• Weed & Turfgrass Science
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• v.6 no.1
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• pp.55-60
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• 2017

Shade affects turf quality by reducing light for photosynthesis. The shade tolerance of the tropical grasses, Digitaria longiflora and Axonopus compressus were evaluated against Stenotaphrum secundatum (St. Augustinegrass). The grasses were established under shade structures that provide 0%, 50%, 75% or 90% shade level for 30 days. A suite of leaf traits, recorded from similar leaf developmental stage, displayed distinct responses to shade conditions. Leaf length, relative to control, increased in all three species as shade level increased. The mean leaf extension rate was lowest in St. Augustinegrass (80.42%) followed by A. compressus (84.62%) and D. longiflora (90.78%). The higher leaf extension rate in D. longiflora implied its poor shade tolerance. Specific leaf area (SLA) increased in all species with highest mean SLA increase in D. longiflora ($348.55cm^2mg^{-1}$)followed by A. compressus ($286.88cm^2mg^{-1}$) and St. Augustinegrass ($276.28cm^2mg^{-1}$). The highest SLA increase in D. longiflora suggested its lowest performance under shade. The percent green cover, as estimated by digital image analysis, was lowest in D. longiflora (53%) under 90% shade level compared to both species. The relative shade tolerance of the three turfgrasses could be ranked as St. Augustinegrass > A. compressus > D. longiflora.

• International Journal of Computer Science & Network Security
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• v.24 no.8
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• pp.72-84
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• 2024

Plant leaf classification is an imperative task when their use in real world is considered either for medicinal purposes or in agricultural sector. Accurate identification of plants is, therefore, quite important, since there are numerous poisonous plants which if by mistake consumed or used by humans can prove fatal to their lives. Furthermore, in agriculture, detection of certain kinds of weeds can prove to be quite significant for saving crops against such unwanted plants. In general, Artificial Neural Networks (ANN) are a suitable candidate for classification of images when small datasets are available. However, these suffer from local minima problems which can be effectively resolved using some global optimization techniques. Considering this issue, the present research paper presents an automated plant leaf classification system using optimized soft computing models in which ANNs are optimized using Grasshopper Optimization algorithm (GOA). In addition, the proposed model outperformed the state-of-the-art techniques when compared with simple ANN and particle swarm optimization based ANN. Results show that proposed GOA-ANN based plant leaf classification system is a promising technique for small image datasets.

• 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.

• 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.

• Journal of Multimedia Information System
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• v.6 no.3
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• pp.155-164
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• 2019

We intended to confirm that microwave attenuation by tree leaves is strongly linked to water content in leaves. We sampled natural broadleaves, including Japanese cinnamon, and investigated their effects on the microwave (3 to 20 GHz) frequency characteristics using a network analyzer. Experiments determined that microwave attenuation by foliage increases as a linear function of frequency per unit weight (gram). As the frequency increases, the spatial resolution increases, but the phase difference (imaginary component) increases. So we solved the dispersion of phase difference by sweeping the frequency and taking the intermediate value. Based on these experimental results, we developed a microwave scanner on 10Ghz to describe foliage moisture as a image and to enable assessments of leaf condition. Photosynthesis is the process whereby plants synthesize oxygen and sugars from carbon dioxide and water, thereby converting light energy into chemical energy. Since water is a major parameter of photosynthesis, the quantity of water accumulated inside a leaf reflects leaf health. The equipment described here and related microwave technologies will help assess the capacity of leaves to absorb atmospheric carbon dioxide.

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.243-243
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• 2017

Leaf colors of rice can be used to identify stress level due to its adaptation to environmental change. For most leaves green-related colors are sourced from chlorophyll a and b. For most leaves green-related colors are consisted of chlorophyll a and b. Chlorophyll concentration is normally measured using a spectrophotometer in laboratory. In some remote observation fields, it is impossible to collect the leaves, preserve them, and bring them to laboratory to measure their chlorophyll content. The measurement of chlorophyll content is observed through its color. Using CIE chromaticity diagram leaf color information in RGB is transformed into wavelength (in nm). Pheophytin contents were also analyzed in 95% ethanol extracts. In the process of leaf development of rice young seedling, both pigments were compared. Leaf samples from different rice seedling bed is taken, their colors and RGB values are recorded using Photoshop Image Analysis. SPAD-502 values were also measured. The chlorophyll and Pheophytin contents were fully estimated by ${\rightthreetimes}_{avg}$ on CIE chromaticity diagram.

• Progress in Medical Physics
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• v.29 no.2
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• pp.66-72
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• 2018

The proper position of a multi-leaf collimator (MLC) is essential for the quality of intensity-modulated radiation therapy (IMRT) and volumetric modulated arc radiotherapy (VMAT) dose delivery. Task Group (TG) 142 provides a quality assurance (QA) procedure for MLC position. Our study investigated the QA validation of the mechanical leaf gap measurement and the maintenance procedure. Two $VitalBeam^{TM}$ systems were evaluated to validate the acceptance of an MLC position. The dosimetric leaf gaps (DLGs) were measured for 6 MV, 6 MVFFF, 10 MV, and 15 MV photon beams. A solid water phantom was irradiated using $10{\times}10cm^2$ field size at source-to-surface distance (SSD) of 90 cm and depth of 10 cm. The portal dose image prediction (PDIP) calculation was implemented on a treatment planning system (TPS) called $Eclipse^{TM}$. A total of 20 VMAT plans were used to confirm the accuracy of dose distribution measured by an electronic portal imaging device (EPID) and those predicted by VMAT plans. The measured leaf gaps were 0.30 mm and 0.35 mm for VitalBeam 1 and 2, respectively. The DLG values decreased by an average of 6.9% and 5.9% after mechanical MLC adjustment. Although the passing rates increased slightly, by 1.5% (relative) and 1.2% (absolute) in arc 1, the average passing rates were still within the good dose delivery level (>95%). Our study shows the existence of a mechanical leaf gap error caused by a degenerated MLC motor. This can be recovered by reinitialization of MLC position on the machine control panel. Consequently, the QA procedure should be performed regularly to protect the MLC system.

• Journal of the Korean Society of Radiology
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• v.9 no.7
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• pp.535-540
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• 2015

Collimator has important functions with control primary X-ray that decrease radiation exposure dose for patients and reduce scatter ray and make better quality of image. But there are no regulations for X-ray mammography device of collimator, so widely used device adopt rectangularly controlled collimator. Though digital X-ray mammography device expand supply recently, rectangularly controlled collimator of film/screen mode still used. After searching for real condition of beam field with digital mammography, we made a multi-leaf collimator which is able to adjust the beam field in accordance with size and shape of breast, and we measuring up the transitions of image quality, average glandular dose(AGD) and, Dose area product(DAP). There are no significant differences between rectangularly controlled collimator and multi-leaf collimator, and DAP value decreased by 50.72%. As conclusion, there needs to expand the use of multi-leaf collimator for optimum adoption of beam field in digital mammography, and also need to develop an automatic regulation of beam field for reduce of exposure dose to patients.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.77-90
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• 2023

Today, crops face many characteristics/diseases. Insect damage is one of the main characteristics/diseases. Insecticides are not always effective because they can be toxic to some birds. It will also disrupt the natural food chain for animals. A common practice of plant scientists is to visually assess plant damage (leaves, stems) due to disease based on the percentage of disease. Plants suffer from various diseases at any stage of their development. For farmers and agricultural professionals, disease management is a critical issue that requires immediate attention. It requires urgent diagnosis and preventive measures to maintain quality and minimize losses. Many researchers have provided plant disease detection techniques to support rapid disease diagnosis. In this review paper, we mainly focus on artificial intelligence (AI) technology, image processing technology (IP), deep learning technology (DL), vector machine (SVM) technology, the network Convergent neuronal (CNN) content Detailed description of the identification of different types of diseases in tomato and potato plants based on image retrieval technology (CBIR). It also includes the various types of diseases that typically exist in tomato and potato. Content-based Image Retrieval (CBIR) technologies should be used as a supplementary tool to enhance search accuracy by encouraging you to access collections of extra knowledge so that it can be useful. CBIR systems mainly use colour, form, and texture as core features, such that they work on the first level of the lowest level. This is the most sophisticated methods used to diagnose diseases of tomato plants.

• Proceedings of the KIEE Conference
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• 1991.11a
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• pp.201-204
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• 1991

In this paper the method of representing the document image by the quadtree data structure, and extracting each character seperately from the constructed quadtree are described. The document image is represented by a binary encoded quadtree and the segmentation is performed according to the information of each leaf node of the quadtree. Then, each character is extracted by the relation of positions of segments. This method enables to extract characters without examining every pixel in the image and the required storage of document image is decreased.