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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.

SUBMITTER: Qin F

PROVIDER: S-EPMC5158033 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Identification of Alfalfa Leaf Diseases Using Image Recognition Technology.

Qin Feng F Liu Dongxia D Sun Bingda B Ruan Liu L Ma Zhanhong Z Wang Haiguang H

PloS one 20161215 12

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 ...[more]

PMID: 27977767

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Project description:To diagnose neurodegenerative diseases (NDDs), physicians have been clinically evaluating symptoms. However, these symptoms are not very dependable-particularly in the early stages of the diseases. This study has therefore proposed a novel classification algorithm that uses a deep learning approach to classify NDDs based on the recurrence plot of gait vertical ground reaction force (vGRF) data. The irregular gait patterns of NDDs exhibited by vGRF data can indicate different variations of force patterns compared with healthy controls (HC). The classification algorithm in this study comprises three processes: a preprocessing, feature transformation and classification. In the preprocessing process, the 5-min vGRF data divided into 10-s successive time windows. In the feature transformation process, the time-domain vGRF data are modified into an image using a recurrence plot. The total recurrence plots are 1312 plots for HC (16 subjects), 1066 plots for ALS (13 patients), 1230 plots for PD (15 patients) and 1640 plots for HD (20 subjects). The principal component analysis (PCA) is used in this stage for feature enhancement. Lastly, the convolutional neural network (CNN), as a deep learning classifier, is employed in the classification process and evaluated using the leave-one-out cross-validation (LOOCV). Gait data from HC subjects and patients with amyotrophic lateral sclerosis (ALS), Huntington's disease (HD) and Parkinson's disease (PD) obtained from the PhysioNet Gait Dynamics in Neurodegenerative disease were used to validate the proposed algorithm. The experimental results included two-class and multiclass classifications. In the two-class classification, the results included classification of the NDD and the HC groups and classification among the NDDs. The classification accuracy for (HC vs. ALS), (HC vs. HD), (HC vs. PD), (ALS vs. PD), (ALS vs. HD), (PD vs. HD) and (NDDs vs. HC) were 100%, 98.41%, 100%, 95.95%, 100%, 97.25% and 98.91%, respectively. In the multiclass classification, a four-class gait classification among HC, ALS, PD and HD was conducted and the classification accuracy of HC, ALS, PD and HD were 98.99%, 98.32%, 97.41% and 96.74%, respectively. The proposed method can achieve high accuracy compare to the existing results, but with shorter length of input signal (Input of existing literature using the same database is 5-min gait signal, but the proposed method only needs 10-s gait signal).

Project description:Background and aimsThe growth of crops in a mixture is more variable and difficult to predict than that in pure stands. Light partitioning and crop leaf area expansion play prominent roles in explaining this variability. However, in many crops commonly grown in mixtures, including the forage species alfalfa, the sensitivity and relative importance of the physiological responses involved in the light modulation of leaf area expansion are still to be established. This study was designed to assess the relative sensitivity of primary shoot development, branching and individual leaf expansion in alfalfa in response to light availability.MethodsTwo experiments were carried out. The first studied isolated plants to assess the potential development of different shoot types and growth periods. The second consisted of manipulating the intensity of competition for light using a range of canopies in pure and mixed stands at two densities so as to evaluate the relative effects on shoot development, leaf growth, and plant and shoot demography.Key resultsShoot development in the absence of light competition was deterministic (constant phyllochrons of 32·5 °Cd and 48·2 °Cd for primary axes and branches, branching probability of 1, constant delay of 1·75 phyllochron before axillary bud burst) and identical irrespective of shoot type and growth/regrowth periods. During light competition experiments, changes in plant development explained most of the plant leaf area variations, with average leaf size contributing to a lesser extent. Branch development and the number of shoots per plant were the leaf area components most affected by light availability. Primary axis development and plant demography were only affected in situations of severe light competition.ConclusionsPlant leaf area components differed with regard to their sensitivity to light competition. The potential shoot development model presented in this study could serve as a framework to integrate light responses in alfalfa crop models.

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Identification of Alfalfa Leaf Diseases Using Image Recognition Technology.

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Identification of Alfalfa Leaf Diseases Using Image Recognition Technology.

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