Performance Improvement of Decision Tree Model using Fuzzy Membership Function for Classification of Corn Plant Diseases and Pests

Abstract

Corn is an essential agricultural commodity since it is used in animal feed, biofuel, industrial processing, and the manufacture of non-food industrial commodities such as starch, acid, and alcohol. Early detection of diseases and pests of corn aims to reduce the possibility of crop failure and maintain the quality and quantity of crop yields. A decision tree is a nonparametric classification model in statistical machine learning that predicts target variables using tree-structured decisions. The performance of this model can increase significantly if the continuous predictor variables are discretized into valid categories. However, in some cases, the result does not provide satisfactory performance. The possible cause is the ambiguity in discretizing predictor variables. The incorporation of fuzzy membership functions into the model to resolve discretization ambiguity issues. This work aims to classify diseases and pests of corn plants using the decision tree model and improve the model’s performance by implementing fuzzy membership functions. The main contribution of this work is that we have shown a significant improvement in the decision tree model performance by implementing fuzzy membership functions; S-growth, triangle, and S-shrinkage curves. The proposed fuzzy model is better than the decision tree model, with an average performance increase from the largest to the smallest; kappa (12.16%), recall (11.8%), F-score (9.71%), precision (5.08%), accuracy (3.23%), specificity (1.94%), and AUC (0.49%). The combination of bias and variance generated by the proposed model is quite small, indicating that the model is able to capture data trends well.

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

“…As a result, the number of antecedents in the classification rules is few. Implementing fuzzy discretization in several classification methods has increased the classification method's performance [4,5,8,9], including naïve Bayes [10][11][12].…”

“…The RGB represents the red, green, and blue color space model. This technique can also improve object classification performance, [9,11]. However, no less important is the combination of the fuzzy membership functions used in the discretization process [9,11,13].…”

“…This technique can also improve object classification performance, [9,11]. However, no less important is the combination of the fuzzy membership functions used in the discretization process [9,11,13]. The combination related to the number of linguistic terms is determined based on prior knowledge or experience and the used fuzzy membership functions.…”

“…There is no exact method for selecting fuzzy membership functions. Experimenting or trial and error is the best way to achieve the best classification performance [3,6,9,13].…”

“…The early diagnosis of corn diseases and pests aims to reduce the likelihood of crop failure and preserve the quality and quantity of crop yields. The use of digital images as a dataset for identifying corn plant diseases and pests is increasing rapidly [9,[14][15][16][17][18][19], as well as in other food crops [20][21][22][23][24][25][26]. This increase is because the cost is cheaper than other technologies, such as infrared light [21].…”