CIGR2016 Talk - Semantic Segmentation of Mixed Crops using Deep Convolutional Neural Network
Channel: Henrik Skov Midtiby
Date: 30/06/2016

Presenting Author: Anders Krogh Mortensen - Aarhus University, Department of Agroecology, Denmark

Link to conference: http://conferences.au.dk/cigr-2016/

Description

Estimating total crop biomass or individual crop components in a mixed cropping system is important for agricultural research and farming. For example the assessment of either grass and/or clover biomass in a forage based feed system can be used to optimise animal feeding plans. Moreover, the quantification of autumn catch crop biomass coupled with Nitrogen concentration will make it possible to estimate the Nitrogen uptake.The common first step in estimating the biomass of fields with mixed crops is being able to distinguish both the species and the species from the surrounding environment.

In the current study, deep convolutional neural networks for semantic segmentation (or pixelwise classification) of cluttered classes in RGB images was explored in case of catch crops and volunteer barley cereal. The dataset consisted of RGB images from a plot trial using oil radish as catch crops in barley. The images were captured using a high-end consumer camera mounted on a tractor. The images was manually annotated in 7 classes: oil radish, barley, weed, stump, soil, equipment and unknown. Data argumentation was used to artificially increase the dataset by transposing and flipping the images. A modified version of VGG-16 deep neural network was used. First, the last fully-connected was converted to convolutional layer and the depth was modified to cope with our number of classes. Secondly, a deconvolutional layer with a 32 stride was added between the last fully-connected layer and the softmax classification layer to ensure that the output layer has the same size as the input.

Preliminary results using this network show a pixel accuracy of 79% and a frequency weighted intersection over union of 67%. These preliminary results indicate great potential in deep convolutional networks for segmentation of plant species in cluttered RGB images.

Contributors

Anders Krogh Mortensen (Department of Agroecology, Aarhus University, Aarhus, Denmark), Mads Dyrmann (Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense, Denmark), Henrik Karstoft (Department of Engineering, Aarhus University, Aarhus, Denmark), Rasmus Nyholm Jørgensen (Department of Engineering, Aarhus University, Aarhus, Denmark), René Gislum (Department of Agroecology, Aarhus University, Slagelse, Denmark)