A Perspective View of Cotton Leaf Image Classification Using Machine Learning Algorithms Using WEKA

Abstract

Cotton is one of the major crops in India, where 23% of cotton gets exported to other countries. The cotton yield depends on crop growth, and it gets affected by diseases. In this paper, cotton disease classification is performed using different machine learning algorithms. For this research, the cotton leaf image database was used to segment the images from the natural background using modified factorization-based active contour method. First, the color and texture features are extracted from segmented images. Later, it has to be fed to the machine learning algorithms such as multilayer perceptron, support vector machine, Naïve Bayes, Random Forest, AdaBoost, and K-nearest neighbor. Four color features and eight texture features were extracted, and experimentation was done using three cases: (1) only color features, (2) only texture features, and (3) both color and texture features. The performance of classifiers was better when color features are extracted compared to texture feature extraction. The color features are enough to classify the healthy and unhealthy cotton leaf images. The performance of the classifiers was evaluated using performance parameters such as precision, recall, F-measure, and Matthews correlation coefficient. The accuracies of classifiers such as support vector machine, Naïve Bayes, Random Forest, AdaBoost, and K-nearest neighbor are 93.38%, 90.91%, 95.86%, 92.56%, and 94.21%, respectively, whereas that of the multilayer perceptron classifier is 96.69%.

Machine Learning in Agriculture for Crop Diseases Identification: A Survey

“…This paper also included basic ideas about transfer learning with some research questions Abade et al (2021). Patil and Burkpalli. (2021), this paper presents image classification for cotton leaves using machine learning algorithms.…”

“…In this paper, color features show the classification of healthy and diseased, cotton leaf images with an accuracy of nearly about 96.69% which is more than as com-pared to another classifier. Images from real fields and a database of 3000 images with 2 groups of healthy and diseases were used to classify cotton leaf diseases Patil and Burkpalli (2021). Raghavendra et al (2021a), this paper discussed multiple disease classifications and detection for different plant leaves using a support vector machine.…”

Comparison of Machine Learning Performance Using Naive Bayes and Random Forest Methods to Classify Batik Fabric Patterns

“…A probabilistic learning model based on the Bayes theorem, NB is one of the machine learning techniques used to predict the phenomenon of classifying several different classes [22], [25][26][27]. Equation for NB classifier shown in Eq.…”

Detection and Classification of Banana Leaf Disease Using Novel Segmentation and Ensemble Machine Learning Approach

“…The experiments were carried out using SVM, multilayer perceptron, Random Forest, Naïve Bayes, KNN and AdaBoost. The work concluded that only colour features were sufficient to improve the accuracy of classification [19].…”