• Journal of KIISE
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• v.42 no.8
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• pp.961-971
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• 2015

As a representative area-decomposed algorithm, an area-based quad-trie (AQT) has an issue of search performance. The search procedure must continue to follow the path to its end, due to the possibility of the higher priority-matching rule, even though a matching rule is encountered in a node. A leaf-pushing AQT improves the search performance of the AQT by making a single rule node exist in each search path. This paper proposes a new algorithm to further improve the search performance of the leaf-pushing AQT. The proposed algorithm implements a leaf-pushing AQT using a hash table and an on-chip Bloom filter. In the proposed algorithm, by sequentially querying the Bloom filter, the level of the rule node in the leaf-pushing AQT is identified first. After this procedure, the rule database, which is usually stored in an off-chip memory, is accessed. Simulation results show that packet classification can be performed through a single hash table access using a reasonable sized Bloom filter. The proposed algorithm is compared with existing algorithms in terms of the memory requirement and the search performance.

• Journal of the Korean Society of Tobacco Science
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• v.5 no.2
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• pp.25-32
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• 1983

To obtain basic information on the breeding of burley tobacco, classification of 41 varieties was carried out by using the cluster analysis of correlation coefficients and taxonomic distance based on twenty-one agromonic characters. Eight characters, such as days to flowering, length of flower axis, internode length, leaf length, yield, leaf angle to stem, vein angle to midrib and plant height, were useful in monothetic classification. Forty-one varieties were classified into four groups (I, II, III and IV) with weighted variable group method (WVGM ) and weighted jai. group method(WPGM), whereas the results classification of 33 varieties among them by WVGM were coincident with the results by WPGM. As for the characteristics of each group, group I related to late maturity, tall height and high yield, group II related to intermediate maturity, tall height and low yield, group 19 related to early maturity, intermediate height and low yield, and group W related to early maturity, short height and intermediate yield.

• International Journal of Computer Science & Network Security
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• v.21 no.2
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• pp.205-211
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• 2021

Plants are important for humanity. They intervene in several areas of human life: medicine, nutrition, cosmetics, decoration, etc. The large number of varieties of these plants requires an efficient solution to identify them for proper use. The ease of recognition of these plants undoubtedly depends on the classification of these species into family; however, finding the relevant characteristics to achieve better automatic classification is still a huge challenge for researchers in the field. In this paper, we have developed a new automatic plant classification technique based on artificial neural networks. Our model uses leaf texture characteristics as parameters for plant family identification. The results of our model gave a perfect classification of three plant families of the Ivorian flora, with a determination coefficient (R2) of 0.99; an error rate (RMSE) of 1.348e-14, a sensitivity of 84.85%, a specificity of 100%, a precision of 100% and an accuracy (Accuracy) of 100%. The same technique was applied on Flavia: the international basis of plants and showed a perfect identification regression (R2) of 0.98, an error rate (RMSE) of 1.136e-14, a sensitivity of 84.85%, a specificity of 100%, a precision of 100% and a trueness (Accuracy) of 100%. These results show that our technique is efficient and can guide the botanist to establish a model for many plants to avoid identification problems.

• Korean Journal of Remote Sensing
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• v.26 no.2
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• pp.87-98
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• 2010

For generating accurate land cover map over the whole Korean Peninsula, post-mosaic classification method is desirable in large area where multiple image data sets are used. We try to derive an optimal mosaic method of multi-temporal Landsat ETM+ scenes for the land cover classification over the Korea Peninsula. Total 65 Landsat ETM+ scenes were acquired, which were taken in 2000 and 2001. To reduce radiometric difference between adjacent Landsat ETM+ scenes, we apply three relative radiometric correction methods (histogram matching, 1st-regression method referenced center image, and 1st-regression method at each Landsat ETM+ path). After the relative correction, we generated three mosaic images for three seasons of leaf-off, transplanting, leaf-on season. For comparison, three mosaic images were compared by the mean absolute difference and computer classification accuracy. The results show that the mosaic image using 1st-regression method at each path show the best correction results and highest classification accuracy. Additionally, the mosaic image acquired during leaf-on season show the higher radiance variance between adjacent images than other season.

• Horticultural Science & Technology
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• v.17 no.1
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• pp.23-28
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• 1999

To examine taxonomic relationships among 53 plums derived from Prunus cerasifera, P. domestica, and P. salicina, principal component analysis (PCA) and cluster analysis on 27 morphological characters were conducted. Of 27 characters, leaf size, leaf shape, and leaf hair were useful characters for plum identification and understanding of taxonomic relationships among them. Leaf length, petiole length, number of leaf nectaries, leaf shape, leaf base, and date of full blooming showed the clear differences between P. salicina group and P. domestica group. Results of cluster analysis using scores of the first three principal components indicated that 53 plums could be grouped into P. salicina-P. cerasifera, P. domestica, and P. spinosa phenon at 1.0 of average distance in UPGMA. Although PCA was useful for rough classification of plums, much more characters were needed for the exact classification.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.11 no.1
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• pp.55-60
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• 1993

Forest occupied a part of natural ecosystem carries out a role of purifying air, preserving water resource, prevention of the breeding and extermination, recreation areas and etc that preserve and for me one's living environment. In this study, the classification for management of this forest is performed with Landsat TM Image. The classes are decided needle-leaf trees, broad-leaf trees, farming land and grass land, and water. When the TM digital images are classified on computer, water is represented in 7∼13 D.N. of 4th band. But the others is appeared similar mostly specific values so that must be done image processing. When the images compounded 2ed band and 3ed band are processed with ratio of enhancement. Needle-leaf treas is represented in l18∼136 D.N. of 1st band, broad-leaf trees in 72∼91 D.N. of 3ed band, farm land and glass land in 96∼120 of 3ed band. Forest Information is classified with M.L.C, an image classification method. The errors of needle-leaf trees, broad-leaf trees, farm land and grass land, and water are appeared each －7.43, ＋1.89,＋7.58 and －2.04 as compared the digital image with investigation on the scene. Finally, these results are useful for classification of forest vegetation with Landsat TM Image.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.21 no.45
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• pp.57-65
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• 1998

This paper addresses the data classification using the induction of decision trees. A weakness of other techniques of induction of decision trees is that decision trees are too large because they construct decision trees until leaf nodes have a single class. Our study include both overcoming this weakness and constructing decision trees which is small and accurate. First, we construct the decision trees using classification threshold and exception threshold in construction stage. Next, we present two stage pruning method using classification threshold and reduced error pruning in pruning stage. Empirical results show that our method obtain the decision trees which is accurate and small.

• Journal of Biosystems Engineering
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• v.32 no.4
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• pp.256-262
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• 2007

Image processing systems have been used to measure the plant parameters such as size, shape and structure of plants. There are yet some limited applications for evaluating plant colors due to illumination conditions. This study was focused to present adaptive methods to analyze plant leaf color regardless of illumination conditions. Color patches attached on the calibration bars were selected to represent leaf colors of lettuces and to test a possibility of health monitoring of lettuces. Repeatability of assigning leaf colors to color patches was investigated by two-tailed t-test for paired comparison. It resulted that there were no differences of assignment histogram between two images of one lettuce that were acquired at different light conditions. It supported that use of the calibration bars proposed for leaf color analysis provided color constancy, which was one of the most important issues in a video color analysis. A health discrimination equation was developed to classify lettuces into one of two classes, SOUND group and POOR group, using the machine vision. The classification accuracy of the developed health discrimination equation was 80.8%, compared to farmers' decision. This study could provide a feasible method to develop a standard color chart for evaluating leaf colors of plants and plant health monitoring system using the machine vision.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.8
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• pp.3312-3327
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• 2020

Plant diseases are a significant yield and quality constraint for farmers around the world due to their severe impact on agricultural productivity. Such losses can have a substantial impact on the economy which causes a reduction in farmer's income and higher prices for consumers. Further, it may also result in a severe shortage of food ensuing violent hunger and starvation, especially, in less-developed countries where access to disease prevention methods is limited. This research presents an investigation of Directional Local Quinary Patterns (DLQP) as a feature descriptor for plants leaf disease detection and Support Vector Machine (SVM) as a classifier. The DLQP as a feature descriptor is specifically the first time being used for disease detection in horticulture. DLQP provides directional edge information attending the reference pixel with its neighboring pixel value by involving computation of their grey-level difference based on quinary value (-2, -1, 0, 1, 2) in 0°, 45°, 90°, and 135° directions of selected window of plant leaf image. To assess the robustness of DLQP as a texture descriptor we used a research-oriented Plant Village dataset of Tomato plant (3,900 leaf images) comprising of 6 diseased classes, Potato plant (1,526 leaf images) and Apple plant (2,600 leaf images) comprising of 3 diseased classes. The accuracies of 95.6%, 96.2% and 97.8% for the above-mentioned crops, respectively, were achieved which are higher in comparison with classification on the same dataset using other standard feature descriptors like Local Binary Pattern (LBP) and Local Ternary Patterns (LTP). Further, the effectiveness of the proposed method is proven by comparing it with existing algorithms for plant disease phenotyping.

• Proceedings of the Turfgrass Society of Korea Conference
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• 2002.02a
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• pp.3-5
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• 2002

This study was carried out to get better understandings about morphological, ecological, and genetical characteristics of annual bluegrass collected from different golf courses in Korea and eventually to establish a successful control strategy. Twenty five local lines of annual bluegrass collected from 20 golf courses in Korea were classified into annual or perennial type on the basis of morphological characteristics. Twelve local lines showing obvious morphological differences were selected and then genetically assessed using RAPD analysis. Classification of the 12 local lines through RAPD analysis were considerably similar to that determined by both of morphological differences and phenotype. Responses of the two types of annual blugrass to herbicides were also examined. Shoot growth of annual bluegrass was significantly suppressed by flazasulfuron and the annual type was more susceptible than perennial type, regardless of flazasulfuron concentrations used. By pendimethalin treatment, there was no clear difference in susceptibility between the two types of annual bluegrass. However, by the treatment of dithiopyr, annual type was more sensitive than perennial type in both shoot and root growth. Nine tree species were screened to detect their allelopathic potential on turfgrasses and annual bluegrass. Acacia (Robinia pseudo-acacia) leaves showed selective inhibition in the shoot and root growth as well as their seed germination when treated with 2% and 10%(v/v) of the extract. However, the other leaf extracts except acacia inhibited non-selectively the growth of three turfgrass species such as bentgrass, perennial ryegrass and zoysiagrass and annual bluegrass. The PAL activities of annual bluegrass increased at 24 h after treatment of acacia leaf extract and peaked at 36 h and then decreased till 60h. The highest PAL activity was observed at 36h after treatment of 10%. The highest activity of CA4H in annual bluegrass was observed at 2h after treatment of acacia extract and the level was 4 times greater than that of the control. The phenolic acids such as p-coumaric acid, salicylic acid and ferulic acid were increased with the treatment of acacia leaf extract. The chloroplast membrane and cell wall of annual bluegrass were destroyed by treatment of acacia leaf extract and its inner materials were released. The membranes in annual bluegrass cells might be destroyed by phytotoxic compounds from acacia leaf extract.