Proceedings :: International Society of Precision Agriculture

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Kumpatla, S

Lindsey, A

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Authors

Sridharan, S

Sornapudi, S

Hu, Q

Kumpatla, S

Bier, J

Fulton, J.P

Shearer, S.A

Gauci, A

Lindsey, A

Barker, D

Hawkins, E

Topics

Big Data, Data Mining and Deep Learning

On Farm Experimentation with Site-Specific Technologies

Type

Oral

Year

2022

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Filter results2 paper(s) found.
1. 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 Download paper
2. Limitations of Yield Monitor Data to Support Field-scale Research Precision agriculture adoption on farms continues to grow globally on farms. Today, yield monitors have become standard technologies on grain, cotton and sugarcane harvesters. In recent years, we have seen industry and even academics leveraging the adoption of precision agriculture technologies to conduct field-scale, on-farm research. Industry has been a primary driver of the increase in on-farm research globally through the development of software to support on-farm research. ... J.P. Fulton, S.A. Shearer, A. Gauci, A. Lindsey, D. Barker, E. Hawkins

Filter results2 paper(s) found.

1. 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

Download paper

2. Limitations of Yield Monitor Data to Support Field-scale Research

Precision agriculture adoption on farms continues to grow globally on farms. Today, yield monitors have become standard technologies on grain, cotton and sugarcane harvesters. In recent years, we have seen industry and even academics leveraging the adoption of precision agriculture technologies to conduct field-scale, on-farm research. Industry has been a primary driver of the increase in on-farm research globally through the development of software to support on-farm research. ... J.P. Fulton, S.A. Shearer, A. Gauci, A. Lindsey, D. Barker, E. Hawkins