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Mulla, D
Basso, B
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Authors
Nigon, T.J
Rosen, C
Mulla, D
Cohen, Y
Alchanatis, V
Rud, R
Cohen, Y
Alchanatis, V
Heuer, B
Lemcoff, H
Sprintsin, M
Rosen, C
Mulla, D
Nigon, T
Dar, Z
Cohen, A
Levi, A
Brikman, R
Markovits, T
Rud, R
Basso, B
Fiorentino, C
Cammarano, D
D'Errico, A
Chou, T
Yeh, M
Chen, J
Basso, B
Basso, B
http://icons.paqinteractive.com/16x16/ac, G
http://icons.paqinteractive.com/16x16/ac, G
http://icons.paqinteractive.com/16x16/ac, G
Basso, B
Destain, J
Bodson, B
Destain, M
Dumont, B
Mulla, D
Zermas, D
Kaiser, D
Bazakos, M
Papanikolopoulos, N
Stanitsas, P
Morellas, V
Mulla, D
Laacouri, A
Kaiser, D
Brockgreitens, J
Bui, M
Abbas, A
Mulla, D
Nigon, T
Mulla, D
Yang, C
Laacouri, A
Nigon, T
Mulla, D
Yang, C
Bohman, B
Mulla, D
Rosen, C
Topics
Remote Sensing Applications in Precision Agriculture
Modeling and Geo-statistics
Modeling and Geo-statistics
Precision Nutrient Management
Unmanned Aerial Systems
Remote Sensing Applications in Precision Agriculture
Engineering Technologies and Advances
In-Season Nitrogen Management
Big Data, Data Mining and Deep Learning
Type
Poster
Oral
Year
2012
2010
2014
2016
2018
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Authors

Filter results12 paper(s) found.

1. Hyperspectral Imagery for the Detection of Nitrogen Stress in Potato for In-season Management

... T.J. Nigon, C. Rosen, D. Mulla, Y. Cohen, V. Alchanatis, R. Rud

2. Evaluating Water Status in Potato Fields Using Combined Information from RGB and Thermal Aerial Images

Potato yield and quality are highly dependent on an adequate supply of water. In this study the combined information from RGB and thermal aerial images to evaluate... Y. Cohen, V. Alchanatis, B. Heuer, H. Lemcoff, M. Sprintsin, C. Rosen, D. Mulla, T. Nigon, Z. Dar, A. Cohen, A. Levi, R. Brikman, T. Markovits, R. Rud

3. Understanding Spatial and Temporal Variability of Wheat Yield: An Integrated System Approach

Spatial variation in soil water and nitrogen are often the causes of crop yield spatial variability due to their influence on the uniformity of plant stand at emergence and for in-season stresses. Natural and acquired variability in production capacity or potential within a field causes uniform agronomic management practices for the field to be correct in some parts and inappropriate in others. To achieve... B. Basso, C. Fiorentino, D. Cammarano, A. D'errico

4. I-SALUS: New Web Based Spatial Systems for Simulating Crop Yield and Environmental Impact

  SALUS (System Approach to Land Use Sustainability) model is designed to simulate the impact of agronomic management on yield and environmental impact. SALUS model has new approaches and algorithms for simulating soil carbon, nitrogen, phosphorous, tillage, soil water balance and yield components. In the past, the use of the crop model was not easy for general... T. Chou, M. Yeh, J. Chen, B. Basso

5. Assessment Of Climate Variability On Optimal Nitrogen Fertilizer Rates For Precision Agriculture

 Yield response functions... B. Basso, G. Http://icons.paqinteractive.com/16x16/ac, G. Http://icons.paqinteractive.com/16x16/ac, G. Http://icons.paqinteractive.com/16x16/ac

6. Nitrogen Fertilisation Recommendations : Could They Be Improved Using Stochastically Generated Climates In Conjunction With Crop Models ?

In the context of precision nitrogen (N) management, to ensure that the yield potential could be reached each year, farmers have too often applied quantities of fertilizers much larger than what was strictly required. However, since 2002, the Belgian Government transposed the European Nitrate Directive 91/676/EEC in the Belgian law, with the aim to maintain the productivity and the revenue of Belgian's farmers while reducing the environmental impact of excessive N management... B. Basso, J. Destain, B. Bodson, M. Destain, B. Dumont

7. Early Detection of Nitrogen Deficiency in Corn Using High Resolution Remote Sensing and Computer Vision

The continuously growing need for increasing the production of food and reducing the degradation of water supplies, has led to the development of several precision agriculture systems over the past decade so as to meet the needs of modern societies. The present study describes a methodology for the detection and characterization of Nitrogen (N) deficiencies in corn fields. Current methods of field surveillance are either completed manually or with the assistance of satellite imaging, which offer... D. Mulla, D. Zermas, D. Kaiser, M. Bazakos, N. Papanikolopoulos, P. Stanitsas, V. Morellas

8. Comparison Between High Resolution Spectral Indices and SPAD Meter Estimates of Nitrogen Deficiency in Corn

Low altitude remote sensing provides an ideal platform for monitoring time sensitive nitrogen status in crops. Research is needed however to understand the interaction between crop growth stage, spatial resolution and spectral indices derived from low altitude remote sensing. A TetraCam camera equipped with six bands including the red edge and near infrared (NIR) was used to investigate corn nitrogen dynamics. Remote sensing data were collected during the 2013 and 2014 growing seasons at four... D. Mulla, A. Laacouri, D. Kaiser

9. Field Sampling and Electrochemical Detection of Nitrate in Agricultural Soils

Nitrate is an essential plant nutrient and is added to farm fields to increase crop yields. While the addition of nitrate is important for production, over-fertilization with nitrate can lead to leaching and contamination of water bodies. Increased nitrate loading in water sources then leads to eutrophication and hypoxia in downstream regions. Many efforts are being made to accurately control nitrate fertilizer additions to fields. Here, we present a soil sampling device that directly samples... J. Brockgreitens, M. Bui, A. Abbas, D. Mulla

10. Utilization of Spatially Precise Measurements to Autocalibrate the EPIC Agroecosystem Model

Corn nitrogen recommendations for individual fields must improve to minimize the negative influence that agriculture has on the environment and society. Two adaptive N management approaches for making in-season N fertilizer recommendations are remote sensing and crop systems modeling. Remote sensing has the advantage of characterizing the spatial variability at a high spatial resolution, and crop models are prognostic and can assess expected additions and losses that are not yet reflected by the... T. Nigon, D. Mulla, C. Yang

11. A Case Study Comparing Machine Learning and Vegetation Indices for Assessing Corn Nitrogen Status in an Agricultural Field in Minnesota

Compact hyperspectral sensors compatible with UAV platforms are becoming more readily available. These sensors provide reflectance in narrow spectral bands while covering a wide range of the electromagnetic spectrum. However, because of the narrow spectral bands and wide spectral range, hyperspectral data analysis can benefit greatly from data mining and machine learning techniques to leverage its power. In this study, rainfed corn was grown during the 2017 growing season using four nitrogen treatments... A. Laacouri, T. Nigon, D. Mulla, C. Yang

12. Evaluating Remote Sensing Based Adaptive Nitrogen Management for Potato Production

Conventional nitrogen (N) management for potato production in the Upper Midwest, USA relies on using split-applications of N fertilizer or a controlled release N product. Using remote sensing to adaptively manage N applications has the potential to improve N use efficiency and reduce losses of nitrate to groundwater, which are important regional concerns. A two-year plot-scale experiment was established to evaluate adaptive N-management using remote sensing compared to conventional practices for... B. Bohman, D. Mulla, C. Rosen