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Mulla, D
MARTÍNEZ-CASASNOVAS, J.A
Majumdar, K
Gonzalez, J
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Authors
Arno, J
DEL MORAL, I
Escolà, A
Company, J
MARTÍNEZ-CASASNOVAS, J.A
MASIP, J
SANZ, R
ROSELL, J.R
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
Pampolino, M
Majumdar, K
Phillips, S
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
Zhao, T
Chen, Y
Franzen, J
Gonzalez, J
Yang, Q
Nigon, T
Mulla, D
Yang, C
Laacouri, A
Nigon, T
Mulla, D
Yang, C
Bohman, B
Mulla, D
Rosen, C
Topics
Proximal Sensing in Precision Agriculture
Remote Sensing Applications in Precision Agriculture
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
2014
2016
2018
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Authors

Filter results11 paper(s) found.

1. Mapping the Leaf Area Index In Vineyard Using a Ground-Based LIDAR Scanner

The leaf area index (LAI) is defined as the one-sided leaf area per unit ground area and is probably the most widely used index to characterize grapevine vigour. However, direct LAI measurement requires the use of destructive leaves sampling methods which are costly and time-consuming and so are other indirect methods. Faced with these techniques, vineyard leaf area can be indirectly estimated using ground-based LIDAR sensors that scan the vines and get information about the geometry and/or structure... J. Arno, I. Del moral, A. Escolà, J. Company, J.A. MartÍnez-casasnovas, J. Masip, R. Sanz, J.R. Rosell

2. 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

3. 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

4. Nutrient Expert Software For Nutrient Management In Cereal Crops

Many countries in Asia have started replacing blanket fertilizer recommendations for vast areas of rice, maize, or wheat with more site-specific guidelines adapted to local needs. This process has been accompanied with a shift from traditional on-station research to on-farm development and evaluation of novel practices. A key challenge faced by the local extension agencies remains the complex nature of factors influencing nutrient requirements.  To aid in this process, the International... M. Pampolino, K. Majumdar, S. Phillips

5. 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

6. 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

7. 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

8. Melon Classification and Segementation Using Low Cost Remote Sensing Data Drones

Object recognition represents currently one of the most developing and challenging areas of the Computer Vision. This work presents a systematic study of various relevant parameters and approaches allowing semi-automatic or automatic object detection, applied onto a study case of melons on the field to be counted. In addition it is of a cardinal interest to obtain the quantitative information about performance of the algorithm in terms of metrics the suitability whereof is determined by the final... T. Zhao, Y. Chen, J. Franzen, J. Gonzalez, Q. Yang

9. 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

10. 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

11. 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