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Shahid, A
Brungardt, J.J
Zillmann, E
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Authors
Frotscher, K.J
Schacht, R
Smith, L
Zillmann, E
Hunt, E
Rondon, S.I
Bruce, A.E
Turner, R.W
Brungardt, J.J
De Waele, T
Peralta, D
Shahid, A
De Poorter, E
Topics
Remote Sensing Applications in Precision Agriculture
Big Data, Data Mining and Deep Learning
Type
Oral
Poster
Year
2016
2022
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1. Detection of Potato Beetle Damage Using Remote Sensing from Small Unmanned Aircraft Systems

Remote sensing with small unmanned aircraft systems (sUAS) has potential applications in agriculture because low flight altitudes allow image acquisition at very high spatial resolution.  We set up experiments at the Oregon State University Hermiston Agricultural Research and Extension Center (HAREC) to assess advantages and disadvantages of sUAS for precision farming. In 2014, we conducted an experiment in irrigated potatoes with 4 levels of artificial infestation by Colorado Potato Beetles.... E. Hunt, S.I. Rondon, A.E. Bruce, R.W. Turner, J.J. Brungardt

2. Planet Labs' Monitoring Solution in Support of Precision Agriculture Practices

Satellite imagery is particularly useful for efficiently monitoring very large areas and providing regular feedback on the status and productivity of agricultural fields. These data are now widely used in precision farming; however, many challenges to making optimal use of this technology remain, such as easy access to data, management and exploitation of large datasets with deep time series, and sharing of the data and derived analytics with users. Providing satellite imagery through a cloud... K.J. Frotscher, R. Schacht, L. Smith, E. Zillmann

3. Supervised Feature Selection and Clustering for Equine Activity Recognition

In this paper we introduce a novel supervised algorithm for equine activity recognition based on accelerometer data. By combining an approach of calculating a wide variety of time-series features with a supervised feature significance test we can obtain the best suited features using just 5 labeled samples per class and without requiring any expert domain knowledge. By using a simple cluster assignment algorithm with these obtained features, we get a classification algorithm that achieves a mean... T. De waele, D. Peralta, A. Shahid, E. De poorter