Identification of Alfalfa Leaf Diseases Using Image Recognition Technology

Abstract

Common leaf spot (caused by Pseudopeziza medicaginis), rust (caused by Uromyces striatus), Leptosphaerulina leaf spot (caused by Leptosphaerulina briosiana) and Cercospora leaf spot (caused by Cercospora medicaginis) are the four common types of alfalfa leaf diseases. Timely and accurate diagnoses of these diseases are critical for disease management, alfalfa quality control and the healthy development of the alfalfa industry. In this study, the identification and diagnosis of the four types of alfalfa leaf diseases were investigated using pattern recognition algorithms based on image-processing technology. A sub-image with one or multiple typical lesions was obtained by artificial cutting from each acquired digital disease image. Then the sub-images were segmented using twelve lesion segmentation methods integrated with clustering algorithms (including K_means clustering, fuzzy C-means clustering and K_median clustering) and supervised classification algorithms (including logistic regression analysis, Naive Bayes algorithm, classification and regression tree, and linear discriminant analysis). After a comprehensive comparison, the segmentation method integrating the K_median clustering algorithm and linear discriminant analysis was chosen to obtain lesion images. After the lesion segmentation using this method, a total of 129 texture, color and shape features were extracted from the lesion images. Based on the features selected using three methods (ReliefF, 1R and correlation-based feature selection), disease recognition models were built using three supervised learning methods, including the random forest, support vector machine (SVM) and K-nearest neighbor methods. A comparison of the recognition results of the models was conducted. The results showed that when the ReliefF method was used for feature selection, the SVM model built with the most important 45 features (selected from a total of 129 features) was the optimal model. For this SVM model, the recognition accuracies of the training set and the testing set were 97.64% and 94.74%, respectively. Semi-supervised models for disease recognition were built based on the 45 effective features that were used for building the optimal SVM model. For the optimal semi-supervised models built with three ratios of labeled to unlabeled samples in the training set, the recognition accuracies of the training set and the testing set were both approximately 80%. The results indicated that image recognition of the four alfalfa leaf diseases can be implemented with high accuracy. This study provides a feasible solution for lesion image segmentation and image recognition of alfalfa leaf disease.

Identification of Apple Leaf Diseases Based on Deep Convolutional Neural Networks

“…In the last few years, traditional machine learning algorithms have been widely used to realize disease detection. In [6], Qin et al proposed a feasible solution for lesion image segmentation and image recognition of alfalfa leaf disease. The ReliefF method was first used to extract a total of 129 features, and then an SVM model was trained with the most important features.…”

“…With the popularity of machine learning algorithms in computer vision, in order to improve the accuracy and rapidity of the diagnosis results, researchers have studied automated plant disease diagnosis based on traditional machine learning algorithms, such as random forest, k-nearest neighbor, and Support Vector Machine (SVM) [3][4][5][6][7][8][9][10][11][12]. However, because the classification features are selected and adopted based on human experience, these approaches improved the recognition accuracy, but the recognition rate is still not high enough and is vulnerable to artificial feature selection.…”

An End-To-End Practical Plant Disease Diagnosis System for Wide-Angle Cucumber Images

“…We introduce a brightness adjustment on every single extracted image patch to adjust the brightness. In our experiment, we calculated the perceived brightness (PB) as in (1). Note that there are several ways to calculate this value.…”

“…In this context, many automatic computer-based diagnosis methodologies which are capable of identifying diseases in a rapid and reliable way have been recently proposed. In [1] used conventional image segmentation techniques and a support vector machine (SVM) [2] to classify four types of alfalfa diseases in leaves. The SVM classifier achieved an accuracy of 94.7% on images in a laboratory environment.…”

A Practical Plant Diagnosis System for Field Leaf Images and Feature Visualization

“…In such circumstances, methodologies for automated plant diagnosis characterized by accuracy, speed and low costs have been requested by the agricultural industry. Several studies have been carried out in response to such requests [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23]. In [4] used support vector machines (SVM) to classify rice plant diseases and attained 92.7% accuracy.…”