Comparison of Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), and Stochastic Gradient Descent (SGD) for Classifying Corn Leaf Disease based on Histogram of Oriented Gradients (HOG) Feature Extraction (original) (raw)
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International Journal of Computer and Information Technology(2279-0764)
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 ac...
International Journal of Electrical and Computer Engineering (IJECE), 2022
Corn leaf disease possesses a huge impact on the food industry and corn crop yield as corn is one of the essential and basic nutrition of human life especially to vegetarians and vegans. Hence it is obvious that the quality of corn has to be ideal, however, to achieve that it has to be protected from the several diseases. Thus, there is a high demand for an automated method, which can detect the disease in early-stage and take necessary steps. However, early disease detection possesses a huge challenge, and it is highly critical. Thus, in this research work, we focus on designing and developing enhanced k-nearest neighbor (EKNN) model by adopting the basic k-nearest neighbour (KNN) model. EKNN helps in distinguishing the different class disease. Further fine and coarse features with high quality are generated to obtain the discriminative, boundary, pattern and structural related information and this information are used for classification procedure. Classification process provides the gradient-based features of high quality. Moreover, the proposed model is evaluated considering the Plant-Village dataset; also, a comparative analysis is carried out with different traditional classification model with different metrics.
Machine Learning-based for Automatic Detection of Corn-Plant Diseases Using Image Processing
Tarım Bilimleri Dergisi , 2024
Corn is one of the major crops in Sudan. Disease outbreaks can significantly reduce maize production, causing huge damage. Conventionally, disease diagnosis is made through visual inspection of the damage in fields or through laboratory tests conducted by experts on the affected plant parts of the crop. This process typically requires highly skilled personnel, and it can be time-consuming to complete the necessary tasks. Machine learning methods can be implemented to rapidly and accurately detect disease and reduce the risk of crop failure due to disease outbreaks. This study aimed to use traditional machine learning techniques to detect maize diseases using image processing techniques. A total of 600 images were obtained from the open-source Plant Village dataset for experimentation. In this study, image segmentation was done using K-means clustering, and a total of 4 GLCM texture features and two statistical features were extracted from the images. In this study, four traditional machine learning algorithms were applied to detect diseased maize leaves (common rust and gray leaf spot) and healthy maize leaves. The results showed that all the algorithms performed well in identifying the diseased and healthy leaves, with accuracy rates ranging from 90% to 92.7%. The highest accuracy scores were obtained with support vector machine and artificial neural networks, respectively.
Leaf Diseases Detection of Medicinal Plants Based on Support Vector Machine Classification Algorithm
Journal of Pharmaceutical Research International, 2021
On earth, plants play the most important part. Every organ of a plant plays a vital role in the ecological field as well as the medicinal field. But on the whole earth there are several species of plants are available. The different species of plants have different diseases. Therefore, it is required to identify the plants as well as their diseases correctly. It is difficult and also time consuming to identify the plants and their diseases manually. In this research an automatic disease detection system of plant is proposed. High-quality leaf images are used for training and testing. For detecting the healthy area and diseased area in a leaf, region-based and color-based region thresholding techniques are used. For feature selection Histogram Oriented Gradient (HOG) and Local Binary Pattern (LBP) method were applied. Finally, for classification two-class and multi-class Support Vector Machine (SVM) were used. It is found that both feature selection processes with SVM give 99% accura...
SVM and ANN Based Classification of Plant Diseases Using Feature Reduction Technique
Computers have been used for mechanization and automation in different applications of agriculture/horticulture. The critical decision on the agricultural yield and plant protection is done with the development of expert system (decision support system) using computer vision techniques. One of the areas considered in the present work is the processing of images of plant diseases affecting agriculture/horticulture crops. The first symptoms of plant disease have to be correctly detected, identified, and quantified in the initial stages. The color and texture features have been used in order to work with the sample images of plant diseases. Algorithms for extraction of color and texture features have been developed, which are in turn used to train support vector machine (SVM) and artificial neural network (ANN) classifiers. The study has presented a reduced feature set based approach for recognition and classification of images of plant diseases. The results reveal that SVM classifier is more suitable for identification and classification of plant diseases affecting agriculture/horticulture crops.
International Journal of Computer Trends and Technology (IJCTT), Vol. 68 No. 4, 2020
Identification of plant disease is very important to prevent the loss and keep the harvest healthy. Determination of plant disease via visual monitoring is difficult and time consuming. In this paper, we described a method of detection and classification of plant disease using image processing and machine learning techniques. We used standard images of leaves of several types of plants to test our method. Initially, our method segments the input image to isolate disease parts of the leaf. Then we obtain various features from the diseased affected segmented image. Finally, we classify leaves into healthy and disease types based on its features using Multiclass Support Vector Machine (SVM) classifier. Experimental results indicate that our method yields very high accuracy rate for detection and classification of plant diseases.
Maize Leaf Disease Image Classification Using Bag of Features
JURNAL INFOTEL
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...
Blue Eyes Intelligence Engineering & Sciences Publication, 2019
Pest attack and infectious diseases has become more common in the field of agriculture in the recent times. It has become a challenging task to identify the infection or the insect that destructs the plant growth and production. Diagnosing the disease or the insect attack on the plants in the early stage will safe guard the plant growth and the production rate. Timely intervention of technology that deals with disease detection and control method can protect the plants from usage of harmful pesticides. The higher dosage of pesticides impacts the health of human as well as other creatures like birds and animals which directly or indirectly consumes the plant or get in touch with the plants in different circumstances. A Computer vision technique which combines the Digital Image processing and Machine Learning methodology has been proposed to provide pest management solution. The disease detection is based on the statistical texture feature analysis and it is classified using K nearest neighbor classifier. Statistical PCA is combined with SIFT method to extract the key points, which eliminates the non-operational key points and SFTA is used to extract the texture. The system has achieved better result in identifying and differentiating the infection and insect attack on multiple plant taxonomy. The implementation has been performed using MATLAB.
Classification of Paddy Leaf using SVM Classifier
Khaing War Htun, 2019
The value of paddy is strongly related to the quality, types and sizes of paddy without any damage or disease of that leaves. Hence detecting the disease or damage area of the leaf is very important to improve the utilization rate. Though the crop production is well grown there is still lagging in visual inspection of diseases. Although it is done manually, it is not accurate in all the times. Therefore, there is a need for technique to detect the diseases. The system proposed is based on this method which can classify the diseases using Support Vector Machine (SVM). This system involves image acquisition, converting the RGB images into gray scale image and morphological process for removing noise. Principal Component Analysis (PCA), Color Grid based Moment and Gray Level Co-occurrence matrix (GLCM) are used for extracting the shape, color and texture features like mean, standard deviation, contrast, correlation, energy and entropy of image. Finally, it is classified based on the diseases using support vector machine. The system is achieved 85% accuracy rate.