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하케아스라쿨 ( Asrakul Haque ),레자나심 ( Nasim Reza ),하비네자엘리에젤 ( Eliezel Habineza ),강영호 ( Yeong Ho Kang ),이경도 ( Keong Do Lee ),정선옥 ( Sun-ok Chung ) 한국농업기계학회 2022 한국농업기계학회 학술발표논문집 Vol.27 No.2
The popularity of ground based sensors (active crop canopy sensors) is going upward for determining crop growth status and recommending additional fertilizer. By employing these sensors, Vegetation indices (VI) amplified the significance of identification in determining crop nutritional status. However, The sensor output might be continuously affected by operating and ambient factors such as movement speed and solar radiation respectively. In this study, we investigated the effects of movement speed and PAR (Photosynthetically Active Radiation) on the consistency of Crop Circle ACS-435 & DAS44X during NDVI (normalized differential vegetation index) measurement. The effects of these two parameters were evaluated on different platforms. Several movement speeds (0, 0.1, 0.2, 0.3, 0.4, and 0.5 ms-1) and PAR at different times of a sunny day with no cloud interference (10.00- 11.00, 13.00- 14.00, and 16.00- 17.00 hrs) were considered as factors to be assessed for enhancing the sensors' accuracy in plant health detection. In addition, linear regression models, Root mean square error (RMSE), and additional graphical analysis was employed to assess the effects of sensor movement speed and solar source intensity. A significant difference in speed was found in Crop-circle and DAS44X during the calculation of the NDVI. However, the difference in illumination intensity didn’t appear to have much effect on Crop-circle and DAS44X. Data acquisition has been shown to be most effective at speeds up to 0.3 ms-1 and at any time of day for both sensors in order to produce the highest output feasible. These findings illustrated the speed range certainty and daytime flexibility of Crop-Circle and DAS44X.
레자나심 ( Nasim Reza ),하케아스라쿨 ( Asrakul Haque ),카림레자울 ( Rejaul Karim ),송민호 ( Minho Song ),김국환 ( Gookhwan Kim ),정선옥 ( Sun-ok Chung ) 한국농업기계학회 2022 한국농업기계학회 학술발표논문집 Vol.27 No.2
Monitoring and preventing diseases in livestock is essential for modern farming, and an early warning system may significantly reduce the economic impact of diseased events. Manual monitoring of pigs in a pig farm is time consuming and labor intensive. Automatic monitoring for pig diseases may help to control the spread of infections. The purpose of this research was to identify the signs of sickness in pigs using acoustic monitoring in real-time. Two microphones were installed in the pig farm for automatic sound acquisition. The sound signals were converted into spectrograms by fast fourier transform (FFT) and mel-frequency cepstral coefficients (MFCC) as a characteristic parameter. Using a 2D convolutional neural network (CNN) and features extracted from the spectrogram, we presented a classification approach for real-time application. The conversion of sound inputs into spectrograms made it possible to recognize by the use of CNN. For the real-time detection, the proposed algorithm showed the ability to recognize the sounds of cough, sneezing, scream, and crushing with an overall recognition accuracy of 75.8%, 69.5%, 72.4%, and 71.6%, respectively, and an average F1-score of 81.2%. Future work is needed to enhance sound detection robustness.
잇림수마이아 ( Sumaiya Islam ),아흐메드샤리아르 ( Shahriar Ahmed ),하케아스라쿨 ( Asrakul Haque ),조연진 ( Yeon Jin Cho ),노동희 ( Dong-hee Noh ),정선옥 ( Sun-ok Chung ) 한국농업기계학회 2022 한국농업기계학회 학술발표논문집 Vol.27 No.2
Detection and management of nutritional stress in tomato seedlings is the key to growing high-yield, high-quality tomatoes. Canopy level image based plant stress monitoring may limit the stressed condition in plants. Prior to visual stress detection by human eyes, the primary goal in this study was to identify nutrient stress in tomato seedlings using image based plant feature extraction. Tomato seedlings were grown under three different levels of electrical conductivity (EC) of 0.0, 3.0, and 6.0 dS/m, with the optimum growth conditions. Images were captured of tomato seedlings and the top projected canopy area (TPCA) was calculated from the white pixels of the image, extracted from the image background. Morphological and textural parameters were collected, including homogeneity, energy, entropy, and contrast. A statistical study based on dual-segmented regression analysis was carried out to find out the stressed condition. With a confidence interval of 97.0% and a coefficient of determination (R2) of 96.7%, day 4.2 was predicted as the change point for the parameters. The method identified nutritional stress on tomato seedlings one day earlier than ocular detection. Color and texture features need further investigation to detect typical stress symptoms.
레자나심 ( Nasim Reza ),카비르사자둘 ( Sazzadul Kabir ),하케아스라쿨 ( Asrakul Haque ),정선옥 ( Sun-ok Chung ) 한국농업기계학회 2022 한국농업기계학회 학술발표논문집 Vol.27 No.1
Pig posture changes throughout the growing period are most often indicators to illness. Monitoring pigs postural movements enables us to identify morphological changes in pigs early and to detect potential risk factors for pig health. Large-scale pig farming requires extensive manual monitoring by pig farmers, which is time-consuming and laborious. Computer vision-based monitoring of posture activities over time may help to limit the spread of disease infections. The objective of this study was to recognize and detect pig posture using an masked based instance segmentation in the pig farm. Two automatic video acquisition systems were installed from top and side view, respectively. RGB images were extracted from the RGB video files and used for annotation work. Manual annotation of 200 images were prepared as training dataset, including the three postures: standing, lying, and eating from bin. An instance segmentation framework was employed to recognize and detect pig posture. A region proposal network is used in the first stage of the masked R-CNN based instance segmentation procedure. It obtains features from candidate boxes using RoIPool and conducts classification and bounding-box regression in the second step. Proposed method was evaluated using test image dataset and the experimental results showed the proposed framework obtained a F1 score of 0.911. Our work investigated a new way for recognizing and detecting pig posture in pig farm, which enables useful research into vision-based, real-time automated pig monitoring and diseases assessment.