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Cisdeli Magalhães, P
Cesario Pinto, J
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Authors
Cesario Pinto, J
Thompson, L
Mueller, N
Mieno, T
Puntel, L
Paccioretti, P
Balboa, G
Nocera Santiago, G.N
Cisdeli Magalhães, P
Ciampitti, I
Marziotte, L
CARCEDO, A
Topics
On Farm Experimentation with Site-Specific Technologies
Artificial Intelligence (AI) in Agriculture
Type
Oral
Year
2024
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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. Algorithm to Estimate Sorghum Grain Number from Panicles Using Images Collected with a Smartphone at Field-scale

An estimation of on-farm yield before harvest is important to assist farmers on deciding additional input use, time to harvest, and options for end uses of the harvestable product. However, obtaining a rapid assessment of on-farm yield can be challenging, even more for sorghum (Sorghum bicolor L.) crop due to the complexity for accounting for the grain number at field-scale. One alternative to reduce labor is to develop a rapid assessment method employing computer vision and artificial intelligence... G.N. Nocera santiago, P. Cisdeli magalhães, I. Ciampitti, L. Marziotte