Evaluation of Machine Learning Models for Plant Disease Classification Using Modified GLCM and Wavelet Based Statistical Features

Abstract

In this paper, different types of plant diseases in the PlantVillage dataset are getting focused for classification. In the realm of machine vision, plant disease identification is one of the most crucial tasks in the agricultural sector. It is a technique that employs equipment to capture images to detect and classify different types of diseases in plants. However, nakedeye monitoring of plants is impractical due to long processing times and a lack of specialists on farms in remote locations. Hence, combining image processing techniques with machine learning provides a solution to the problem of agricultural production while also ensuring food security. The plant features are extracted using a modified gray-level co-occurrence matrix (GLCM) technique and based on various statistical features. Both of these approaches were applied on original images with background and segmented images without background. Wavelet transform is also used with segmented images to decompose the image into sub-bands. All the features obtained are combined and SMOTE technique is used to balance the dataset prior to classification. For the purpose of classification, six machine learning models were compared, namely Light Gradient Boosting Machine (LGBM), Random Forest (RF), Decision Trees (DT), Logistic Regression (LG), AdaBoost, and Support Vector Machine (SVM). Further, different combinations of features were experimented and the experimental results prove that employing LGBM and SVM models resulted in attaining higher accuracy values i.e. 94.39% and 93.15%, respectively.

A Deep Features Extraction Model Based on the Transfer Learning Model and Vision Transformer “TLMViT” for Plant Disease Classification

“…The plant disease-based machine learning classifiers need more steps in pre-processing stage to extract the leaf features, and the model performance would be less accurate due to the limitation of machine learning. In [33], a machine learning model using LGBM mode is proposed and trained on the PlantVillage dataset, the performance metrics did not exceed 94%. Though CNN models are effective in classifying plant disease images, these models represent only the connections between neighbouring pixels and are not able to encode the orientation and position of infected parts in the leaf.…”

“…The parametric variant classification techniques are also tuned with a Genetic algorithm (GA). Tabbakh and Barpanda [33] proposed an approach of a machine-learning model where wavelet transforms, GLCM methods, and statistical features are used to extract different combinations of leaf features. Then the extracted features are utilized for training and comparing six machine-learning models, e.g., SVM, AdaBoost, etc.…”

Development of Classification Method for Determining Chicken Egg Quality Using GLCM-CNN Method

“…Compared to other studies, this research employs a GLCMbased image processing approach that has been popular and widely used in various domains, such as Recognition and Classification of Apple Leaf Diseases [22] , Plant Disease Classificationc [23], Leather Defect Detection and Classification [24], Apple Sorting [25], Potato Agricultural Product Defects [26], Tomato Leaf Diseases [27], enhancing chestnut quality [28], mango leaf variety classification [29], and Leaf Disease Detection [30].…”