Comparison of estimating vegetation index for outdoor free-range pig production using convolutional neural networks
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초록
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.
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.
참고문헌 (21)
- Szyndler-Nedza M, "The production system of high quality pork products – an example", Ann Warsaw Univ Life Sci SGGW Anim Sci, 58, pp.181~198 (2019)[DOI]
- 오상현, "Estimating vegetation index for outdoor free-range pig production using YOLO", 한국축산학회지, 65(3), pp.638~651 (2023), 한국축산학회
- 오상현, "Estimating vegetation index for outdoor freerange pig production", Korean Journal of Agricultural Science, 50(1), pp.141~153 (2023), 농업과학연구소[DOI]
- Voulodimos A, "Recent developments in deep learning for engineering applications", Comput Intell Neurosci, 2018, pp.8141259~ (2018)[DOI]
- Krizhevsky A, "ImageNet classification with deep convolutional neural networks", Commun ACM, 60, pp.84~90 (2017)[DOI]
- Zou Z, "Object detection in 20 years : a survey", Proc IEEE, 111, pp.257~276 (2023)
- "Very deep convolutional networks for large-scale image recognition" (2015)[URL]
- Alibabaei K, "A review of the challenges of using deep learning algorithms to support decision-making in agricultural activities", Remote Sens, 14, pp.638~ (2022)[DOI]
- Priyadharshini RA, "Maize leaf disease classification using deep convolutional neural networks", Neural Comput Appl, 31, pp.8887~8895 (2019)[DOI]
- Koirala A, "Deep learning for real-time fruit detection and orchard fruit load estimation : benchmarking of ‘MangoYOLO’", Precis Agric, 20, pp.1107~1135 (2019)[DOI]
- Fu L, "Fast and accurate detection of banana fruits in complex background orchards", IEEE Access, 8, pp.196835~196846 (2020)[DOI]
- Kitano BT, "Corn plant counting using deep learning and UAV images", IEEE Geosci Remote Sens Lett, pp.293054~ (2019)[DOI]
- Mota-Delfin C, "Detection and counting of corn plants in the presence of weeds with convolutional neural networks", Remote Sens, 14, pp.4892~ (2022)[DOI]
- Shorten C, "A survey on image data augmentation for deep learning", J Big Data, 6, pp.60~ (2019)[DOI]
- Taylor L, "Improving deep learning with generic data augmentation", 2018IEEE Symposium Series on Computational Intelligence (SSCI), pp.1542~1547 (2018)
- Lecun Y, "Gradient-based learning applied to document recognition", Proceedings of the IEEE, 86, pp.2278~2324 (1998)[DOI]
- Wang X, "Improving bag-of-deep-visual-words model via combining deep features with feature difference vectors", IEEE Access, 10, pp.35824~35834 (2022)[DOI]
- Scaramuzza D, "A flexible technique for accurate omnidirectional camera calibration and structure from motion", Fourth IEEE International Conference on Computer Vision Systems (ICVS’06), pp.45~ (2006)
- Szegedy C, "Going deeper with convolutions", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.1~5 (2015)
- He K, "Deep residual learning for image recognition", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.770~778 (2016)