• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1993.10a
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• pp.601-610
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• 1993

With the purpose of labour reduction in tea plucking operation with portable type machine, the influence of frame angles and tea leaves weight on the grasping forces of each finger were investigated. At the measurement of the grasping force of each finger except for thumb, grip strength dynamometers were attached at the grasping position of the frame instead of handle grips. A series of measurement was carried out changing frame angles of the tea plucking machine and the weight of tea leaves. With the obtained results of the experiments , the influences of the frame angles and the weight of the tea leaves on the grasping forces of each finger were analyzed. Some reasonable suggestions for the labour reduction in the tea plucking operation with portable type machine were obtained in the aspect of normalizing the balance of the grasping force on each finger and these suggestions are expected to contribute the labour reduction of the tea plucking operation.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1997.12a
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• pp.392-400
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• 1997

This study was conducted to develope the machine vision system of a robotic transplanter for bedding plants. Specific objectives of this study were 1) to get coordinates of the healthy seedlings except empty cells and bad seedlings in high-density plug tray, and 2) to get the angle of the leaves of the healthy seedlings to avoid damage to the seedlings by gripper. The results of this study are summarized as follows. (1) The machine vision system of a robotic transplanter was developed. (2) The success rates of detecting empty cell and bad seedlings in 72-cell and 128-cell plug trays were 98.8% and 94.9% respectively. (3) The success rates of calculating the angle of leaves in 72-cell and 128-cell plug trays were 93.5% and 91.0% respectively.

• Journal of Information Processing Systems
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• v.17 no.1
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• pp.75-88
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• 2021

Automobile credit business has developed rapidly in recent years, and corresponding default phenomena occur frequently. Credit default will bring great losses to automobile financial institutions. Therefore, the successful prediction of automobile credit default is of great significance. Firstly, the missing values are deleted, then the random forest is used for feature selection, and then the sample data are randomly grouped. Finally, six prediction models of support vector machine (SVM), random forest and k-nearest neighbor (KNN), logistic, decision tree, and artificial neural network (ANN) are constructed. The results show that these six machine learning models can be used to predict the default of automobile credit. Among these six models, the accuracy of decision tree is 0.79, which is the highest, but the comprehensive performance of SVM is the best. And random grouping can improve the efficiency of model operation to a certain extent, especially SVM.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.11 no.3
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• pp.223-229
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• 2017

This study analyzed any accidents occurred by agricultural machines and comprehended the safety for trends in the current research. The researches pertaining to the safety for agricultural machines are identified by two different methods: scrutinizing the statistical data for accidents and suggesting solutions to ways of an investigation. Other researches related to investigations for the accidents by agricultural machines are planned on the national level and the broad terms of an accident of agricultural activities. The studies are conducted for the prevention and control of accidents about the requirement analysis as well as administrative, educational and financial policy in accordance with the different kinds of the agricultural machines. In addition, for the better responses to injuries caused by the agricultural machines, more researches about the function and ability of elders are much needed.

• Journal of Biosystems Engineering
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• v.40 no.4
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• pp.417-427
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• 2015

Purpose: This study investigated different band selection methods to classify spectrally similar data - obtained from aerial images of healthy citrus canopies and citrus greening disease (Huanglongbing or HLB) infected canopies - using small differences without unmixing endmember components and therefore without the need for an endmember library. However, large number of hyperspectral bands has high redundancy which had to be reduced through band selection. The objective, therefore, was to first select the best set of bands and then detect citrus Huanglongbing infected canopies using these bands in aerial hyperspectral images. Methods: The forward feature selection algorithm (FFSA) was chosen for band selection. The selected bands were used for identifying HLB infected pixels using various classifiers such as K nearest neighbor (KNN), support vector machine (SVM), naïve Bayesian classifier (NBC), and generalized local discriminant bases (LDB). All bands were also utilized to compare results. Results: It was determined that a few well-chosen bands yielded much better results than when all bands were chosen, and brought the classification results on par with standard hyperspectral classification techniques such as spectral angle mapper (SAM) and mixture tuned matched filtering (MTMF). Median detection accuracies ranged from 66-80%, which showed great potential toward rapid detection of the disease. Conclusions: Among the methods investigated, a support vector machine classifier combined with the forward feature selection algorithm yielded the best results.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1996.06c
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• pp.852-862
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• 1996

This study was carried out to develop tools to detect defects of apple using machine vision. For the purpose, 6 kinds of frame for color images, R, G, B, h, S, and I frame, and a frame for near infra-red images (NIR frame) were tested first to select one which is useful to segment defect areas from apple images. After then, several methods to classify kind of defect for the segmented defect areas were developed and tested. Five kinds of apple defect -bruise , decay ,fleck worm hole and scar were investigated . The results are as follows: NIR frame was selected as the best one among the 7 kinds of image frame, and R, G and I frames showed favourable result to segment areas of apple defect. Various features of the segmented defect areas were measured to classify the defect areas. Eight kids of feature of the areas-size, roundness, axes length ratio, mean and variance of pixel values, variance of real part of spectrum, mean and variance of power spectrum resulted from spacial ourier transform were observed for the segmented defect areas in the selected 4 frames. then procedures to classify defects using the features were developed for the 4 frames and tested with 75-113 defects on apples. The test resulted that NIR and I frames showed high accuracies to classify the kind of defect as 77% and 76% , respectively.

• Proceedings of the Korean Society of Crop Science Conference
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• 2005.08a
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• pp.136-137
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• 2005

• Proceedings of the Korean Society of Crop Science Conference
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• 2005.08a
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• pp.134-135
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• 2005

• Journal of Biosystems Engineering
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• v.35 no.6
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• pp.443-449
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• 2010

A machine vision-based instrument to measure a droplet size spectrum of a spray nozzle was developed and tested to evaluate its accuracy on measuring spray droplet sizes and classifying nozzle sizes. The instrument consisted of a machine vision, light emitting diode (LED) illumination and a desktop computer. The illumination and machine vision were controlled by the computer through a C++ program. The program controlled the machine vision to capture droplet images under controlled illumination, and processed the droplet images to characterize the droplet size distribution of a spray nozzle. An image processing algorithm was developed to improve the accuracy of the system by eliminating random noise and out-of-focus droplets in droplet images while measuring droplet sizes. The instrument measured sizes of the three different balls (254.0, 497.8 and $793.8\;{\mu}m$) and the measurement ranges were $241.2-273.6\;{\mu}m$, $492.9-529.6\;{\mu}m$ and $800.8-824.1\;{\mu}m$ for 254.0-, 497.84- and $793.75-\;{\mu}m$ balls, respectively. Error of the measured droplet mean was less than 3.0 %. Droplet statistics, $D_{V0.1}$, $D_{V0.5}$ and $D_{V0.9}$, of a reference nozzle set were measured, and droplet size spectra of five spray nozzles covering from very fine to extremely coarse were measured to classify spray nozzle sizes. Ninety percent of the classification results of the instrument agreed with manufacturer's classification. A comparison study was carried out between developed and commercial instruments, and measurement results of the developed instrument were within 20 % of commercial instrument results.

• Proceedings of the Korean Society of Crop Science Conference
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• 2004.04a
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• pp.210-211
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• 2004