Segmentation and Extraction of Individual Leaves from Plant Images for Species Classification

Plant species classification through the examination of images of plant leaves requires as input an image of a single leaf with no stems or other non-leaf objects. Images of plants, however, usually include more than one leaf, stems, branches, flowers, and other non-leaf objects. For such images each individual leaf needs to be extracted into a unique sub-image, and these sub-images must be cleaned to remove all non-leaf objects. A target leaf could then be selected from the group of sub-images to be provided as the input to the plant species classification program. As a part of the research on this thesis, an algorithm was developed to automate the tasks of detecting and extracting leaf sub-images from plant images and to clean the leaf sub-images by removing all non-leaf objects. To implement the algorithm, software was developed in Java. The proposed algorithm produced at least one perfect leaf result in 18 of the 21 (86%) plant images used in this research, while the remaining three (14%) plant images produced acceptable leaves

Feature Extraction using Histogram of Oriented Gradients for Image Classification in Maize Leaf Diseases

The paper presents feature extraction methods and classification algorithms used to classify maize leaf disease images. From maize disease images, features are extracted and passed to the machine learning classification algorithm to identify the possible disease based on the features detected using the feature extraction method. The maize disease images used include images of common rust, leaf spot, and northern leaf blight and healthy images. An evaluation was done for the feature extraction method to see which feature extraction method performs best with image classification algorithms. Based on the evaluation, the outcomes revealed Histogram of Oriented Gradients performed best with classifiers compared to KAZE and Oriented FAST and rotated BRIEF. The random forest classifier emerged the best in terms of image classification, based on four performance metrics which are accuracy, precision, recall, and F1-score. The experimental outcome indicated that the random forest had 0.74 accuracy, 0.77 precision, 0.77 recall, and 0.75 F1-score

Feature Extraction using Histogram of Oriented Gradients for Image Classification in Maize Leaf Diseases

The paper presents feature extraction methods and classification algorithms used to classify maize leaf disease images. From maize disease images, features are extracted and passed to the machine learning classification algorithm to identify the possible disease based on the features detected using the feature extraction method. The maize disease images used include images of common rust, leaf spot, and northern leaf blight and healthy images. An evaluation was done for the feature extraction method to see which feature extraction method performs best with image classification algorithms. Based on the evaluation, the outcomes revealed Histogram of Oriented Gradients performed best with classifiers compared to KAZE and Oriented FAST and rotated BRIEF. The random forest classifier emerged the best in terms of image classification, based on four performance metrics which are accuracy, precision, recall, and F1-score. The experimental outcome indicated that the random forest had 0.74 accuracy, 0.77 precision, 0.77 recall, and 0.75 F1-score

Maize Leaf Disease Image Classification Using Bag of Features

Image classification is an image grouping based on similarities features. The features extraction stage is a crucial factor for classifying an image. In the conventional image classification, the features commonly used are morphology, color, and texture with various derivative features. The type and number of appropriate features will affect the classification results. In this study, image classification by using the Bag of Features (BOF) method which can generate features automatically. It consists of 4 stages: feature point location by using grid method, feature extraction by using Speed Up Robust Feature (SURF), clustering word-visual vocabularies by using k-means, and classification by using Support Vector Machine (SVM). The classification data are maize leaf images from the PlantVillage-Dataset. The data consists of 3 types of images: RGB, grayscale, and segmentation images. Each type includes four classes: healthy, Cercospora, common rust, and northern leaf blight. There are 50 images for each class. We used two scenarios of testing for each type of data: training and validation, 70% and 80% images for training, and the rest for validation. Experimental results showed that the validation accuracies of RGB, grayscale, and segmentation images were 82%, 77%, and 85%.Image classification is an image grouping based on similarities features. The features extraction stage is a crucial factor for classifying an image. In the conventional image classification, the features commonly used are morphology, color, and texture with various derivative features. The type and number of appropriate features will affect the classification results. In this study, image classification by using the Bag of Features (BOF) method which can generate features automatically. It consists of 4 stages: feature point location by using grid method, feature extraction by using Speed Up Robust Feature (SURF), clustering word-visual vocabularies by using k-means, and classification by using Support Vector Machine (SVM). The classification data are maize leaf images from the PlantVillage-Dataset. The data consists of 3 types of images: RGB, grayscale, and segmentation images. Each type includes four classes: healthy, Cercospora, common rust, and northern leaf blight. There are 50 images for each class. We used two scenarios of testing for each type of data: training and validation, 70% and 80% images for training, and the rest for validation. Experimental results showed that the validation accuracies of RGB, grayscale, and segmentation images were 82%, 77%, and 85%

Convolutional neural network for maize leaf disease image classification

This article discusses the maize leaf disease image classification. The experimental images consist of 200 images with 4 classes: healthy, cercospora, common rust and northern leaf blight. There are 2 steps: feature extraction and classification. Feature extraction obtains features automatically using convolutional neural network (CNN). Seven CNN models were tested i.e AlexNet, virtual geometry group (VGG) 16, VGG19, GoogleNet, Inception-V3, residual network 50 (ResNet50) and ResNet101. While the classification using machine learning methods include k-Nearest neighbor, decision tree and support vector machine. Based on the testing results, the best classification was AlexNet and support vector machine with accuracy, sensitivity, specificity of 93.5%, 95.08%, and 93%, respectively

IoT based Automated Plant Disease Classification using Support Vector Machine

Leaf - a significant part of the plant, produces foodusing the process called photosynthesis. Leaf disease can causedamage to the entire plant and eventually lowers crop production.Machine learning algorithm for classifying five types of diseases,such as Alternaria leaf diseases, Bacterial Blight, Gray Mildew,Leaf Curl and Myrothecium leaf diseases, is proposed in theproposed study. The classification of diseases needs front faceof leafs. This paper proposes an automated image acquisitionprocess using a USB camera interfaced with Raspberry PI SoC.The image is transmitted to host PC for classification of diseasesusing online web server. Pre-processing of the acquired image byhost PC to obtain full leaf, and later classification model basedon SVM is used to detect type diseases. Results were checkedwith a 97% accuracy for the collection of acquired images

Leaf Classification Based on GLCM Texture and SVM

This paper involves classification of leaves using GLCM (Gray Level Co-occurrence matrix) texture and SVM (Support Vector Machines). GLCM is used for extracting texture feature of leaves. Creating a plant database for quick and efficient classification and recognition is an important step for their conservation. This approach would help to extract useful features of leaf and improve the accuracy of leaf classification. The standard leaf images are subjected to pre-processing. Feature values are extracted from pre-processed image and they are trained and classified. Standard data sets are used for enhancing the properties of the image