• Proceedings of the Korean Society of Sericultural Science Conference
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• 1997.06a
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• pp.103-117
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• 1997

Decrease in the cocoon production of Japan is drastic because of low price of cocoon, scarcity of successors and so on. To tide over the difficulty, the automation system in the sericulture was discussed and some trials have been conducted by the Ministry of Agriculture, Forestry and Fisheries of Japan. The attempts are based on a low cost artificial diet which does not rely on mulberry leaves. Automatic machines developed until now are a rearing machine constituted with repeated belt conveyor, an reformation type of former rearing machine "Bombyx" and a mounting machine. Running parallel with them, utilization of 20-hydroxyecdysone extracted from a plant to the mounting was also studied to use their machines efficiently in the fields. In conclusion, 10 tons of law cocoon will come to be produced by two persons labor. At present, an automatic rearing system on low cost artificial diet has been developing for the future sericulture.

• International journal of advanced smart convergence
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• v.8 no.4
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• pp.16-25
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• 2019

The Internet of things (IOT) is remodeling the agribusiness empowering the agriculturists through the extensive range of strategies, for example, accuracy as well as practical farming to deal with challenges in the field. The paper aims making use of evolving technology i.e. IoT and smart agriculture using automation. The objective of this research paper to present tools and best practices for understanding the role of information and communication technologies in agriculture sector, motivate and make the illiterate farmers to understand the best insights given by the big data analytics using machine learning. The methodology used in this system can monitor the humidity, moisture level and can even detect motions. According to the data received from all the sensors the water pump, cutter and sprayer get automatically activated or deactivated. we investigate a remote monitoring system using Wi-Fi. These nodes send data wirelessly to a central server, which collects the data, stores it and will allow it to be analyzed then displayed as needed and can also be sent to the client mobile.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.11b
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• pp.300-306
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• 2000

This paper introduces a new concept of automation for bio-production with tele-operative system. The proposed system showed practical and feasible way of automation for the volatile bio-production process. Based on the proposition, recognition of the job environment with object identification was performed using computer vision system. A man-machine interactive hybrid decision-making, which utilized a concept of tele-operation was proposed to overcome limitations of the capability of computer in image processing and feature extraction from the complex environment image. Identifying watermelons from the outdoor scene of the cultivation field was selected to realize the proposed concept. Identifying watermelon from the camera image of the outdoor cultivation field is very difficult because of the ambiguity among stems, leaves, shades, and especially fruits covered partly by leaves or stems. The analog signal of the outdoor image was captured and transmitted wireless to the host computer by R.F module. The localized window was formed from the outdoor image by pointing to the touch screen. And then a sequence of algorithms to identify the location and size of the watermelon was performed with the local window image. The effect of the light reflectance of fruits, stems, ground, and leaves were also investigated.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.2
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• pp.73-81
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• 2024

Recently, mobile manipulators have been utilized in agriculture industry for weed removal and harvest automation. This paper proposes a domain adaptive fruit detection method for harvest automation, by utilizing OWL-ViT model which is an open-vocabulary object detection model. The vision-language model can detect objects based on text prompt, and therefore, it can be extended to detect objects of undefined categories. In the development of deep learning models for real-world problems, constructing a large-scale labeled dataset is a time-consuming task and heavily relies on human effort. To reduce the labor-intensive workload, we utilized a large-scale public dataset as a source domain data and employed a domain adaptation method. Adversarial learning was conducted between a domain discriminator and feature extractor to reduce the gap between the distribution of feature vectors from the source domain and our target domain data. We collected a target domain dataset in a real-like environment and conducted experiments to demonstrate the effectiveness of the proposed method. In experiments, the domain adaptation method improved the AP50 metric from 38.88% to 78.59% for detecting objects within the range of 2m, and we achieved 81.7% of manipulation success rate.

• Journal of the Korean Wood Science and Technology
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• v.22 no.1
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• pp.12-19
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• 1994

An electrically heated experimental lumber dry kiln was retrofitted with a computer-based control system to control kiln conditions more precisely and monitor and record several kiln variables. Flat-sawn 2.5cm-thick Pinus densiflora boards were dried in constant temperature process(65$^{\circ}C$ & 50~60 %RH) and continuously rising temperature process, respectively. The average drying rate in continuously rising temperature process was 5.7 %/hr, which was above 3 times faster than that in constant temperature process. But, the average rate of case-hardening and moisture difference between shells and cores of boards dried in continuously rising temperature process were 82 % and 5.5 %, respectively, which were much larger than those of boards dried in constant temperature process.

• Advances in robotics research
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• v.2 no.3
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• pp.201-217
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• 2018

Agriculture production is a critical human intensive task, which takes place in all regions of the world. The process to grow and harvest crops is labor intensive in many countries due to the lack of automation and advanced technology. Much of the difficult, dangerous and dirty labor of crop production can be automated with intelligent and robotic platforms. We propose an intelligent, agent-oriented robotic team, which can enable the process of harvesting, gathering and collecting crops and fruits, of many types, from agricultural fields. This paper describes a novel robotic organization enabling humans, robots and agents to work together for automation of gathering and collection functions. The focus of the research is a model, called HARMS, which can enable Humans, software Agents, Robots, Machines and Sensors to work together indistinguishably. With this model, any capability-based human-like organization can be conceived and modeled, such as in manufacturing or agriculture. In this research, we model, design and implement a technology application of knowledge-based robot-to-robot and human-to-robot collaboration for an agricultural gathering and collection function. The gathering and collection functions were chosen as they are some of the most labor intensive and least automated processes in the process acquisition of agricultural products. The use of robotic organizations can reduce human labor and increase efficiency allowing people to focus on higher level tasks and minimizing the backbreaking tasks of agricultural production in the future. In this work, the HARMS model was applied to three different robotic instances and an integrated test was completed with satisfactory results that show the basic promise of this research.

• KIISE Transactions on Computing Practices
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• v.21 no.11
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• pp.689-698
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• 2015

Currently, the itemized box loading operation at the Agriculture Products Processing Center which distributes agricultural products for the region is carried out manually. The process of loading agricultural products requires great manpower and had been resolved through the part-time employment of the residents of farm villages. However, recently it has become quite difficult to secure manpower as the aging within the rural community has been intensified. Hence, the necessity for countermeasures such as facility automation or use of robots have become necessary. This study suggests an automatic robot system for the itemized loading of apple boxes at the Agriculture Products Processing Center. The suggested method is to design and develop equipment such as a conveyer, robot, and bar code reader. In addition, a sorting plan, operational control, generation of control information, and software module that could monitor the inside of the Agriculture Products Processing Center is needed. After test-operating and evaluating the developed system, the existing manual work would be replaced with the automated robot system in order to enhance work efficiency and resolve safety issues.

• Journal of IKEEE
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• v.22 no.4
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• pp.959-964
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• 2018

In this study, a study is conducted on a monitoring system that can control the environment of plants using sensors in conjunction with the LED light system and the plant growth control system. To verify the performance of the developed plant growth system, an experiment was conducted on the characteristics of energy efficiency, data transmission rate, and light volume control. The experiment resulted in a satisfactory result by controlling more than 80% energy efficiency, 1Mb/sec wireless communication speed, and 5 levels of optical control. The proposed system can be applied to LED plant facilities and will contribute to the automation of agriculture by organizing an automated system for production efficiency and labor cost reduction for future commercialization.

• Korean Journal of Environmental Agriculture
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• v.35 no.1
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• pp.46-54
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• 2016

BACKGROUND: The tobacco whitefly(Bemisia tabaci Gennadius) and western flower thrips(Frankliniella occidentalis Pergande) seriously damaged to several greenhouse crops and transmitted plant viruses such as the Tomato Yellow Leaf Curl Virus(TYLCV) and Tomato Spotted Wilt Virus(TSWV). Objective of this study was to elucidate exclusion effects of insect screen nets by various hole sizes and colors for control of the two insect pests in controlled environments such as a closed plant production system.METHODS AND RESULTS: The exclusion effects to various hole sizes of three other colors with 30 individuals of two insect pests was evaluated. B. tabaci was not showed not difference to different colors and sizes. F. occidentalis showed that 0.2 mm black screen was the most effective exclusion than other colors of 0.6 and 0.8 mm.CONCLUSION: The two insects were different reponses to various hole sizes of white and other color screen nets. It was proved that the 0.4 mm white screen net used in this experimental condition was suitable for exclusion of B. tabaci and 0.2 mm black forF. occidentalis.

• Food Science of Animal Resources
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• v.43 no.6
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• pp.1150-1169
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• 2023

Edible insects are gaining popularity as a potential future food source because of their high protein content and efficient use of space. Black soldier fly larvae (BSFL) are noteworthy because they can be used as feed for various animals including reptiles, dogs, fish, chickens, and pigs. However, if the edible insect industry is to advance, we should use automation to reduce labor and increase production. Consequently, there is a growing demand for sensing technologies that can automate the evaluation of insect quality. This study used short-wave infrared (SWIR) hyperspectral imaging to predict the proximate composition of dried BSFL, including moisture, crude protein, crude fat, crude fiber, and crude ash content. The larvae were dried at various temperatures and times, and images were captured using an SWIR camera. A partial least-squares regression (PLSR) model was developed to predict the proximate content. The SWIR-based hyperspectral camera accurately predicted the proximate composition of BSFL from the best preprocessing model; moisture, crude protein, crude fat, crude fiber, and crude ash content were predicted with high accuracy, with R² values of 0.89 or more, and root mean square error of prediction values were within 2%. Among preprocessing methods, mean normalization and max normalization methods were effective in proximate prediction models. Therefore, SWIR-based hyperspectral cameras can be used to create automated quality management systems for BSFL.