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Research Interests 【 display / non-display 】

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Research Areas 【 display / non-display 】

Published Papers 【 display / non-display 】

Published Papers 【 display / non-display 】

• Study on Behavior of Crawler Vehicle on Rough Terrain

  Hayao ISHITSUKA, Masami MATSUI, Yuya AOYAGI

  JOURNAL of the JAPANESE SOCIETY of AGRICULTURAL MACHINERY and FOOD ENGINEERS   85 ( 5 ) 308 - 313   2023.09 [ Peer Review Accepted ]

  Type of publication: Research paper (scientific journal)

• Considerations on the Lateral Overturning of Agricultural Vehicles When Cornering and Going off the Road - Comparison of Tractor and Rice-transplanter Behavior -

  Yuya AOYAGI

  JOURNAL OF KYUSHU SOCIETY OF AGRICULTURAL MACHINERY AND FOOD ENGINEERS   ( 73 ) 14 - 20   2023.09

  Type of publication: Research paper (research society, symposium materials, etc.)

• Automatic travelling of agricultural support robot for a fruit farm -Verification of effectiveness of RTK-GNSS and developed simulator for specification design

  Ren Hiraoka, Yuya Aoyagi, Kazuki Kobayashi

  Journal of Agricultural Engineering ( PAGEPress Publications )    2023.02 [ Peer Review Accepted ]

  Type of publication: Research paper (scientific journal)

  　View Summary

  Labour shortages and fatal accidents in agricultural work have recently emerged as critical problems in Japan, necessitating productivity enhancement, workload reduction, and safety assurance. Therefore, in Japan and countries with similar agricultural environments, the use of small and inexpensive agricultural robots that can be used in mountain farms and orchards is desirable. In this study, a dynamic positioning test was performed in orchards in a mountainous region to verify the positioning accuracy and stability of the Global Navigation Satellite System (GNSS) and real time kinematic (RTK)-GNSS. In addition, a simulator for an agricultural robot that could consider the environmental information of orchards was developed, and driving tests were conducted using the GNSS data acquired in the simulation. The error of the GNSS module was set to be higher than that for the measured value, and robot travelling in the orchard was simulated. The results of GNSS positioning tests in an orchard near a mountainous area indicate that in the specific environmental conditions, the RTK-GNSS and stand-alone (SA)-GNSS can attain a positioning accuracy with an order of tens of centimetres and few metres, respectively. Moreover, the simulation results based on the GNSS positioning results indicate that a vehicle implementing RTK-GNSS and a simple obstacle detection sensor can travel autonomously in a farmyard without colliding with the tree rows. In contrast, a vehicle implementing SA-GNSS and a simple obstacle detection sensor cannot drive autonomously in an orchard and must realise self-positioning using a more accurate sensor. Therefore, the proposed approach of realising simulations of autonomous agricultural robots based on GNSS data from a real orchard can facilitate the evaluation of practical agricultural robots and confirming safety traveling root. The results demonstrate the possibility of development of small agricultural robot for orchards. We conducted the GNSS positioning test in an orchard at an altitude of approximately 830 m, and a similar performance can be expected under similar agricultural situations because the error of the GNSS module was set to be higher than the measured value in driving simulation test.

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• Evaluation of a dementia prevention program to improve health and social care and promote human rights among older adults

  Keisuke Kaneko, Fumihito Sasamori, Masao Okuhara, Suchinda Jarupat Maruo, Kazuki Ashida, Hisaaki Tabuchi, Hisaki Akasaki, Kazuki Kobayashi, Yuya Aoyagi, Noriaki Watanabe, Tomoyuki Nishino, Koji Terasawa

  International Journal of Human Rights in Healthcare ( Emerald )    2022.12 [ Peer Review Accepted ]

  Type of publication: Research paper (scientific journal)

  　View Summary

  Purpose This study aims to evaluate a human rights-informed dementia prevention program promoting better health and social care among older adults. In this study, the authors examined whether a dual-task training would improve cognition in healthy older adults. Design/methodology/approach Individuals attending the systematic health education program for older adults based in Japan were recruited for study inclusion, and divided into a dual-task training group (TG) and a control group (CG). The TG underwent 90 min of a weekly dual-task training for 12 weeks. Severity of dementia was measured using the Mini-Mental State Examination (MMSE) test. Brain function was assessed using a go/no-go task paradigm, during which cerebral blood flow was additionally measured using functional near-infrared spectroscopy to quantify oxyhemoglobin (oxy-Hb). Findings MMSE total score, number of errors in the go/no-go tasks and oxy-Hb values showed significant improvements in the TG. Research limitations/implications Owing to the small number of participants allocated to the CG, the results must be interpreted with caution. Replication and further validation based on large-scale, randomized-controlled trials is warranted. Practical implications This study highlights potential benefits of incorporating an early prevention training for dementia into a human rights-friendly health education program. Social implications This study suggests a potential means to reduce costs of social security and health care by introducing a human rights-informed dementia prevention program. Originality/value The results suggest that dual-task training may improve cognitive function in healthy older adults, thereby contributing to better health and improvement of social health care, based on a human rights-informed health education program for the prevention of dementia.

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• Real-Time Prediction of Growth Characteristics for Individual Fruits Using Deep Learning

  Takaya Hondo, Kazuki Kobayashi, Yuya Aoyagi

  Sensors ( MDPI AG )  22 ( 17 ) 6473 - 6473   2022.08 [ Peer Review Accepted ]

  Type of publication: Research paper (scientific journal)

  　View Summary

  Understanding the growth status of fruits can enable precise growth management and improve the product quality. Previous studies have rarely used deep learning to observe changes over time, and manual annotation is required to detect hidden regions of fruit. Thus, additional research is required for automatic annotation and tracking fruit changes over time. We propose a system to record the growth characteristics of individual apples in real time using Mask R-CNN. To accurately detect fruit regions hidden behind leaves and other fruits, we developed a region detection model by automatically generating 3000 composite orchard images using cropped images of leaves and fruits. The effectiveness of the proposed method was verified on a total of 1417 orchard images obtained from the monitoring system, tracking the size of fruits in the images. The mean absolute percentage error between the true value manually annotated from the images and detection value provided by the proposed method was less than 0.079, suggesting that the proposed method could extract fruit sizes in real time with high accuracy. Moreover, each prediction could capture a relative growth curve that closely matched the actual curve after approximately 150 elapsed days, even if a target fruit was partially hidden.

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Other Papers 【 display / non-display 】

Other Papers 【 display / non-display 】

• Countermeasure Development for Agricultural Vehicle Accidents Based on Accident Cases Using a New Evaluation Method - Clarification by Case Study and Simulator -

  Yuya AOYAGI

  Journal of The Agricultural Society of Japan ( The Agricultural Society of Japan )  ( 1704 ) 39 - 48   2023.06

   

Presentations 【 display / non-display 】

Presentations 【 display / non-display 】

• Analysis of Reaction Force on the Handle Section of a Walking Tractor Passing through Level-Difference of Agricultural Land

  Saki TSUKIDA, Yasumaru HIRAI, Takashi OKAYASU, Yuya AOYAGI, Muneshi MITSUOKA

  JOINT CONFERENCE ON ENVIRONMENTAL ENGINEERING IN AGRICULTURE 2023  2023.09  -  2023.09 

• Influence of Control Parameters on the Behavior in Torque Control System for Prevention Overturning -Comparison of behavior under different machine and environmental conditions-

  Yuya AOYAGI

  JOINT CONFERENCE ON ENVIRONMENTAL ENGINEERING IN AGRICULTURE 2023  2023.09  -  2023.09 

• Tractor nonlinear dynamics and social Implementation -Current status and prospects for the development of safety educational materials using tractor DS-

  Kenshi SAKAI, Nakajim MASAHIRO, Masahisa WATANABE, Tadashi Chosa, Megumi YAMASHITA, Masami MATSUI, Takahiro TAMURA, Yuya AOYAGI, Keisuke KAZAMA, Hidehiko INOUE, Hiroki, TAKIMOTO, Ichiro HARADA, Tsukasa TESHIMA

  JOINT CONFERENCE ON ENVIRONMENTAL ENGINEERING IN AGRICULTURE 2023  2023.09  -  2023.09 

• Automatic Annotated Farm Image Generation from 3D Computer Graphics for Machine Learning

  Hideto Kubo, Kazuki Kobayashi, Yuya Aoyagi

  2022 Joint 12th International Conference on Soft Computing and Intelligent Systems and 23rd International Symposium on Advanced Intelligent Systems  2022.11  -  2022.12 

• Simulation of Vibration Caused by an Automatic Transporter on Harvested Products

  Ren Hiraoka, Kazuki Kobayashi, Yuya Aoyagi

  2022 Joint 12th International Conference on Soft Computing and Intelligent Systems and 23rd International Symposium on Advanced Intelligent Systems  2022.11  -  2022.12 

Academic Awards 【 display / non-display 】

Academic Awards 【 display / non-display 】

• Research Encouragement Award

  2022.09   The Japanese Society of Agricultural Machinery and Food Engineers   Study on Prevention of Agricultural Vehicle Accident Based on Actual Case

  Winner: Yuya AOYAGI

Grant-in-Aid for Scientific Research 【 display / non-display 】

Grant-in-Aid for Scientific Research 【 display / non-display 】

• Grant-in-Aid for Scientific Research(A)

  Project Year: 2022.04  -  2026.03 

  Direct: 33,000,000 (YEN)  Overheads: 9,900,000 (YEN)  Total: 42,900,000 (YEN)

• Grant-in-Aid for Research Activity start-up

  Project Year: 2020.09  -  2022.03 

  Direct: 2,200,000 (YEN)  Overheads: 660,000 (YEN)  Total: 2,860,000 (YEN)

• Development of a driving support system for preventing overtunrning accidents of farm tractor utilizing controlling chaos

  Grant-in-Aid for Scientific Research(A)

  Project Year: 2019.04  -  2023.03 

  Direct: 35,300,000 (YEN)  Overheads: 10,590,000 (YEN)  Total: 45,890,000 (YEN)

• Grant-in-Aid for JSPS Fellows

  Project Year: 2018.04  -  2020.03 

  Direct: 1,500,000 (YEN)  Overheads: 0 (YEN)  Total: 1,500,000 (YEN)

SDGs 【 display / non-display 】

SDGs 【 display / non-display 】

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Social Activity 【 display / non-display 】

• 2024.03