Identification of Corn Plant Diseases and Pests Based on Digital Images using Multinomial Naïve Bayes and K-Nearest Neighbor

Abstract

Statistical machine learning has developed into integral components of contemporary scientific methodology. This integration provides automated procedures for predicting phenomena, case diagnosis, or object identification based on previous observations, uncovering patterns underlying data, and providing insights into the problem. Identification of corn plant diseases and pests using it has become popular recently. Corn (Zea mays L) is one of the essential carbohydrate-producing foodstuffs besides wheat and rice. Corn plants are sensitive to pests and diseases, resulting in a decrease in the quantity and quality of the production. Eradicate pests and diseases according to their type is a solution to overcome the problem of disease in corn plants. This research aims to identify corn plant diseases and pests based on the digital image using the Multinomial Naïve Bayes and K-Nearest Neighbor methods. The data used consisted of 761 digital images with six classes of corn plants disease and pest. The investigation shows that the K-Nearest Neighbor method has a better predictive performance than the Multinomial Naïve Bayes (MNB) method. The MNB method with two categories has an accuracy level of 92.72%, a precision level of 79.88%, a recall level of 79.24%, F1-score 78.17%, kappa 72.44%, and AUC 71.91%. Simultaneously, the K-Nearest Neighbor approach with k=3 has an accuracy of 99.54 %, a precision of 88.57%, recall 94.38%, F1-score 93.59%, kappa 94.30%, and AUC 95.45%.

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

“…Suppose X d • be the d-th predictor variable which represents the color pixel values of numeric type. Variable X d is variable X d o which is discretized as much as k(X d ) by (Resti et al, 2022)…”

“…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. Detection of food plant diseases using digital images by applying classi cation tasks to statistical machine learning algorithms has become popular in recent years (Resti et al, 2022;Xian and Ngadiran, 2021;Ngugi et al, 2021;Syarief and Setiawan, 2020;Rajesh et al, 2020;Kasinathan et al, 2021;Kusumo et al, 2018). This trend occurs because detection uses low-cost digital images (Ngugi et al, 2021) .…”

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

“…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].…”

“…In terms of detecting the diseases and pests of corn crops, digital image processing using the RGB color space model is the most informative compared to other features [16]. In addition, it provides satisfactory performance [9,14]. However, discretizing RGB features into several classes is a subjectivity that tends to be vagueness [14].…”

“…In addition, it provides satisfactory performance [9,14]. However, discretizing RGB features into several classes is a subjectivity that tends to be vagueness [14].…”