Purpose: Damage to pulse crops by wild birds is a serious problem. The damage is to such an extent that the rate of damage during the period between seeding and cotyledon stages reaches 54.6% on an average. In this study, a crop-position detection method was developed wherein infrared (IR) sensors w...
Purpose: Damage to pulse crops by wild birds is a serious problem. The damage is to such an extent that the rate of damage during the period between seeding and cotyledon stages reaches 54.6% on an average. In this study, a crop-position detection method was developed wherein infrared (IR) sensors were used to determine the cotyledon position under a vinyl mulch. Methods: IR sensors that helped measure the temperature were used to locate the cotyledons below the vinyl mulch. A single IR sensor module was installed at three locations of the crops (peanut, red lettuce, and crown daisy) in the cotyledon stage. The representative thermal response of a $16{\times}4$ pixel area was detected using this sensor in the case where the distance from the target was 25 cm. A spatial image was applied to the two-dimensional temperature distribution using a non-integral moving-average method. The collected data were first processed by taking the moving average via interpolation to determine the frame where the variance was the lowest for a resolution unit of 1.02 cm. Results: The temperature distribution was plotted corresponding to a distance of 10 cm between the crops. A clear leaf pattern of the crop was visually confirmed. However, the temperature distribution after the normalization was unclear. The image conversion and frequency-conversion graphs were obtained based on the moving average by averaging the points corresponding to a frequency of 40 Hz for 8 pixels. The most optimized resolutions at locations 1, 2, and 3 were found on 3.4, 4.1, and 5.6 Pixels, respectively. Conclusions: In this study, to solve the problem of damage caused by birds to crops in the cotyledon stage after seeding, the vinyl mulch is punched after seeding. The crops in the cotyledon stage could be accurately located using the proposed method. By conducting the experiments using the single IR sensor and a sliding mechanical device with the help of a non-integral interpolation method, the crops in the cotyledon stage could be precisely located.
Purpose: Damage to pulse crops by wild birds is a serious problem. The damage is to such an extent that the rate of damage during the period between seeding and cotyledon stages reaches 54.6% on an average. In this study, a crop-position detection method was developed wherein infrared (IR) sensors were used to determine the cotyledon position under a vinyl mulch. Methods: IR sensors that helped measure the temperature were used to locate the cotyledons below the vinyl mulch. A single IR sensor module was installed at three locations of the crops (peanut, red lettuce, and crown daisy) in the cotyledon stage. The representative thermal response of a $16{\times}4$ pixel area was detected using this sensor in the case where the distance from the target was 25 cm. A spatial image was applied to the two-dimensional temperature distribution using a non-integral moving-average method. The collected data were first processed by taking the moving average via interpolation to determine the frame where the variance was the lowest for a resolution unit of 1.02 cm. Results: The temperature distribution was plotted corresponding to a distance of 10 cm between the crops. A clear leaf pattern of the crop was visually confirmed. However, the temperature distribution after the normalization was unclear. The image conversion and frequency-conversion graphs were obtained based on the moving average by averaging the points corresponding to a frequency of 40 Hz for 8 pixels. The most optimized resolutions at locations 1, 2, and 3 were found on 3.4, 4.1, and 5.6 Pixels, respectively. Conclusions: In this study, to solve the problem of damage caused by birds to crops in the cotyledon stage after seeding, the vinyl mulch is punched after seeding. The crops in the cotyledon stage could be accurately located using the proposed method. By conducting the experiments using the single IR sensor and a sliding mechanical device with the help of a non-integral interpolation method, the crops in the cotyledon stage could be precisely located.
* AI 자동 식별 결과로 적합하지 않은 문장이 있을 수 있으니, 이용에 유의하시기 바랍니다.
제안 방법
In addition, among the various types of polynomial interpolation methods, the spline method is relatively faster because it has a lower maximum order of formulation when using a system such as the tridiagonal linear equation system, which provided the capability of real-time processing of the measurement system in this study. The frequency was analyzed to determine the optimal correction period based on the number of overlapping pixels.
In the study, an empirical experiment was conducted by designing and producing the IR sensor under a vinyl mulch. Moreover, using the images obtained from the experiments, the spatial error correction of the moving measurement system was performed using a non-integral interpolation method.
(2016) used multiple sensors with a constructed system in the form of a linear array. In this study, DTPAL-UART-1604 (Diwell Electronics Co., Ltd., South Korea) was used as a single sensor module to detect the locations of the crops in the cotyledon stage. The DTPALUART-1604 had a resolution of 0.
In this study, to minimize the damage due to vermin, protect the cultivation against weeds, and maintain the water content in the soil, a vinyl-mulching technique is proposed immediately after the seeding stage. The vinyl-mulching technique applied after seeding can be fundamentally blocked by vermin, thus punching is essential to satisfactorily grow the crops.
The defects in the olives, which cannot be easily distinguished from the visible images, were distinguished using the IR images. The IR image was combined along with the visible image, and the degree of damage inflicted to the olives was determined using an edge-detection technique to classify the quality. The edge-detection technique has been used in the field of image processing, particularly to extract the features of objects.
, China). The experiments were conducted by traveling the IR sensor at a speed of 4 cm/s by changing the rotational speed of the stepping motor based on a digital pulse width modulation (PWM) signal obtained from the micro-controller.
Therefore, in this study, a crop-position detection method was studied wherein IR sensors are applied to determine the cotyledon position under the vinyl mulch.
Although the sizes and shapes of the crops are slightly different, the cotyledons are visually identifiable. Therefore, the experiments were conducted under the conditions where the crops were covered by vinyl mulch after the crops reached a visually identifiable state immediately after seeding. To grow these crops, nine mulches with an area of 100 cm × 25 cm were covered on the culture soil in a greenhouse to maintain the same growth conditions (Figure 1(d)).
대상 데이터
Peanuts, red lettuce, and crown daisy were selected to conduct the experiments. Figures 1(a), 1(b), and 1(c) show the peanuts, red lettuce, and crown daisy, respectively, in the cotyledon stage after seeding.
이론/모형
In the study, an empirical experiment was conducted by designing and producing the IR sensor under a vinyl mulch. Moreover, using the images obtained from the experiments, the spatial error correction of the moving measurement system was performed using a non-integral interpolation method. The conclusions of the study are as follows.
성능/효과
(3) The image and frequency conversion graphs based on the moving-average size was obtained by averaging the points corresponding to a frequency of 40 Hz for 8 pixels.
참고문헌 (9)
Ceccardi T. L., R. L. Heath and I. P. Ting. 1995. Lowtemperature exotherm measurement using infrared thermography. Hortscience 30(1): 140-142.
Guzman E., V. Baeten, J. A. F. Pierna and J. A. Garcia-Mesa. 2013. Infrared machine vision system for the automatic detection of olive fruit quality. Talanta. 116: 984-898.
Jeon H. Y., H. Zhu, R. Derksen, E. Ozkan and C. Krause. 2011. Evaluation of ultrasonic sensor for variable-rate spray applications. Computers and Electronics in Agriculture 75: 213-221.
Jeong B. J. and S. W. Jang. 2009. Image processing using thermal infrared image. Journal of the Korea Academia-Industrial cooperation Society 10(7): 1503-1508.
Lee K. S, Y. J. Cho and D. H. Lee. 2016. Detection method for bean cotyledon locations under vinyl mulch using multiple infrared sensors. Journal of biosystem engineering, 41(3): 263-272.
Lim S. K. 2009. Seedling emergence rates and the degrees of damage after soybean seeding by time of eating damage by pigeons and pheasants. National Institute of Crop Science (In Korean).
Kim G. Y., K. H. Ryu and H. Y. Chae. 1999. Measurement of stress related crop temperature variations. Journal of Bio-Environment Control 8(2): 233-236 (In Korean, with English abstract).
Viola, P. and M. Jones. 2001. Rapid object detection using a boosted cascade of simple features. In Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on (Vol. 1, pp. I-I). IEEE.
Yuan, F. 2008. A fast accumulative motion orientation model based on integral image for video smoke detection. Pattern Recognition Letters 29(7): 925-932.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.