Estimation of the Botanical Composition of Clover-Grass Leys from RGB Images Using Data Simulation and Fully Convolutional Neural Networks

Estimation of the Botanical Composition of Clover-Grass Leys from RGB Images Using Data Simulation and Fully Convolutional Neural Networks

Optimal fertilization of clover-grass fields relies on knowledge of the clover and grass fractions. This study shows how knowledge can be obtained by analyzing images collected in fields automatically. A fully convolutional neural network was trained to create a pixel-wise classification of clover,...

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Bibliographic Details
Main Authors: Søren Skovsen, Mads Dyrmann, Anders Krogh Mortensen, Kim Arild Steen, Ole Green, Jørgen Eriksen, René Gislum, Rasmus Nyholm Jørgensen, Henrik Karstoft
Format: Article
Language:English
Published: MDPI AG 2017-12-01
Series:Sensors
Subjects:
deep learning
clover-grass
precision agriculture
dry matter composition
proximity sensing
Online Access:https://www.mdpi.com/1424-8220/17/12/2930

Internet

https://www.mdpi.com/1424-8220/17/12/2930

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