<i>An Imaging System Based on Deep Learning for Monitoring the Feeding Behavior of Dairy Cows</i>

2019 ◽  
Author(s):  
Cheng Yu Kuan ◽  
Yu Chi Tsai ◽  
Jih Tay Hsu ◽  
Shih Torng Ding ◽  
Ta Te Lin
Keyword(s):  
Deep Learning ◽  
Feeding Behavior ◽  
Dairy Cows ◽  
Imaging System

Effect of the inoculation of sugarcane silage with and on feeding behavior and milk yield of dairy cows

2017 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
W. P. Santos ◽  
C. L. S. Ávila ◽  
M. N. Pereira ◽  
R. F. Schwan ◽  
N. M. Lopes ◽  
...  
Keyword(s):  
Feeding Behavior ◽  
Dairy Cows ◽  
Milk Yield

scholarly journals Synthetic polarization-sensitive optical coherence tomography by deep learning

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Yi Sun ◽  
Jianfeng Wang ◽  
Jindou Shi ◽  
Stephen A. Boppart
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Imaging System ◽  
Imaging Modality ◽  
Similarity Index ◽  
Polarization Sensitivity ◽  
Imaging Method ◽  
Optical Coherence ◽  
The Real ◽  
Oct Imaging

AbstractPolarization-sensitive optical coherence tomography (PS-OCT) is a high-resolution label-free optical biomedical imaging modality that is sensitive to the microstructural architecture in tissue that gives rise to form birefringence, such as collagen or muscle fibers. To enable polarization sensitivity in an OCT system, however, requires additional hardware and complexity. We developed a deep-learning method to synthesize PS-OCT images by training a generative adversarial network (GAN) on OCT intensity and PS-OCT images. The synthesis accuracy was first evaluated by the structural similarity index (SSIM) between the synthetic and real PS-OCT images. Furthermore, the effectiveness of the computational PS-OCT images was validated by separately training two image classifiers using the real and synthetic PS-OCT images for cancer/normal classification. The similar classification results of the two trained classifiers demonstrate that the predicted PS-OCT images can be potentially used interchangeably in cancer diagnosis applications. In addition, we applied the trained GAN models on OCT images collected from a separate OCT imaging system, and the synthetic PS-OCT images correlate well with the real PS-OCT image collected from the same sample sites using the PS-OCT imaging system. This computational PS-OCT imaging method has the potential to reduce the cost, complexity, and need for hardware-based PS-OCT imaging systems.

scholarly journals The Biologically Relevant Unit for the Analysis of Short-Term Feeding Behavior of Dairy Cows

2000 ◽  
Vol 83 (9) ◽  
pp. 2057-2068 ◽  
Author(s):  
B.J. Tolkamp ◽  
D.P.N. Schweitzer ◽  
I. Kyriazakis
Keyword(s):  
Feeding Behavior ◽  
Dairy Cows ◽  
Short Term ◽  
Biologically Relevant

Target recognition method based on single-pixel imaging system and deep learning in the noisy environment

2020 ◽  
Vol 49 (6) ◽  
pp. 20200010
Author(s):  
石峰 Feng Shi ◽  
陆同希 Tongxi Lu ◽  
杨书宁 Shuning Yang ◽  
苗壮 Zhuang Miao ◽  
杨晔 Ye Yang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Imaging System ◽  
Target Recognition ◽  
Noisy Environment ◽  
Recognition Method ◽  
Single Pixel

scholarly journals Effects of feeding live yeast at 2 dosages on performance and feeding behavior of dairy cows under heat stress

2020 ◽  
Vol 103 (1) ◽  
pp. 325-339
Author(s):  
M.C. Perdomo ◽  
R.S. Marsola ◽  
M.G. Favoreto ◽  
A. Adesogan ◽  
C.R. Staples ◽  
...  
Keyword(s):  
Heat Stress ◽  
Feeding Behavior ◽  
Dairy Cows ◽  
Live Yeast

scholarly journals Mango Fruit Load Estimation Using a Video Based MangoYOLO—Kalman Filter—Hungarian Algorithm Method

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2742 ◽  
Author(s):  
Wang ◽  
Walsh ◽  
Koirala
Keyword(s):  
Deep Learning ◽  
Kalman Filter ◽  
Imaging System ◽  
Tracking System ◽  
Video Tracking ◽  
Fruit Tree ◽  
Fruit Yield ◽  
Image Method ◽  
Mango Fruit ◽  
Hungarian Algorithm

: Pre-harvest fruit yield estimation is useful to guide harvesting and marketing resourcing, but machine vision estimates based on a single view from each side of the tree (“dual-view”) underestimates the fruit yield as fruit can be hidden from view. A method is proposed involving deep learning, Kalman filter, and Hungarian algorithm for on-tree mango fruit detection, tracking, and counting from 10 frame-per-second videos captured of trees from a platform moving along the inter row at 5 km/h. The deep learning based mango fruit detection algorithm, MangoYOLO, was used to detect fruit in each frame. The Hungarian algorithm was used to correlate fruit between neighbouring frames, with the improvement of enabling multiple-to-one assignment. The Kalman filter was used to predict the position of fruit in following frames, to avoid multiple counts of a single fruit that is obscured or otherwise not detected with a frame series. A “borrow” concept was added to the Kalman filter to predict fruit position when its precise prediction model was absent, by borrowing the horizontal and vertical speed from neighbouring fruit. By comparison with human count for a video with 110 frames and 192 (human count) fruit, the method produced 9.9% double counts and 7.3% missing count errors, resulting in around 2.6% over count. In another test, a video (of 1162 frames, with 42 images centred on the tree trunk) was acquired of both sides of a row of 21 trees, for which the harvest fruit count was 3286 (i.e., average of 156 fruit/tree). The trees had thick canopies, such that the proportion of fruit hidden from view from any given perspective was high. The proposed method recorded 2050 fruit (62% of harvest) with a bias corrected Root Mean Square Error (RMSE) = 18.0 fruit/tree while the dual-view image method (also using MangoYOLO) recorded 1322 fruit (40%) with a bias corrected RMSE = 21.7 fruit/tree. The video tracking system is recommended over the dual-view imaging system for mango orchard fruit count.

scholarly journals Yeast product supplementation modulated feeding behavior and metabolism in transition dairy cows

2015 ◽  
Vol 98 (1) ◽  
pp. 532-540 ◽  
Author(s):  
K. Yuan ◽  
T. Liang ◽  
M.B. Muckey ◽  
L.G.D. Mendonça ◽  
L.E. Hulbert ◽  
...  
Keyword(s):  
Feeding Behavior ◽  
Dairy Cows ◽  
Transition Dairy Cows ◽  
Yeast Product
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