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Hennessy, P.J

Hyrien, M

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Authors

Romier, C

Hyrien, M

Lamker, D

Hennessy, P.J

Esau, T.J

Schumann, A.W

Farooque, A.A

Zaman, Q.U

White, S.N

Topics

Education and Training in Precision Agriculture

Big Data, Data Mining and Deep Learning

Type

Poster

Oral

Year

2012

2022

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Filter results2 paper(s) found.
1. A New Approach to Yield Map Creation One of the barriers to using yield maps as a data layer in precision agriculture activities is that the maps being generated to day are not very accurate in representing what really happened in field. Numerous data errors in the way the data is collected, poor calibration habits on the part of operators... C. Romier, M. Hyrien, D. Lamker Download paper
2. Meta Deep Learning Using Minimal Training Images for Weed Classification in Wild Blueberry Deep learning convolutional neural networks (CNNs) have gained popularity in recent years for their ability to classify images with high levels of accuracy. In agriculture, they have been applied for disease identification, crop growth monitoring, animal behaviour tracking, and weed classification. Datasets traditionally consisting of thousands of images of each desired target are required to train CNNs. A recent survey of Nova Scotia wild blueberry (Vaccinium angustifolium Ait.) fields,... P.J. Hennessy, T.J. Esau, A.W. Schumann, A.A. Farooque, Q.U. Zaman, S.N. White Download paper

Filter results2 paper(s) found.

1. A New Approach to Yield Map Creation

One of the barriers to using yield maps as a data layer in precision agriculture activities is that the maps being generated to day are not very accurate in representing what really happened in field. Numerous data errors in the way the data is collected, poor calibration habits on the part of operators... C. Romier, M. Hyrien, D. Lamker

Download paper

2. Meta Deep Learning Using Minimal Training Images for Weed Classification in Wild Blueberry

Deep learning convolutional neural networks (CNNs) have gained popularity in recent years for their ability to classify images with high levels of accuracy. In agriculture, they have been applied for disease identification, crop growth monitoring, animal behaviour tracking, and weed classification. Datasets traditionally consisting of thousands of images of each desired target are required to train CNNs. A recent survey of Nova Scotia wild blueberry (Vaccinium angustifolium Ait.) fields,... P.J. Hennessy, T.J. Esau, A.W. Schumann, A.A. Farooque, Q.U. Zaman, S.N. White

Download paper