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RISS 인기검색어
- 검색키워드
- 저자 : 키라가샤피크 ( Shafik Kiraga ) (검색결과 4 건)
- 무료
- 기관 내 무료
- 유료
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Potential of impact-based mass estimation of individual radish tubers for real-time yield monitoring
키라가샤피크 ( Shafik Kiraga ),구란다즈아스라푸자만 ( Ashraffuzaman Gulandaz ),카비르사자둘 ( Kabir Sazzadul ),레자나심 ( Nasim Reza ),정선옥 ( Sun-ok Chung ) 한국농업기계학회 2022 한국농업기계학회 학술발표논문집 Vol.27 No.1
Yield monitoring provides information on the spatial variability of yield in the field and it is one of the basic components of precision agriculture. The objective of this study was to investigate the effects of different harvesting conditions on radish mass measurements using a double load cell impact plate. The harvesting conditions included the falling height, conveyor speed, and impact plate angle, which were simulated using an impact plate attached to a laboratory test bench. The relative error (RE), standard error (SE), and the coefficient of determination (R2) were the statistical indicators used to describe the accuracy of the estimates. Analysis of variance (ANOVA) without interaction of factors and the Duncan multiple range tests were performed using the above indicators except R2. The falling height and conveyor speed had no significant effect on radish mass measurement. In contrast, the impact plate angle significantly affected the impact plate precision. Minimum and maximum standard error of 1.68 and 4.39 were obtained at -100, 40 cm, 0.05 m/s and -500, 30 cm, 0.25 m/s, respectively. The results showed the possibility of using impact-based sensors for individual measurement of radish for real-time yield monitoring.
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자주식 양파정식기 슬라이딩 메쉬형 기어박스의 동력전달 해석
이슬람나피울 ( Md Nafiul Islam ),알리모하마드 ( Mohammod Ali ),키라가샤피크 ( Shafik Kiraga ),초두리밀론 ( Milon Chowdhury ),권행주 ( Haing-ju Kwon ),정선옥 ( Sun-ok Chung ) 한국농업기계학회 2021 한국농업기계학회 학술발표논문집 Vol.26 No.1
An appropriate gearbox selection is essential to avoid transmission losses and convey the engine power to the transplanter components efficiently. Therefore, the objective of this research was to simulate the power transmission of a self-propelled onion transplanter gearbox for calculating the power loss and efficiency. The automatic transplanter power transmission scheme consists of wheels, and dibbling mechanism, and picking mechanism. A computer-aided gear efficiency calculation software package was used to develop a three-dimensional simulation model for the automatic onion transplanter. A V-belt with pulley and nine gear stages sliding mesh type gearbox were used to transmit power from engine to wheel and other transplanter components. The last two gear sets were used as dibbling and picking mechanism gear shafts, respectively. The transmission load was measured at the input shaft of the gearbox, and the driving axle load was measured at the final drive shaft. The load measurements were made at three-speed levels. The input power of the gearbox was 1.7 kW, and the last stage of power (picking scheme) was found as 0.8 kW. The overall efficiency of this gearbox was found as 83.39%. The outcomes of the research would provide a significant reference for the development a power transmission scheme for efficient automatic onion transplanting.
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Basic performance test for sound detection and remote monitoring in Pig Farm
레자나심 ( Nasim Reza ),초두리밀론 ( Milon Chowdhury ),키라가샤피크 ( Shafik Kiraga ),정선옥 ( Sun-ok Chung ) 한국농업기계학회 2021 한국농업기계학회 학술발표논문집 Vol.26 No.2
Precision livestock farming is an intelligent technology, which allows the closer monitoring of each animal on farms. Sound based precision farming provides considerable benefits compared to other technology, such as imaging sensor, motion sensors, etc. In addition, sound sensors are inexpensive, no direct contact, and a huge number of animal can be observed using a single sensor. The objective of this study was to investigate a remotely monitored sound detection and imaging system in pig farm for early detection of respiratory diseases. Three microphones and three RGB cameras with three micro-controller were used to receive the sound and image data in the pig farm. Total 30 pigs were covered by our surveillance system. A sound analysis algorithm was developed to record the sounds received by the microphones and distinguished the pig sounds from the outside noises. The sound was then processed by the algorithm to detect the abnormal sound of pigs. The images were synchronized and used to monitor the unwanted movement and behaviour. High, medium, and low frequency sounds were detected. The results showed that the detection efficiency for high frequency sound was around 85%, and for low frequency sound was 73%. Moreover, movement of pigs were also monitored by images. From this study, it would be feasible to recognize early respiratory illness in pigs through automated and sequential monitoring of sounds and images within the pig farm.
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Image based algorithm for growth prediction of pennywort plant grown in a plant factory
이슬람수마이야 ( Sumaiya Islam ),레자나심 ( Nasim Reza ),초두리밀론 ( Milon Chowdhury ),키라가샤피크 ( Shafik Kiraga ),정선옥 ( Sun-ok Chung ) 한국농업기계학회 2021 한국농업기계학회 학술발표논문집 Vol.26 No.2
Plant growth prediction typically relies on the estimation of changes in plant structure and size. The leaf is one of the visual structures of plants, which has a significant impact on growth. The objective of this study was to predict pennywort plant growth using an image processing algorithm. Pennywort plant was grown in the plant factory, where ambient environmental variables were maintained precisely. The experiment was carried out for four weeks. RGB images of the plant were captured by a digital camera from the top of the plants everyday. In the image processing algorithm, the images were converted to grayscale and then binary masking was applied to classify each pixel as belonging to the region of interest. The masked images were segmented from the background. Then the region filling technique was applied to fill out the leaf region. We calculated the total pixel number in the image leaf area and calculated the leaf area using reference object. Actual plant leaf area was also continuously measured by a leaf area meter with specific time intervals without hindering plant growth. Our proposed algorithm demonstrated a high correlation of 0.954 between the actual and image-based leaf area measurements. A linear regression curve was found and growth was predicted using the desired cultivation period on the regression equation. Growth prediction model showed the potentiality to estimate plant growth cultivated in controlled environment.
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