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Li, L
Laursen, M.S
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Authors
Zhao, Y
Li, L
Ting, K.C
Tian, L.F
Ahamed, T
Li, L
Jiang, D
Campos, R.P
Lu, Z
Tian, L.F
Dyrmann, M
Skovsen, S
Jørgensen, R.N
Laursen, M.S
Christiansen, M.P
Laursen, M.S
Jørgensen, R.N
Skovsen, S
Gislum, R
Topics
Remote Sensing Applications in Precision Agriculture
Proximal Sensing in Precision Agriculture
Proximal and Remote Sensing of Soil and Crop (including Phenotyping)
Type
Oral
Year
2014
2018
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Filter results4 paper(s) found.

1. Near-Real-Time Remote Sensing And Yield Monitoring Of Biomass Crops

The demand for bioenergy crops production has increased tremendously by the biofuel industry for substitution of traditional fuels due to the economic availability and environmental benefits. Pre-Harvest monitoring of biomass production is necessary to develop optimized instrumentation and data processing systems for crop growth, health and stress monitoring; and to develop algorithms for field operation scheduling. To cope with the problems of missing critical... Y. Zhao, L. Li, K.C. Ting, L.F. Tian, T. Ahamed

2. Field-Based High-Throughput Phenotyping Approach For Soybean Plant Improvement

The continued development of new, high yielding cultivars needed to meet the world’s growing food demands will be aided by improving the technology to rapidly phenotype potential cultivars. High-throughput phenotyping (HTP) is essential to maximize the greatest value of genetics analysis and to better understand the plant biology and physiology in view of a “Feed the World in 2050” theme. Field-based high-throughput phenotyping platform... L. Li, D. Jiang, R.P. Campos, Z. Lu, L.F. Tian

3. Using a Fully Convolutional Neural Network for Detecting Locations of Weeds in Images from Cereal Fields

Information about the presence of weeds in fields is important to decide on a weed control strategy. This is especially crucial in precision weed management, where the position of each plant is essential for conducting mechanical weed control or patch spraying. For detecting weeds, this study proposes a fully convolutional neural network, which detects weeds in images and classifies each one as either a monocot or dicot. The network has been trained on over 13 000 weed annotations... M. Dyrmann, S. Skovsen, R.N. Jørgensen, M.S. Laursen

4. Ground Vehicle Mapping of Fields Using LiDAR to Enable Prediction of Crop Biomass

Mapping field environments into point clouds using a 3D LIDAR has the ability to become a new approach for online estimation of crop biomass in the field. The estimation of crop biomass in agriculture is expected to be closely correlated to canopy heights. The work presented in this paper contributes to the mapping and textual analysis of agricultural fields. Crop and environmental state information can be used to tailor treatments to the specific site. This paper presents the current results... M.P. Christiansen, M.S. Laursen, R.N. Jørgensen, S. Skovsen, R. Gislum