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Matese, A
Ferraz, M.N
Fernandez, F.G
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Authors
Spekken, M
Molin, J.P
Romanelli, T.L
Ferraz, M.N
Ferraz, M.N
Molin, J.P
Bean, G
Kitchen, N.R
Franzen, D.W
Miles, R.J
Ransom, C
Scharf, P
Camberato, J
Carter, P
Ferguson, R.B
Fernandez, F.G
Laboski, C
Nafziger, E
Sawyer, J
Shanahan, J
Ransom, C.J
Bean, M
Kitchen, N
Camberato, J
Carter, P
Ferguson, R.B
Fernandez, F.G
Franzen, D.W
Laboski, C
Nafziger, E
Sawyer, J
Shanahan, J
Trevisan, R.G
Eitelwein, M.T
Ferraz, M.N
Tavares, T.R
Molin, J.P
Neves, D.C
Ferraz, M.N
Trevisan, R.G
Eitelwein, M.T
Molin, J
Karp, F.H
Matese, A
Topics
Decision Support Systems in Precision Agriculture
Proximal Sensing in Precision Agriculture
Precision Nutrient Management
Sensor Application in Managing In-season Crop Variability
Precision Crop Protection
Proximal and Remote Sensing of Soil and Crop (including Phenotyping)
Type
Oral
Poster
Year
2016
2018
2025
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Filter results7 paper(s) found.

1. Site Specific Costs Concerning Machine Path Orientation

Computer algorithms have been created to simulate in advance the orientation/pattern of a machine operation on a field. Undesired impacts were obtained and quantified for these simulations, like: maneuvering and overlap of inputs in headlands; servicing of secondary units; and soil loss by water erosion. While the efforts could minimize the overall costs, they disregard the fact that these costs aren’t uniformly distributed over irregular fields. The cost of a non-productive machine process... M. Spekken, J.P. Molin, T.L. Romanelli, M.N. Ferraz

2. NIR Spectroscopy to Map Quality Parameters of Sugarcane

Precision Agriculture aims to explore the potential of each crop considering the differences within the field. One information that is considered the most important is the yield or the obtained income in the field. However, in the case of sugarcane, quality will also directly influence farmer’s income. Several studies suggest harvester automation aiming to monitor yield, but few consider the quality analysis in the process. Among the existing methods for measuring sugar content the one that... M.N. Ferraz, J.P. Molin

3. Modifying the University of Missouri Corn Canopy Sensor Algorithm Using Soil and Weather Information

Corn production across the U.S. Corn belt can be often limited by the loss of nitrogen (N) due to leaching, volatilization and denitrification. The use of canopy sensors for making in-season N fertilizer applications has been proven effective in matching plant N requirements with periods of rapid N uptake (V7-V11), reducing the amount of N lost to these processes. However, N recommendation algorithms used in conjunction with canopy sensor measurements have not proven accurate in making N recommendations... G. Bean, N.R. Kitchen, D.W. Franzen, R.J. Miles, C. Ransom, P. Scharf, J. Camberato, P. Carter, R.B. Ferguson, F. Fernandez, C. Laboski, E. Nafziger, J. Sawyer, J. Shanahan

4. Field-scale Nitrogen Recommendation Tools for Improving a Canopy Reflectance Sensor Algorithm

Nitrogen (N) rate recommendation tools are utilized to help producers maximize grain yield production. Many of these tools provide recommendations at field scales but often fail when corn N requirements are variable across the field. This may result in excess N being lost to the environment or producers receiving decreased economic returns on yield. Canopy reflectance sensors are capable of capturing within-field variability, although the sensor algorithm recommendations may not always be as accurate... C.J. Ransom, M. Bean, N. Kitchen, J. Camberato, P. Carter, R. Ferguson, F. Fernandez, D. Franzen, C. Laboski, E. Nafziger, J. Sawyer, J. Shanahan

5. Optimum Spatial Resolution for Precision Weed Management

The occurrence and number of herbicide-resistant weeds in the world has increased in recent years. Controlling these weeds becomes more difficult and raises production costs. Precision spraying technologies have been developed to overcome this challenge. However, these systems still have relatively high acquisition cost, requiring studies of the relation between the spatial distribution of weeds and the economically optimum spatial resolution of the control method. In this context, the objective... R.G. Trevisan, M.T. Eitelwein, M.N. Ferraz, T.R. Tavares, J.P. Molin, D.C. Neves

6. Soybean Plant Phenotyping Using Low-Cost Sensors

Plant phenotyping techniques are important to present the performance of a crop and it interaction with the environment. The phenotype information is important for plant breeders to analyze and understand the plant responses from the ambient conditions and the inputs offered for it. However, for conclusive analysis it is necessary a large number of individuals. Thus, phenotyping is the bottleneck of plant breeding, a consequence of the labor intensive and costly nature of the classical phenotyping.... M.N. Ferraz, R.G. Trevisan, M.T. Eitelwein, J. Molin, F.H. Karp

7. Innovating Irrigation: Affordable Smart Solutions for Water Sustainability

Agriculture accounts for 70–80% of global freshwater use, a level increasingly unsustainable under climate change. This study reports the development and field validation of a low-cost smart irrigation system for tomato and melon in Tuscany (2021–2023). The system integrates evapotranspiration-based models, wireless sensor networks, and adaptive control algorithms. In 2023 it achieved up to 50% water savings compared to traditional practices, without yield reduction, at a total cost... A. Matese