• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1996.06c
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• pp.802-811
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• 1996

Farmers need alternatives for weed control due to the desire to reduce chemicals used in farming. However, conventional mechanical cultivation cannot selectively remove weeds located in the seedline between crop plants and there are no selective heribicides for some crop/weed situations. Since hand labor is costly , an automated weed control system could be feasible. A robotic weed control system can also reduce or eliminate the need for chemicals. Currently no such system exists for removing weeds located in the seedline between crop plants. The goal of this project is to build a real-time , machine vision weed control system that can detect crop and weed locations. remove weeds and thin crop plants. In order to accomplish this objective , a real-time robotic system was developed to identify and locate outdoor plants using machine vision technology, pattern recognition techniques, knowledge-based decision theory, and robotics. The prototype weed control system is composed f a real-time computer vision system, a uniform illumination device, and a precision chemical application system. The prototype system is mounted on the UC Davis Robotic Cultivator , which finds the center of the seedline of crop plants. Field tests showed that the robotic spraying system correctly targeted simulated weeds (metal coins of 2.54 cm diameter) with an average error of 0.78 cm and the standard deviation of 0.62cm.

• Journal of Biosystems Engineering
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• v.28 no.5
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• pp.403-410
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• 2003

To enhance the performance of a rice seed pelleting machine and the quality of rice-seed pellets made, improvement of the rice seed pelleting machine developed previously(Park, 2002) was tried and its performance was evaluated. As compared with the previous pelleting machine, a feeding mechanism of pellet materials to the forming rolls was changed from screw conveyor to hydraulic cylinder for proper feeding, rings were installed among rows of semi-spherical forming grooves on the forming rolls for reducing pellet materials loss and seeds damage, and discharging air nozzles were added for complete discharging of the pellets made. Through performance tests, capacity, pelleting ratio, and seed loss ratio of the pelleting machine were investigated at the mixing ratios of soil to rice seed of 6 : 1, 7 : 1, and 8 : 1, and rotating speeds of the forming rolls of 7 rpm, 10 rpm, and 13 rpm. As results of performance evaluation, pelleting ratios were in the range of 77 ∼ 89 ％, and maximum pelleting ratio increased by 18 % in comparison with that of the previous machine. Maximum capacity was about 110 kg/h(about 63,000 pellets/h), which was increased by 70 % in comparison with that of the previous machine. But, ratios of seed loss were in the range of 24 - 49 ％, which were not improved.

• The Plant Pathology Journal
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• v.38 no.4
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• pp.395-402
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• 2022

To predict rice blast, many machine learning methods have been proposed. As the quality and quantity of input data are essential for machine learning techniques, this study develops three artificial neural network (ANN)-based rice blast prediction models by combining two ANN models, the feed-forward neural network (FFNN) and long short-term memory, with diverse input datasets, and compares their performance. The Blast_Weathe long short-term memory r_FFNN model had the highest recall score (66.3%) for rice blast prediction. This model requires two types of input data: blast occurrence data for the last 3 years and weather data (daily maximum temperature, relative humidity, and precipitation) between January and July of the prediction year. This study showed that the performance of an ANN-based disease prediction model was improved by applying suitable machine learning techniques together with the optimization of hyperparameter tuning involving input data. Moreover, we highlight the importance of the systematic collection of long-term disease data.

• Advances in nano research
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• v.14 no.3
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• pp.225-234
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• 2023

In this study, we aim to use big data resources and statistical analysis to obtain a reliable instruction to reach high-quality and high yield agricultural yields. In this regard, soil type data, raining and temperature data as well as wheat production in each year are collected for a specific region. Using statistical methodology, the acquired data was cleaned to remove incomplete and defective data. Afterwards, using several classification methods in machine learning we tried to distinguish between different factors and their influence on the final crop yields. Comparing the proposed models' prediction using statistical quantities correlation factor and mean squared error between predicted values of the crop yield and actual values the efficacy of machine learning methods is discussed. The results of the analysis show high accuracy of machine learning methods in the prediction of the crop yields. Moreover, it is indicated that the random forest (RF) classification approach provides best results among other classification methods utilized in this study.

• Journal of Biosystems Engineering
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• v.32 no.3
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• pp.179-189
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• 2007

Most of the seeds of cucurbitaceous rootstock species used for grafting were mainly sown by hand. This study was carried out to develop an on-line discriminating algorithm of seed direction using machine vision and an automatic seeding system. The seeding system was composed of a supplying device, feeding device, machine vision system, reversing device, seeding device and system control section. Machine vision was composed of a color CCD camera, frame grabber, image inspection chamber, lighting and personal computer. The seed image was segmented into a region of seed part and background part using thresholding technique in which H value of HSI color coordinate system. A seed direction was discriminated by comparing position between the center of circumscribed rectangle to a seed and the center of seed image. It took about 49ms to identify and redirect seed. Line-up status of seed was good the more than 95% of a sowed seed. Seeding capacity of this system was shown to be 10,140 grains per hour, which is three times faster than that of a typical worker.

• Agricultural and Biosystems Engineering
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• v.4 no.1
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• pp.16-21
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• 2003

In the agricultural field, a machine vision system has been widely used to automate most inspection processes especially in quality grading. Though machine vision system was very effective in quantifying geometrical quality factors, it had a deficiency in quantifying color information. This study was conducted to evaluate color of beef using machine vision system. Though measuring color of a beef using machine vision system had an advantage of covering whole lean tissue area at a time compared to a colorimeter, it revealed the problem of sensitivity depending on the system components such as types of camera, lighting conditions, and so on. The effect of color balancing control of a camera was investigated and multi-layer BP neural network based color calibration process was developed. Color calibration network model was trained using reference color patches and showed the high correlation with L*a*b* coordinates of a colorimeter. The proposed calibration process showed the successful adaptability to various measurement environments such as different types of cameras and light sources. Compared results with the proposed calibration process and MLR based calibration were also presented. Color calibration network was also successfully applied to measure the color of the beef. However, it was suggested that reflectance properties of reference materials for calibration and test materials should be considered to achieve more accurate color measurement.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1996.06c
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• pp.488-499
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• 1996

A propeller type mistblower was designed and manufactured. The machine consisted of a 770 millimeters diameter propeller driven by the power take off of a tractor. It delivered 26,400 cubic meters of air per hour and has the outlet speed about 180 kilometers per hour. Spray liquid was injected at 30 bars pressure through hollow cone type nozzles which were located around the air outlet of the machine bya poston type pump. Power consumption of the machine was found to be 12.46 kilowatts and the effective forward travel speed was about 2.7 kilometers per hour. Upon spraying mango trees, it was shown that the density of spray partices was about 100 microns and consumed 3.12 liters per tree. Working speed in 6 meters row spacing mango orchard was about one hectare per hour.

• Journal of information and communication convergence engineering
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• v.17 no.1
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• pp.8-13
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• 2019

Smart Farming has been regarded as an important application in information and communications technology (ICT) fields. Selecting crops for cultivation at the pre-production stage is critical for agricultural producers' final profits because over-production and under-production may result in uncountable losses, and it is necessary to predict crop production to prevent these losses. The ITU-T Recommendation for Smart Farming (Y.4450/Y.2238) defines plan/production consultation service at the pre-production stage; this type of service must trace crop production in a predictive way. Several research papers present that machine learning technology can be applied to predict crop production after related data are learned, but these technologies have little to do with standardized ICT services. This paper clarifies the relationship between agricultural consultation services and predicting crop production. A prediction scheme is proposed, and the results confirm the usability and superiority of machine learning for predicting crop production.

• Journal of Biosystems Engineering
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• v.27 no.5
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• pp.381-390
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• 2002

Direct seeding of rice-seed pellets is expected to be an alternative for solving problems in current direct seeding cultivation of rice. but mass production of rice-seed pellets is prerequisite for practical application. Design. construction and performance evaluation of an experimental rice seed pelleting machine were carried out for mass production of rice-seed pellets. The pelleting machine intended to make a ball type rice-seed pellet, which have 3∼5 rice seeds and diameter of which is 12 mm. Pellet materials ; rice seeds, soil, and binder were mixed and kneaded by the mixer. The designed rice seed pelleting machine fed pellet materials by screw conveyor to forming rolls and made rice-seed pellets. Capacity, ratio of perfect rice-seed pellets, seed and pellet material loss were investigated as mixing ratio of soil to rice seed and feeding rate of pellet materials. The pelleting machine showed up to 37,000 pellets/h of pelleting rate, 61∼71% of weight ratio of perfect rice-seed pellets to pellet materials supplied, 17∼48% of seed loss ratio. Average weight and average diameter of the pellets were 1.66 g and 12.0 mm. respectively. More than 3 rice seeds were included in most pellets at 6 : 1 of mixing ratio of soil to rice seed. And compression strength of the pellets was in the range of 88-130 N. To improve performance of the pelleting machine, improvements of the forming rolls, feeding mechanism, and discharging mechanism for reducing loss of pellet materials and seeds damage are needed.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• v.10 no.1
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• pp.202-205
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• 2005