Comparison of estimating vegetation index for outdoor free-range pig production using convolutional neural networksopen accessComparison of estimating vegetation index for outdoor free-range pig production using convolutional neural networks
- Other Titles
- Comparison of estimating vegetation index for outdoor free-range pig production using convolutional neural networks
- Authors
- Sang-Hyon Oh; 박희문; 박진현
- Issue Date
- Nov-2023
- Publisher
- 한국축산학회
- Keywords
- Outdoor; Pig; Vegetation index; Image analysis; Convolutional neural network
- Citation
- 한국축산학회지, v.65, no.6, pp 1254 - 1269
- Pages
- 16
- Indexed
- SCIE
SCOPUS
KCI
- Journal Title
- 한국축산학회지
- Volume
- 65
- Number
- 6
- Start Page
- 1254
- End Page
- 1269
- URI
- https://scholarworks.gnu.ac.kr/handle/sw.gnu/68895
- DOI
- 10.5187/JAST.2023.E81
- ISSN
- 2672-0191
2055-0391
- Abstract
- This study aims to predict the change in corn share according to the grazing of 20 gestational sows in a mature corn field by taking images with a camera-equipped unmanned air vehicle (UAV). Deep learning based on convolutional neural networks (CNNs) has been verified for its performance in various areas. It has also demonstrated high recognition accuracy and detection time in agricultural applications such as pest and disease diagnosis and prediction. A large amount of data is required to train CNNs effectively. Still, since UAVs capture only a limited number of images, we propose a data augmentation method that can effectively increase data. And most occupancy prediction predicts occupancy by designing a CNN-based object detector for an image and counting the number of recognized objects or calculating the number of pixels occupied by an object. These methods require complex occupancy rate calculations; the accuracy depends on whether the object features of interest are visible in the image. However, in this study, CNN is not approached as a corn object detection and classification problem but as a function approximation and regression problem so that the occupancy rate of corn objects in an image can be represented as the CNN output. The proposed method effectively estimates occupancy for a limited number of cornfield photos, shows excellent prediction accuracy, and confirms the potential and scalability of deep learning.
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Collections - 융합기술공과대학 > Division of Mechatronics Engineering > Journal Articles
- 농업생명과학대학 > 축산과학부 > Journal Articles
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