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ISPA Community: Nitrogen

Smart Weather for Precision Agriculture

Plenary

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Authors

Bean, G.M

Camberato, J.J

Carter, P.R

Dash, M

Ferguson, R.B

Fernández, F.G

Franzen, D.W

Jalem, R.S

Kitchen, N.R

Laboski, C.A

Li, D

Miao, Y

Nafziger, E.D

Ransom, C.J

Sawyer, J.E

Shanahan, J.F

Sharma, A

Sparrow, R

Topics

ISPA Community: Nitrogen

Smart Weather for Precision Agriculture

Plenary

Type

Oral

Year

2022

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Filter results3 paper(s) found.
1. Micro-climate Prediction System Using IoT Data and AutoML Microclimate variables like temperature, humidity are sensitive to land surface properties and land-atmosphere connections. They can vary over short distances and even between sections of the farm. Getting the accurate microclimate around the crop canopy allows farmers to effectively manage crop growth. However, most of the weather forecast services available to farmers globally, either by the meteorological department or universities or some weather app, provide weather forecasts for l... A. Sharma, R.S. Jalem, M. Dash Download paper
2. Realising the Potential of Agricultural Robotics and AI: The Ethical Challenges Recent advances in AI and robotics may dramatically transform agriculture by greatly expanding the number of contexts in which the techniques of precision agriculture may be applied. Inevitably, this next agricultural revolution will generate profound ethical issues: opportunities as well as risks. Clever applications of AI and robotics may allow agriculture to be more sustainable by facilitating more precise applications of water, fertilisers, and herbicides. Robots may take some of the drud... R. Sparrow
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. Micro-climate Prediction System Using IoT Data and AutoML

Microclimate variables like temperature, humidity are sensitive to land surface properties and land-atmosphere connections. They can vary over short distances and even between sections of the farm. Getting the accurate microclimate around the crop canopy allows farmers to effectively manage crop growth. However, most of the weather forecast services available to farmers globally, either by the meteorological department or universities or some weather app, provide weather forecasts for l... A. Sharma, R.S. Jalem, M. Dash

Download paper

2. Realising the Potential of Agricultural Robotics and AI: The Ethical Challenges

Recent advances in AI and robotics may dramatically transform agriculture by greatly expanding the number of contexts in which the techniques of precision agriculture may be applied. Inevitably, this next agricultural revolution will generate profound ethical issues: opportunities as well as risks. Clever applications of AI and robotics may allow agriculture to be more sustainable by facilitating more precise applications of water, fertilisers, and herbicides. Robots may take some of the drud... R. Sparrow

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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