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Zimmermanm, L
Reeg, P
Lutz, C.C
Nyéki , A
Bindelle, J
Guan, H
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Authors
Shiratsuchi, L
Lutz, C.C
Ferguson, R.B
Adamchuk, V.I
Reeg, P
Kyveryga, P.M
Mueller, T.A
Andriamandroso, A
Dumont, B
Lebeau, F
Bindelle, J
Nyéki , A
Milics, G
Kovács, A.J
Neményi, M
Kulmány, I
Zsebő, S
Muller, O
Keller, B
Zimmermanm, L
Jedmowski, C
Pingle, V
Acebron, K
Zendonadi, N
Steier, A
Pieruschka, R
Schurr, U
Rascher, U
Kraska, T
Zhou, C
Ampatzidis, Y
Guan, H
Liu, W
de Oliveira Costa Neto, A
Kunwar, S
Batuman, O
Topics
Proximal Sensing in Precision Agriculture
Profitability, Sustainability and Adoption
Precision Dairy and Livestock Management
On Farm Experimentation with Site-Specific Technologies
Proximal and Remote Sensing of Soil and Crop (including Phenotyping)
Robotics and Automation with Row and Horticultural Crops
Type
Poster
Oral
Year
2012
2014
2018
2024
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Filter results6 paper(s) found.

1. Integrated Crop Canopy Sensing System for Spatial Analysis of In-Season Crop Performance

Over the past decade, the relationships between leaf color, chlorophyll content, nitrogen supply, biomass and grain yield of agronomic crops have been studied widely.... L. Shiratsuchi, C.C. Lutz, R.B. Ferguson, V.I. Adamchuk

2. Evaluating Decision Systems For Using Variable Rates In Planting Soybean

Increased interest in managing seeding rates within soybean fields is being driven by the advances in technologies and the need to increase productivity and economic returns. A wealth of previous research was focused on studying how different seeding rates affect soybean yields at small-plot scales. However, little is known how different site-specific factors influence the responsiveness of soybean to higher or lower plant population densities at field levels, especially across geographic... P. Reeg, P.M. Kyveryga, T.A. Mueller

3. The Performance Of Mobile Devices' Inertial Measurement Unit For The Detection Of Cattle's Behaviors On Pasture

Over the past decade, the Precision Livestock Farming (PLF) concept has taken a considerable place in the development of accurate methods for a better management of farm animals. The recent technological improvements allow the raising of numerous motion sensors such as accelerometers and GPS tracking. Several studies have shown the relevancy of these sensors to distinguish the animals’ behavior using various classification techniques such as neuronal networks or multivariate... A. Andriamandroso, B. Dumont, F. Lebeau, J. Bindelle

4. Improving Yield Prediction Accuracy Using Energy Balance Trial, On-the-Go and Remote Sensing Procedure

 Our long term experience in the ~23.5 ha research field since 2001 shows that decision support requires complex databases from each management zone within that field (eg. soil physical and chemical parameters, technological, phenological and meteorological data). In the absence of PA sustainable biomass production cannot be achieved. The size of management zones will be ever smaller. Consequently, the on the go and remote sensing data collection should be preferred.  The... A. Nyéki , G. Milics, A.J. Kovács, M. Neményi, I. Kulmány, S. Zsebő

5. Field Phenotyping and an Example of Proximal Sensing of Photosynthesis

Field phenotyping conceptually can be divided in five pillars 1) traits of interest 2) sensors to measure these traits 3) positioning systems to allow high throughput measurements by the sensors 4) experimental sites and 5) environmental monitoring. In this paper we will focus on photosynthesis as trait of interest, measured by remote active fluorescence. The sensor presented is the Light Induced Fluorescence Transient (LIFT) instrument. The LIFT instrument is integrated in three positioning systems.... O. Muller, B. Keller, L. Zimmermanm, C. Jedmowski, V. Pingle, K. Acebron, N. Zendonadi, A. Steier, R. Pieruschka, U. Schurr, U. Rascher, T. Kraska

6. Agrosense: AI-enabled Sensing for Precision Management of Tree Crops

Monitoring the tree inventory and canopy density and height frequently is critical for researchers and farm managers. However, it is very expensive and challenging to manually complete these tasks weekly. Therefore, a low-cost and artificial intelligence (AI) enhanced sensing system, Agrosense, was developed for tree inventory, canopy height measurement, and tree canopy density classification in this study. The sensing system mainly consisted of four RGB-D cameras, two Jetson Xavier NX, and one... C. Zhou, Y. Ampatzidis, H. Guan, W. Liu, A. De oliveira costa neto, S. Kunwar, O. Batuman