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Mercatoris, B
Murphy, J.M
McGraw, T
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Authors
Stombaugh, T
Zandonadi, R.S
Luck, J.D
McDonald, T.P
McGraw, T
Destain, M
Leemans, V
Marlier, G
Goffart, J
Bodson, B
Mercatoris, B
Gritten, F
Dandrifosse, S
Ennadifi, E
Carlier, A
Gosselin, B
Dumont, B
Mercatoris, B
El-Mejjaouy, Y
Dumont, B
Oukarroum, A
Mercatoris , B
Vermeulen , P
Tasissa, A
Li, L
Murphy, J.M
Topics
Engineering Technologies and Advances
Proximal Sensing in Precision Agriculture
Proximal and Remote Sensing of Soil and Crop (including Phenotyping)
Artificial Intelligence (AI) in Agriculture
Type
Oral
Year
2010
2016
2022
2024
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Filter results5 paper(s) found.

1. Tools For Evaluating The Potential Of Automatic Section Control

One of the newest technologies in precision agriculture is automatic section control on application equipment. This technology has tremendous potential to reduce wasted inputs, especially on irregularly shaped fields. Paybacks are not necessarily as great on rectangular fields. Producers considering adoption of the technology need to decide whether they will receive sufficient payback for their field shapes. They must also decide... T. Stombaugh, R.S. Zandonadi, J.D. Luck, T.P. Mcdonald, T. Mcgraw

2. Detection of Nitrogen Stress on Winter Wheat by Multispectral Machine Vision

Hand-held sensors (SPAD meter, N-Tester, …) used for detecting the leaves nitrogen  concentration (Nc) present several drawbacks. The nitrogen concentration is gained by an indirect way through the chlorophyll concentration and the leaves have to be fixed in a defined position for the measurements. These drawbacks could be overcome by an imaging device that measures the canopy reflectance. Hence, the objective of the paper is to analyse the potential of multispectral imaging for detecting... M. Destain, V. Leemans, G. Marlier, J. Goffart, B. Bodson, B. Mercatoris, F. Gritten

3. Sun Effect on the Estimation of Wheat Ear Density by Deep Learning

Ear density is one of the yield components of wheat and therefore a variable of high agronomic interest. Its traditional measurement necessitates laborious human observations in the field or destructive sampling. In the recent years, deep learning based on RGB images has been identified as a low-cost, robust and high-throughput alternative to measure this variable. However, most of the studies were limited to the computer challenge of counting the ears in the images, without aiming to convert... S. Dandrifosse, E. Ennadifi, A. Carlier, B. Gosselin, B. Dumont, B. Mercatoris

4. Investigating the Potential of Visible and Near-infrared Spectroscopy (VNIR) for Detecting Phosphorus Status of Winter Wheat Leaves Grown in Long-term Trial

The determination of plant nutrient content is crucial for evaluating crop nutrient removal, enhancing nutrient use efficiency, and optimizing yields. Nutrient conventional monitoring involves colorimetric analyses in the laboratory; however, this approach is labor-intensive, costly, and time-consuming. The visible and near-infrared spectroscopy (VNIR) or hyperspectral non-imaging sensors have been an emerging technology that has been proved its potential for rapid detection of plant nutrient... Y. El-mejjaouy, B. Dumont, A. Oukarroum, B. Mercatoris , P. Vermeulen

5. Sparse Coding for Classification Via a Locality Regularizer: with Applications to Agriculture

High-dimensional data is commonly encountered in various applications, including genomics, as well as image and video processing. Analyzing, computing, and visualizing such data pose significant challenges. Feature extraction methods become crucial in addressing these challenges by obtaining compressed representations that are suitable for analysis and downstream tasks. One effective technique along these lines is sparse coding, which involves representing data as a sparse linear combination of... A. Tasissa, L. Li, J.M. Murphy