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Jones, B
Sridharan, S
Fernandez, O
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Authors
Jones, B
McBeath, T
Wilhelm, N
Sridharan, S
Sornapudi, S
Hu, Q
Kumpatla, S
Bier, J
Topics
Spatial Variability in Crop, Soil and Natural Resources
Big Data, Data Mining and Deep Learning
Type
Oral
Year
2016
2022
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Filter results2 paper(s) found.

1. Shifting Fertiliser Response Zones in a Four Year, Whole-paddock Cereal Cropping Experiment.

Precision agriculture in cropping areas of dryland Australia has focused on managing within production zones. These are ideally stable, possibly soil- and topography-based areas within fields. There are many different ideas on how to delimit and implement zones, and a four year whole-field experiment, with low, medium and high treatment philosophies applied per 9m seeder/harvester width across the entire field, was established to explore how zones might best be established and used. The treatment... B. Jones, T. Mcbeath, N. Wilhelm

2. 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