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

Abstract

As an agricultural commodity, corn functions as food, animal feed, and industrial raw material. Therefore, diseases and pests pose a major challenge to the production of corn plants. Modeling the classification of corn plant diseases and pests based on digital images is essential for developing an information technology-based early detection system. This plant’s early detection technology is beneficial for lowering farmers’ losses. The detection system based on digital images is also cost-effective. This paper aims to model the classification of corn plant diseases and pests based on digital images by implementing fuzzy discretization. Discretization is an essential technique to improve the knowledge extraction process of continuous-type data. It is also essential in some methods where continuous data must be processed or handled. Fuzzy discretization allows classes to have overlapping intervals so that they can handle information that is vague or unclear. We developed hypotheses and proved that different combinations of membership functions in fuzzy discretization affect classification performance. Empirical assessment using Monte Carlo resampling was carried out to obtain the generalizability of the performance of the best classification model of all proposed models. The best model is determined based on the number of metrics with the highest value and the highest metric on the Fscore and Kappa, a multiclass measure. The combination of digital image data preprocessing and classification methods also affects the performance of the classification model. We hope this work can provide an overview for experts in building early detection systems of corn plant diseases and pests using classification models based on fuzzy discretization.

An analysis to investigate plant disease identification based on machine learning techniques

“…In the first step, a fuzzy discretization approach is used to transform continuous data into discrete values, which are then used as input for the MNB classifier. This approach is designed to address the limitations of traditional discretization methods, which can be sensitive to the choice of thresholds and can lead to information loss (Resti et al, 2023). Verma et al propose a meta‐learning framework for CNN architecture recommendation in plant disease identification.…”

An Innovative Decision Model Utilizing Intuitionistic Hesitant Fuzzy Aczel-Alsina Aggregation Operators and Its Application

“…Another approach to handling vagueness was established by Torra [10] in the form of hesitant fuzzy sets (HFSs), which expand upon the theory of FSs by allowing membership grades to hold a range of possible values within the interval of 0-1. The concept of HFSs has been widely applied across various complexities, with numerous researchers critically examining data aggregation procedures and their impact on decision-making [11][12][13][14].…”