Proceedings

Find matching any: Reset
Anup, A
Paccioretti, P
Add filter to result:
Authors
Cesario Pinto, J
Thompson, L
Mueller, N
Mieno, T
Puntel, L
Paccioretti, P
Balboa, G
Balboa, G
Puntel, L
Thompson, L
Paccioretti, P
Waltz, L
Khanal, S
Katari, S
Hong, C
Anup, A
Colbert, J
Potlapally, A
Dill, T
Porter, C
Engle, J
Stewart, C
Subramoni, H
Machiraju, R
Ortez, O
Lindsey, L
Nandi, A
Topics
On Farm Experimentation with Site-Specific Technologies
Drivers and Barriers to Adoption of Precision Ag Technologies or Digital Agriculture
Artificial Intelligence (AI) in Agriculture
Type
Oral
Year
2024
Home » Authors » Results

Authors

Filter results3 paper(s) found.

1. Site-specific Evaluation of Sensor-based Winter Wheat Nitrogen Tools Via On-farm Research

Crop producers face the challenge of optimizing high yields and nitrogen use efficiency (NUE) in their agricultural practices. Enhancing NUE has been demonstrated by adopting digital agricultural technologies for site-specific nitrogen (N) management, such as remote-sensing based N recommendations for winter wheat. However, winter wheat fields are often uniformly fertilized, disregarding the inherent variability within the fields. Thus, an on-farm evaluation of sensor-based N tools is needed to... J. Cesario pinto, L. Thompson, N. Mueller, T. Mieno, L. Puntel, P. Paccioretti, G. Balboa

2. Barriers and Adoption of Precision Ag Tehcnologies for Nitrogen Management Nebraska

A statewide survey of Nebraska farmers shows that they determine the N rate based on soil lab recommendations (82%),  intuition, traditional rate, and own experience (67%). The adoption of dynamic site-specific models (23%), and sensor-based algorithms (11%) remains low. The survey identified the main barriers to the adoption of these N management technologies.  ... G. Balboa, L. Puntel, L. Thompson, P. Paccioretti

3. Cyberinfrastructure for Machine Learning Applications in Agriculture: Experiences, Analysis, and Vision

Advancements in machine learning algorithms and GPU computational speeds over the last decade have led to remarkable progress in the capabilities of machine learning. This progress has been so much that, in many domains, including agriculture, access to sufficiently diverse and high-quality datasets has become a limiting factor.  While many agricultural use cases appear feasible with current compute resources and machine learning algorithms, the lack of software infrastructure for collecting,... L. Waltz, S. Khanal, S. Katari, C. Hong, A. Anup, J. Colbert, A. Potlapally, T. Dill, C. Porter, J. Engle, C. Stewart, H. Subramoni, R. Machiraju, O. Ortez, L. Lindsey, A. Nandi