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Measurement of pepper plant height and canopy area using 3D LiDAR point cloud
알리모하마드 ( Mohammod Ali ),카림레자울 ( Rejaul Karim ),카비르사자둘 ( Sazzadul Kabir ),구란다즈아스라푸자만 ( Ashrafuzzaman Gulandaz ),레자나심 ( Nasim Reza ),선저스틴 ( Justsung ),정선옥 ( Sun-ok Chung ) 한국농업기계학회 2022 한국농업기계학회 학술발표논문집 Vol.27 No.2
Plant height and canopy area are crucial plant factors for growth and yield monitoring. A significant number of plants at different heights and canopies are required to evaluate the plant phenotyping characteristics, which is labor-intensive and time-consuming. Therefore, the aim of this study was to use 3D LiDAR point clouds to assess the height and canopy area of pepper plants. A LiDAR (VLP-16) was installed in the experimental field to collect the 3D point clouds of pepper plants. The collected data was preprocessed with 3D point cloud data processing software. The automatic segmentation methods were tested on 13 pepper plants to calculate the height and canopy area. The 3D LiDAR point clouds used in the measuring method were compared to the manually gathered ground truth to determine the accuracy of the results. The average plant height and canopy area were found to be 66±4.5 m and 0.48±0.11 m2, respectively, by manual measurement, whereas the 3D point cloud data processing algorithm showed less accuracy. The R2 values were found to be more than 0.89 for the individual phenotypic traits. The results showed that the proposed system could automatically segment and measure plant height and canopy area. The findings of this study would contribute to further research for upland crop growth and yield monitoring.
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.