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Udompetaikul, V
Hülsbergen, K.J
Hu, Q
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Authors
Upadhayaya, S.K
Udompetaikul, V
Shafii, M.S
Browne, G.T
Strenner, M
Maidl, F.X
Hülsbergen, K.J
Sridharan, S
Sornapudi, S
Hu, Q
Kumpatla, S
Bier, J
Topics
Engineering Technologies
Proximal and Remote Sensing of Soil and Crop (including Phenotyping)
Big Data, Data Mining and Deep Learning
Type
Oral
Year
2008
2018
2022
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Filter results3 paper(s) found.

1. A Tree Planting Site-Specific Fumigant Applicator for Orchard Crops

The goal of this research was to use recent advances in the global positioning system and computer technology to apply just the right amount of fumigant where it is most needed (i.e., in the neighborhood of each tree planting site or tree- planting-site-specific application) to decrease the incidence of replant disease, and achieve the environmental and economical benefits of reducing the application of these toxic chemicals. In the first year of this study we retrofitted a chemical applicator... S.K. Upadhayaya, V. Udompetaikul, M.S. Shafii, G.T. Browne

2. Nitrogen Sensing by Using Spectral Reflectance Measurements in Cereal Rye Canopy

Cereal rye (cereale secale L.) is a winter crop well suited for cultivation especially besides high yield areas because of its relatively low demands on the soil and on the climate as well. In 2016 about 4.9% of arable land in Germany was cultivated with cereal rye (Statistisches Bundesamt, 2017). Unlike other crops such as wheat, there is little research on cereal rye for site specific farming. Furthermore, also in a cereal rye cultivation it is necessary to minimize nitrogen loss.... M. Strenner, F.X. Maidl, K.J. Hülsbergen

3. A Generative Adversarial Network-based Method for High Fidelity Synthetic Data Augmentation

Digital Agriculture has led to new phenotyping methods that use artificial intelligence and machine learning solutions on image and video data collected from lab, greenhouse, and field environments. The availability of accurately annotated image and video data remains a bottleneck for developing most machine learning and deep learning models. Typically, deep learning models require thousands of unique samples to accurately learn a given task. However, manual annotation of a large dataset will... S. Sridharan, S. Sornapudi, Q. Hu, S. Kumpatla, J. Bier