Machine Learning Algorithms for Oilseed Disease Diagnosis
5 Pages Posted: 17 Apr 2019
Date Written: April 15, 2019
Classification is a process of observing the characteristics of a new object and consigning it to a known class. The classification problem is distinguished by known classes and a training set of containing pre-categorized samples. The work presents machine learning classification algorithms viz. simple logistic, decision tree, random forest and multilayer perceptron for exact identification of oilseed diseases. Experiments are conducted using 10-fold cross validation. The results recommend that aforementioned classification algorithms diagnose the oilseed diseases at a significant accuracy level. Simple logistic and multilayer perceptron perform better than other algorithms and yield 96.33% and 95.90% accuracy. Random forest and decision tree result in providing fair results in less time. The presented algorithms facilitated in characterization of oilseed diseases using important variables and permit enhanced interpretability.
Keywords: Machine learning, Simple logistic, Decision tree, Multilayer perceptron, Oilseed diseases
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