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Heinrich, T
Tsogt-Ochir, S
Wulfsohn, D
Schmidt, R
Bouroubi, Y
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Authors
Zamora, I
Wulfsohn, D
Tumenjargal, E
Batbayar, E
Munkhbayar, S
Tsogt-Ochir, S
Oyumaa, M
Chung, K
Ham, W
Bouroubi, Y
Bugnet, P
Nguyen-Xuan, T
Bélec, C
Longchamps, L
Vigneault, P
Gosselin, C
Tsukor, V
Scholz, C
Nietfeld, W
Heinrich, T
Mosler , T
Lorenz, F
Najdenko, E
Möller, A
Mentrup, D
Ruckelshausen, A
Hinck, S
Shinde, S
Adamchuk, V
Lacroix, R
Tremblay, N
Bouroubi, Y
Jha, G
Nazrul, F
Nocco, M
Pagé Fortin, M
Whitaker, B
Diaz, D
Gal, A
Schmidt, R
Dey, S
Topics
Applications of UAVs (unmanned aircraft vehicle systems) in precision agriculture
Robotics, Guidance and Automation
Big Data, Data Mining and Deep Learning
Site-Specific Nutrient, Lime and Seed Management
Decision Support Systems
Weather and Models for Precision Agriculture
Type
Oral
Poster
Year
2014
2018
2024
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Filter results6 paper(s) found.

1. The Use Of A Multirotor And High-Resolution Imaging For Precision Horticulture In Chile: An Industry Perspective

As part of the prototype development of a yield forecasting and precision agriculture service for Chilean horticulture, we evaluated the use of an eight-rotor Mikrokopter for high-resolution aerial imaging to support ground-based surveys. Specific considerations for UAV and communications performance under Chilean conditions are windy conditions, limited space for take-off and landing in orchards, tree height and plantation density, and the presence of high metal contents in soils. We discuss... I. Zamora, D. Wulfsohn

2. Design and Analysis of ISO 11783 Task Controller's Functionality in Server - Client ECU for Agricultural Vehicles

A modern agricultural vehicle's electronic control units (ECU) communicated based on the ISO 11783 standards. The connection of different machines, implements, different manufacturers into a single bus for the exchange of control commands and sensor data are a challenge for the precision agriculture. One of main functionality is the Task controller in the intelligent monitoring system. The task controller is to log data and assign set-point values for automated work (task) sequences... E. Tumenjargal, E. Batbayar, S. Munkhbayar, S. Tsogt-ochir, M. Oyumaa, K. Chung, W. Ham

3. Pest Detection on UAV Imagery Using a Deep Convolutional Neural Network

Presently, precision agriculture uses remote sensing for the mapping of crop biophysical parameters with vegetation indices in order to detect problematic areas, and then send a human specialist for a targeted field investigation. The same principle is applied for the use of UAVs in precision agriculture, but with finer spatial resolutions. Vegetation mapping with UAVs requires the mosaicking of several images, which results in significant geometric and radiometric problems. Furthermore, even... Y. Bouroubi, P. Bugnet, T. Nguyen-xuan, C. Bélec, L. Longchamps, P. Vigneault, C. Gosselin

4. soil2data: Concept for a Mobile Field Laboratory for Nutrient Analysis

Knowledge of the small-scale nutrient status of arable land is an important basis for optimizing fertilizer use in crop production. A mobile field laboratory opens up the possibility of carrying out soil sampling and nutrient analysis directly on the field. In addition to the benefits of fast data availability and the avoidance of soil material transport to the laboratory, it provides a future foundation for advanced application options, e.g. a high sampling density, sampling of small sub-fields... V. Tsukor, C. Scholz, W. Nietfeld, T. Heinrich, T. Mosler , F. Lorenz, E. Najdenko, A. Möller, D. Mentrup, A. Ruckelshausen, S. Hinck

5. Development of an Online Decision-Support Infrastructure for Optimized Fertilizer Management

Determination of an optimum fertilizer application rate involves various influential factors, such as past management, soil characteristics, weather, commodity prices, cost of input materials and risk preference. Spatial and temporal variations in these factors constitute sources of uncertainties in selecting the most profitableapplication rate. Therefore, a decision support system (DSS) that could help to minimize production risks in the context of uncertain crop performance is needed. This... S. Shinde, V. Adamchuk, R. Lacroix, N. Tremblay, Y. Bouroubi

6. Prediction of Field-scale Evapotranspiration Using Process Based Modeling and Geostatistical Time-series Interpolation

Irrigation scheduling depends on the combination of evaporative demand from the atmosphere, spatial and temporal heterogeneity in soil properties and changes in crop canopy during a growing season. This on-farm trial is based on data collected in 72-acre processing tomato field in Central Valley of California. The Multiband Spectrometric Arable Mark 2 sensors at three different locations in the field. Multispectral and thermal imagery provided by Ceres Imaging were collected eight times during... G. Jha, F. Nazrul, M. Nocco, M. Pagé fortin, B. Whitaker, D. Diaz, A. Gal, R. Schmidt