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.
Highlights
Alfalfa (Medicago sativa) is an important forage grass containing various nutrients
When the 1R method was used for feature selection, with an increase in the number of decision trees, the recognition accuracies of the training set and the testing set for the built random forest models fluctuated by 0%-2.00%, and the number of applied features changed in a range of 76–129
When the correlation-based feature selection (CFS) method was applied to feature selection, with the increase of the number of decision trees, the recognition accuracies of the training set and the testing set for the built random forest models fluctuated by 0%-2.18%
Summary
Alfalfa (Medicago sativa) is an important forage grass containing various nutrients. The occurrence of disease in alfalfa plants has an important influence on the yield and quality of alfalfa hay, affecting the healthy development of the alfalfa industry [1]. There are more than ten types of alfalfa leaf diseases [2, 3]. Some of these diseases have similar symptoms, resulting in difficulties in achieving an accurate diagnosis and identifying the disease via naked-eye observations of symptoms or microscopic observations of causal agents. The diagnosis and identification of alfalfa diseases mainly rely on the experience of farmers, agricultural experts or agricultural technicians. The rapid, accurate identification and diagnosis of diseases will help to reduce yield losses and quality decline of alfalfa hay, resulting from the diseases. With the rapid development of computer technology and information technology, it is possible to utilize image-processing technology to diagnose and identify alfalfa leaf diseases quickly, accurately and automatically
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