Proceedings :: International Society of Precision Agriculture

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Farm Animals Health and Welfare Monitoring

ISPA Community: Nitrogen

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Authors

Bean, G.M

Camberato, J.J

Carter, P.R

Dufrasne, I

Ferguson, R.B

Fernández, F.G

Franzen, D.W

Kitchen, N.R

Laboski, C.A

Lebeau, F

Li, D

Mahmoudi, S

Miao, Y

Nafziger, E.D

Plum, J

Quoitin, B

Ransom, C.J

Sawyer, J.E

Shanahan, J.F

Xu, Z

Zhou, J

Topics

ISPA Community: Nitrogen

Farm Animals Health and Welfare Monitoring

Type

Oral

Year

2022

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Filter results3 paper(s) found.
1. Detect Estrus in Sows Using a Lidar Sensor and Machine Learning Accurate estrus detection of sows is labor intensive and is crucial to achieve high farrowing rate. This study aims to develop a method to detect accurate estrus time by monitoring the change in vulvar swollenness around estrus using a light detection and ranging (LiDAR) camera. The measurement accuracy of the LiDAR camera was evaluated in laboratory conditions before it was used in monitoring sows in a swine research facility. In this study, twelve multiparous individually housed sows were c... J. Zhou, Z. Xu
2. Use of Watering Hole Data As a Decision Support Tool for the Management of a Grazing Herd of Cattle Establish grazing practices would improve the welfare of the animals, allowing them to express more natural behaviours. However, free-range reduces the ability to monitor the animals, thus increase the time needed to intervene in the event of a health problem. To ease the adoption of grazing, farmer would benefit from autonomously collected indicators at pasture that identify abnormal behaviours possibly related to a health problem in a bovine. These indicators must be individualised and coll... J. Plum, B. Quoitin, I. Dufrasne, S. Mahmoudi, F. Lebeau Download paper
3. Developing a Machine Learning and Proximal Sensing-based In-season Site-specific Nitrogen Management Strategy for Corn in the US Midwest Effective in-season site-specific nitrogen (N) management strategies are urgently needed to ensure both food security and sustainable agricultural development. Different active canopy sensor-based precision N management strategies have been developed and evaluated in different parts of the world. Recent studies evaluating several sensor-based N recommendation algorithms across the US Midwest indicated that these locally developed algorithms generally did not perform well when used broadly acr... D. Li, Y. Miao, .G. Fernández, N.R. Kitchen, C. . Ransom, G.M. Bean, .E. Sawyer, J.J. Camberato, .R. Carter, R.B. Ferguson, D.W. Franzen, D.W. Franzen, D.W. Franzen, D.W. Franzen, C.A. Laboski, E.D. Nafziger, J.F. Shanahan Download paper

Filter results3 paper(s) found.

1. Detect Estrus in Sows Using a Lidar Sensor and Machine Learning

Accurate estrus detection of sows is labor intensive and is crucial to achieve high farrowing rate. This study aims to develop a method to detect accurate estrus time by monitoring the change in vulvar swollenness around estrus using a light detection and ranging (LiDAR) camera. The measurement accuracy of the LiDAR camera was evaluated in laboratory conditions before it was used in monitoring sows in a swine research facility. In this study, twelve multiparous individually housed sows were c... J. Zhou, Z. Xu

2. Use of Watering Hole Data As a Decision Support Tool for the Management of a Grazing Herd of Cattle

Establish grazing practices would improve the welfare of the animals, allowing them to express more natural behaviours. However, free-range reduces the ability to monitor the animals, thus increase the time needed to intervene in the event of a health problem. To ease the adoption of grazing, farmer would benefit from autonomously collected indicators at pasture that identify abnormal behaviours possibly related to a health problem in a bovine. These indicators must be individualised and coll... J. Plum, B. Quoitin, I. Dufrasne, S. Mahmoudi, F. Lebeau

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3. Developing a Machine Learning and Proximal Sensing-based In-season Site-specific Nitrogen Management Strategy for Corn in the US Midwest

Effective in-season site-specific nitrogen (N) management strategies are urgently needed to ensure both food security and sustainable agricultural development. Different active canopy sensor-based precision N management strategies have been developed and evaluated in different parts of the world. Recent studies evaluating several sensor-based N recommendation algorithms across the US Midwest indicated that these locally developed algorithms generally did not perform well when used broadly acr... D. Li, Y. Miao, .G. Fernández, N.R. Kitchen, C. . Ransom, G.M. Bean, .E. Sawyer, J.J. Camberato, .R. Carter, R.B. Ferguson, D.W. Franzen, D.W. Franzen, D.W. Franzen, D.W. Franzen, C.A. Laboski, E.D. Nafziger, J.F. Shanahan

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