Classification of Diseases and Pests of Maize Using Multinomial Logistic Regression Based on Resampling Technique of K-Fold Cross-Validation

Abstract

Some of the obstacles in the cultivation of maize that cause low productivity of maize yields are diseases and pests. Early detection of maize diseases and pests is expected to reduce farmer losses. A system for the early detection of diseases and pests can be created by classifying them based on digital images. This study aimed to classify maize diseases and pests using multinomial logistic regression. The model and testing resampling were based on resampling technique of k-fold cross-validation. The research data was obtained from the RGB color feature extraction process for each object in each class of diseases and pests of corn. The results showed that the classification into seven classes using five folds had an accuracy rate of 99.85%, macro precision of 98.59%, and macro recall of 98.15%.

Fuzzy Discretization on the Multinomial Naïve Bayes Method for Modeling Multiclass Classification of Corn Plant Diseases and Pests

“…Prior knowledge about a variable's characteristics can help form linguistic terms and become a crisp and fuzzy discretization reference. Furthermore, a comparison of the results in this work with other work that classifies two classes or multiclass of disease [15][16][17][18][19], and pests in corn plants [9,14,50], is presented in Table 8. The classification of the two classes consists of a healthy class (non-pathogen) and a class infected with disease [15].…”