• Journal of The Korean Society of Agricultural Engineers
• /
• v.63 no.6
• /
• pp.39-47
• /
• 2021

Reduction effect of the spread of odorant and fine dust through windbreak trees can be predicted through numerical analysis. However, there is a disadvantage that a large space and destructive experiments must be carried out each time to calculate the aerodynamic coefficient of the tree. In order to overcome these shortcomings, In this study, we aimed to estimate the aerodynamic coefficient (C₀, C₁, C₂) by using image processing. Thuja occidentalis, which can be used as windbreak were used as the material. The leaf area index was estimated from the leaf area ratio using image processing with leaf weight, and the optical porosity was calculated through image processing of photos taken from the side while removing the leaves step-by-step. Correlation analysis was conducted with the aerodynamic coefficient of Thuja occidentalis calculated from the wind tunnel test and leaf area index and optical porosity calculated from the image analysis. The aerodynamic coefficient showed positive and negative correlations with the leaf area index and optical porosity, respectively. The results showed that the possibility of estimating the aerodynamic coefficient using image processing.

• Journal of Biosystems Engineering
• /
• v.39 no.4
• /
• pp.318-323
• /
• 2014

Purpose: This study was carried out to predict the growth period and fresh weight of sprouts grown in a cultivator designed to grow sprouts under optimal conditions. Methods: The temperature, light intensity, and amount of irrigation were controlled, and images of seed sprouts were acquired to predict the days of growth and weight from pixel counts of leaf area. Broccoli, clover, and radish sprouts were selected, and each sprout was cultivated in a 90-mm-diameter Petri dish under the same cultivating conditions. An image of each sprout was taken every 24 hours from the 4th day, and the whole cultivating period was 6 days, including 3 days in the dark. Images were processed by histogram inspection, binary images, image erosion, image dilation, and the overlay image process. The RGB range and ratio of leaves were adjusted to calculate the pixel counts for leaf area. Results: The correlation coefficients between the pixel count of leaf area and the growth period of sprouts were 0.91, 0.98, and 0.97 for broccoli, clover, and radish, respectively. Further, the correlation coefficients between the pixel count of leaf area and fresh weight were 0.90 for broccoli, 0.87 for clover, and 0.95 for radish. Conclusions: On the basis of these results, we suggest that the simple image acquisition system and processing algorithm can feasibly estimate the growth period and fresh weight of seed sprouts.

• Journal of radiological science and technology
• /
• v.42 no.5
• /
• pp.365-371
• /
• 2019

We have developed and applied a diagnostic Multi-Leaf Collimator (MLC) to optimized the X-ray field in medical imaging and the usefulness evaluated through the fusion of infrared image and X-ray image acquired by infrared camera. The hand and skull radiography with multi-leaf collimator(MLC) showed significant area dose reductions of 22.9% and 31.3% compared to ARC and leakage dose was compliant with KS A 4732. Also scattering doses of 50 cm and 100 cm showed a significant decrease to confirm the usefulness of MLC. It was confirmed that the fusion of infrared images with an adjustable degree of transparency was possible in the X-ray images. Therefore, fusion of anatomical information with physiological convergence is expected to contribute and improvement of diagnostic ability. In addition, the feasibility of convergence X-ray imaging and DITI devices and the possibility of driving MLC with infrared images were confirmed.

• Journal of Information Processing Systems
• /
• v.16 no.5
• /
• pp.1083-1094
• /
• 2020

Recognizing plant species based on leaf images is challenging because of the large inter-class variation and inter-class similarities among different plant species. The effective extraction of leaf descriptors constitutes the most important problem in plant leaf recognition. In this paper, a multi-scale angular description method is proposed for fast and accurate leaf recognition and retrieval tasks. The proposed method uses a novel scale-generation rule to develop an angular description of leaf contours. It is parameter-free and can capture leaf features from coarse to fine at multiple scales. A fast Fourier transform is used to make the descriptor compact and is effective in matching samples. Both support vector machine and k-nearest neighbors are used to classify leaves. Leaf recognition and retrieval experiments were conducted on three challenging datasets, namely Swedish leaf, Flavia leaf, and ImageCLEF2012 leaf. The results are evaluated with the widely used standard metrics and compared with several state-of-the-art methods. The results and comparisons show that the proposed method not only requires a low computational time, but also achieves good recognition and retrieval accuracies on challenging datasets.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.2 no.6
• /
• pp.280-298
• /
• 2008

For efficient content-based image retrieval, diverse visual features such as color, texture, and shape have been widely used. In the case of leaf images, further improvement can be achieved based on the following observations. Most plants have unique shape of leaves that consist of one or more blades. Hence, blade-based matching can be more efficient than whole shape-based matching since the number and shape of blades are very effective to filtering out dissimilar leaves. Guaranteeing rotational invariance is critical for matching accuracy. In this paper, we propose a new shape representation, indexing and matching scheme for leaf image retrieval. For leaf shape representation, we generated a distance curve that is a sequence of distances between the leaf’s center and all the contour points. For matching, we developed a blade-based matching algorithm called rotation invariant - partial dynamic time warping (RI-PDTW). To speed up the matching, we suggest two additional techniques: i) priority queue-based pruning of unnecessary blade sequences for rotational invariance, and ii) lower bound-based pruning of unnecessary partial dynamic time warping (PDTW) calculations. We implemented a prototype system on the GEMINI framework [1][2]. Using experimental results, we showed that our scheme achieves excellent performance compared to competitive schemes.

• Journal of the Korea Computer Graphics Society
• /
• v.21 no.5
• /
• pp.1-10
• /
• 2015

This study proposes a method of developing authoring tool that can easily and intuitively generate diverse digital leaves that compose virtual landscape. The main system of the proposed authoring tool consists of deformation method for the contour of leaf blade based on image warping, procedural modeling of leaf vein and visualization method based on mathematical model that expresses the color and brightness of leaves. First, the proposed authoring tool receives leaf input image and searches for contour information on the leaf blades. It then designs leaf blade deformation method that can generate diverse shapes of leaf blades in an intuitive structure using feature-based image warping. Based on the computed leaf blade contour, the system implements the generalized procedural modeling method suitable for the authoring tool that generates natural vein patterns appropriate for the leaf blade shape. Finally, the system applies visualization function that can express color and brightness of leaves and their changes over time using a mathematical model based on convolution sums of divisor functions. This paper provides texture support function so that the digital leaves that were generated using the proposed authoring tool can be used in a variety of three-dimensional digital contents field.

• Journal of Biosystems Engineering
• /
• v.22 no.1
• /
• pp.30-40
• /
• 1997

A computer vision system was built to automatically grade the leaf tobacco. A color image processing algorithm was developed to extract shape, color and texture features. An improved back propagation algorithm in an artificial neural network was applied to grade the Burley type dried leaf tobacco. The success rate of grading in three-grade classification(1, 3, 5) was higher than the rate of grading in six-grade classification(1, 2, 3, 4, 5, off), on the average success rate of both the twenty-five local pixel-set and the sixteen local pixel-set. And, the average grading success rate using both shape and color features was higher than the rate using shape, color and texture features. Thus, the texture feature obtained by the spatial gray level dependence method was found not to be important in grading leaf tobacco. Grading according to the shape, color and texture features obtained by machine vision system seemed to be inadequate for replacing manual grading of Burely type dried leaf tobacco.

• International Journal of Computer Science & Network Security
• /
• v.23 no.6
• /
• pp.115-120
• /
• 2023

Plant leaf diseases and destructive insects are major challenges that affect the agriculture production of the country. Accurate and fast prediction of leaf diseases in crops could help to build-up a suitable treatment technique while considerably reducing the economic and crop losses. In this paper, Convolutional Neural Network based model is proposed to detect leaf diseases of a plant in an efficient manner. Convolutional Neural Network (CNN) is the key technique in Deep learning mainly used for object identification. This model includes an image classifier which is built using machine learning concepts. Tensor Flow runs in the backend and Python programming is used in this model. Previous methods are based on various image processing techniques which are implemented in MATLAB. These methods lack the flexibility of providing good level of accuracy. The proposed system can effectively identify different types of diseases with its ability to deal with complex scenarios from a plant's area. Predictor model is used to precise the disease and showcase the accurate problem which helps in enhancing the noble employment of the farmers. Experimental results indicate that an accuracy of around 93% can be achieved using this model on a prepared Data Set.

• International Journal of Computer Science & Network Security
• /
• v.24 no.4
• /
• pp.107-112
• /
• 2024

Plant leaf diseases and destructive insects are major challenges that affect the agriculture production of the country. Accurate and fast prediction of leaf diseases in crops could help to build-up a suitable treatment technique while considerably reducing the economic and crop losses. In this paper, Convolutional Neural Network based model is proposed to detect leaf diseases of a plant in an efficient manner. Convolutional Neural Network (CNN) is the key technique in Deep learning mainly used for object identification. This model includes an image classifier which is built using machine learning concepts. Tensor Flow runs in the backend and Python programming is used in this model. Previous methods are based on various image processing techniques which are implemented in MATLAB. These methods lack the flexibility of providing good level of accuracy. The proposed system can effectively identify different types of diseases with its ability to deal with complex scenarios from a plant's area. Predictor model is used to precise the disease and showcase the accurate problem which helps in enhancing the noble employment of the farmers. Experimental results indicate that an accuracy of around 93% can be achieved using this model on a prepared Data Set.

• Journal of Biosystems Engineering
• /
• v.25 no.4
• /
• pp.293-300
• /
• 2000

Automated greenhouse production system often require crop growth monitoring involving accurate quantification of plant physiological properties. Conventional methods are usually burdensome, inaccurate, and harmful to crops. A thermal image analysis system can accomplish rapid and accurate measurements of physiological-property changes of stressed crops. In this research a thermal imaging system was used to measure the leaf-temperature changes of several crops according to nutrient stresses. Thermal images were obtained from lettuce, cucumber, and pepper plants. Plants were placed in growth chamber to provide relatively constant growth environment. Results showed that there were significant differences in the temperature of stressed plants and non-stressed plants. In a case of the both N deficiency and excess, the leaf temperatures of cucumber were $2^{\circ}C$ lower than controlled temperature. The leaf temperature of cucumber was $2^{\circ}C$ lower than controlled temperature only when it was under N excess stress. For the potassium deficiency or excess stress, the leaf temperaures of cucumber and hot pepper were $2^{\circ}C$ lower than controls, respectively. The phosphorous deficiency stress dropped the leaf temperatures of cucumber and hot pepper $2^{\circ}C$ and $1.5^{\circ}C$ below than controls. However, the leaf temperature of lettuce did not change. It was possible to detect the changes in leaf temperature by infrared thermography when subjected to nutrition stress. Since the changes in leaf temperatures were different each other for plants and kinds of stresses, however, it is necessary to add a nutrient measurement system to a plant-growth monitoring system using thermography.