Search

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The wide use of plastic greenhouses in the word has brought economic benefits, but also caused many environmental problems. Accurate and timely acquisition of spatial distribution information of plastic greenhouse is of great significance to agricultural production and soil management. The use of plastic greenhouse has changed the structure of soil, thus changing the surface spectral characteristics of the soil. Because of the particularity of the plastic film material, the reflection spectrum has strong directionality and uncertainty, so it is difficult to identify plastic greenhouse accurately only depending on the reflectance spectrum characteristics of ground objects. In this paper, Kunming City, Yunnan Province is taken as the research area, using GF-2 image as a single data source, the multi-scale segmentation method is used to obtain the image object efficiently and accurately. According to the detailed spatial information of plastic greenhouse, the applicability of three image object-based texture extraction algorithms, namely GLCM(gray-level co-occurrence matrix), LBP(local binary pattern) and PSI(pixel shape index) for plastic greenhouse identification is analysed and tested. In addition, different texture features are combined with spectral features and NDVI index to form different classification schemes, to explore which one would be the best combination of texture features for identification of plastic greenhouses. In order to explore the robustness of the method, different texture feature combination schemes are applied in the study areas. The results of SVM (support vector machine) classifier are evaluated by confusion matrix. The results show that the overall combination schemes of the two research areas with different landscape patterns have the same trend. The combination of spectral features and NDVI index can accurately identify the scattered waters in each of the two study areas. For plastic greenhouses and impervious surfaces with similar reflectance spectra, adding texture features can make up for limitation of spectral characteristics and improve the overall accuracy. The phenomena of homologous or homologous spectra in spectral features of high spatial resolution image can effectively improve the discrimination between plastic greenhouse and impervious surface. Texture features can significantly improve the identification accuracy of plastic greenhouse following the object-based image classification frame. In the classification scheme of plastic greenhouses with different spatial distribution structures, the LBP (local binary pattern) texture algorithm has the best recognition accuracy, the overall accuracy of study area A is 96.85%, Kappa coefficient is 0.95, and that of study area B is 95.58% and 0.94. Landscape fragment analysis (landscape fragmentation index area mean index, aggregation indices) of the two different study areas showed that the plastic greenhouses in study area A are more fragmented than study area B (area mean indices are 3.39 hm² and 1.37 hm², aggregation indices are 80.64 and 72.98 for plastic greenhouses in study area A and B, respectively). The results of fragmentation are consistent with those of classification, and the accuracy of landscape classification with more space fragments is lower (the highest overall classification accuracy for study area A and B are 98.13% and 96.13%, respectively, the PA(producer accuracy) and UA(user accuracy) are 96.47% and 97.93% for study area A, and 90.67%, 99.68% for study area B). The results show that texture features based on image objects can improve the recognition accuracy of small-scale plastic greenhouse. This is of great significance to the accurate mapping of the distribution of plastic greenhouses. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Honey pomelo, one of the most important fruits in China, always suffers a variety of diseases during the whole process of planting, such as maculopathy, anthracnose, scab and dark mildew, which seriously affects the fruit quality and yield. The accurate recognition of honey pomelo leaf diseases is the premise of the treatment of honey pomelo diseases, and the precision directly affects the efficiency in controlling diseases. However, most of the current researches on disease recognition aimed at the global information of the study objects, but ignored the disease's local feature extraction in multi-scale and multi-direction; in addition, the present researches generally used the method of "one to one" or "one to many" when building many types of support vector machine (SVM) in the disease classification model, few researches used the method about SVM based on directed acyclic decision tree. So, leaf diseases recognition of honey pomelo based on SVM of directed acyclic decision tree was put forward in this paper. At first, statistical analysis on components of color characteristics of collected honey pomelo leaf diseases was carried on, and the conclusion was drawn according to the statistics of component B, component 2G-R-B, component (G+R+B)/3 and component Q in YIQ color model, which were easily distinguished among the 4 diseases, and so the 4 color components were used as disease color features. Secondly, honey pomelo leaf disease images were converted into 4 grayscale images of component B, component 2G-R-B, component (G+R+B)/3 and component Q in YIQ color model. Gabor wavelet with 5 dimensions and 8 directions was used for convolution calculation with 4 grayscale component images, and 16-dimension energy sub-band was got, the mean value of which was used as eigenvector. Disease recognition model of three-level directed acyclic decision tree SVM was constructed by 6 SVM classifiers, in order to recognize 4 honey pomelo diseases, i.e. maculopathy, anthracnose, scab and dark mildew. According to the test results of cross validation method, the recognition accuracies of maculopathy, anthracnose, scab and dark mildew respectively reached 90%, 96.66%, 93.33% and 96.66%, and the average recognition rate of the 4 diseases was 94.16%, showing that the method could effectively recognize the 4 honey pomelo leaf diseases. Optimal binary tree SVM proposed in this paper was compared with BP neural network, one-to-one SVM and one-to-many SVM in different characteristic dimensions, and the results showed that the training time of the proposed method in this paper and other 3 methods was respectively 740 ms, 420 ms, 450 ms and 370 ms, and the disease recognition accuracy of the 4 methods was respectively 86%, 91.5%, 90% and 94.16%. The method proposed in this paper is superior to the other 3 algorithms in training time and recognition precision. So the proposed method can provide technical support for the accurate recognition of honey pomelo leaf diseases, in favor of the prevention and treatment of pomelo diseases, and also provide references for the prevention and cure of other plant's leaf diseases. [ABSTRACT FROM AUTHOR]