [6] were made on crop disease by applying random forest model and decision Tree Model ,the best result is by applying the random forest model. The proposed methodology in this paper [7] depends on CNN and neural network techniques which are configured for leaf disease detection.…”

Section: Related Workmentioning

confidence: 99%

Data Mining Techniques for Identification and Classification of Various Diseases in Plants

2019

IJITEE

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Data mining is currently being used in various applications; In research community it plays a vital role. This paper specify about data mining techniques for the preprocessing and classification of various disease in plants. Since various plants has different diseases based on that each of them has different data sets and different objectives for knowledge discovery. Data Mining Techniques applied on plants that it helps in segmentation and classification of diseased plants, it avoids Oral Inspection and helps to increase in crop productivity. This paper provides various classification techniques Such as K-Nearest Neighbors, Support Vector Machine, Principle component Analysis, Neural Network. Thus among various techniques neural network is effective for disease detection in plants.

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Section: Related Workmentioning

confidence: 99%