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Loewen, S
Fernandez, C.J
Lavagnino, M
Li, C
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Authors
Yang, C
Odvody, G.N
Fernandez, C.J
Landivar, J.A
Nichols, R.L
Wang, C
Chen, T
Dong, J
Li, C
Loewen, S
Maxwell, B.D
Wang, Y
Lu, Y
Morris, D
Benjamin, M
Lavagnino, M
McIntyre, J
Topics
Machine Vision / Multispectral & Hyperspectral Imaging Applications to Precision Agriculture
Spatial Variability in Crop, Soil and Natural Resources
On Farm Experimentation with Site-Specific Technologies
Farm Animals Health and Welfare Monitoring
Type
Poster
Oral
Year
2012
2014
2022
2024
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1. Evaluating Spectral Measures Derived From Airborne Multispectral Imagery for Detecting Cotton Root Rot

Cotton root rot, caused by the soilborne fungus Phymatotrichopsis omnivore, is one of the most destructive plant diseases occurring... C. Yang, G.N. Odvody, C.J. Fernandez, J.A. Landivar, R.L. Nichols

2. Study Of Spatio-Temporal Variation Of Soil Nutrients In Paddy Rice Planting Farm

It is significant to analysis the spatial and temporal variation of soil nutrients for precision agriculture especially in large-scale farms. For the data size of soil nutrients grows once after sampling which mostly by the frequency of one year or months, to discover the changing trends of exact nutrient would be instructive for the fertilization in the future. In this study, theories of GIS and geostatistics were used to characterize the spatial and temporal variability of soil... C. Wang, T. Chen, J. Dong, C. Li

3. Precision Application of Seeding Rates for Weed and Nitrogen Management in Organic Grain Systems

In a time of increasing ecological awareness, organic agriculture offers sustainable solutions to many of the polluting aspects of conventional agriculture. However, without synthetic inputs, organic agriculture faces unique challenges such as weed control and fertility management. Precision Agriculture (PA) has been used to successfully increase input use efficiency in conventional systems and now offers itself as a potential tool for organic farmers as well. PA enables on farm experimentation... S. Loewen, B.D. Maxwell

4. 3D Computer Vision with a Spatial-temporal Neural Network for Lameness Detection of Sows

The lameness of sows is one of the biggest concerns for swine producers, which can lead to considerable economic losses due to reduced productivity and welfare. There is a real need for early detection of lameness in sows to enable timely intervention and minimize loss. Currently, lame detection relies on visual observation and locomotion scoring of sows, which is subjective, labor-intensive, and difficult to conduct for large groups of animals within a short time. This study presents 3D computer... Y. Wang, Y. Lu, D. Morris, M. Benjamin, M. Lavagnino, J. Mcintyre