The objective of this project was to compile estimates of yield loss in soybean [Glycine max (L.) Merr] to diseases in the top eight soybean-producing countries in 2006. The purpose was to provide information needed by local and world agencies to allocate funds for research and to help scientists focus and coordinate research efforts. Methods used by plant pathologists to estimate yield loss to diseases in these countries included systematic field surveys, cultivar trials, diagnostic clinic records, personal observations, and questionnaires sent to crop consultants and extension staff. The 2006 harvest of soybeans in the top eight soybean-producing countries was reduced an estimated 59.9 million metric tonnes (t) by diseases according to results of the current study. Soybean rust, caused by Phakopsora pachyrhizi, reduced yield in all these countries except Canada in 2006, and the total was more than any other. Next in decreasing order of total yield loss were soybean cyst nematode, brown spot, seedling diseases, anthracnose, and charcoal rot. Accepted for publication 27 October 2009. Published 25 January 2010.

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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.

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During 2014 to 2015, a devastating bacterial soft rot on cucumber stems and leaves occurred in Shandong, Shanxi, Hebei, Henan, and Liaoning provinces of China, resulting in serious economic losses for cucumber production. The gummosis emerged on the surface of leaves, stems, petioles, and fruit of cucumber. The basal stem color was dark brown and the stem base turned to wet rot. Yellow spots and wet rot emerged at the edge of the infected cucumber leaves and gradually infected the leaf centers. In total, 45 bacterial strains were isolated from the infected tissues. On the basis of phenotypic properties of morphology, physiology, biochemistry, and 16S ribosomal RNA gene sequence analysis, the pathogen was identified as Pectobacterium carotovorum. Multilocus sequence analysis confirmed that the isolates were P. carotovorum subsp. brasiliense, and the pathogens fell in clade II. The pathogenicity of isolated bacteria strains was confirmed. The strains reisolated were the same as the original. The host range test confirmed that strains had a wide range of hosts. As far as we know, this is the first report of cucumber stem soft rot caused by P. carotovorum subsp. brasiliense in China as well as in the world, which has a significant economic impact on cucumber production.

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