Semantic Segmentation of Mixed Crops using Deep Convolutional Neural Network
Estimation of in field biomass and crop composition is important for both farmers and researchers. Using close up high resolution images of the crops, crop species can be distinguished using image processing. In the current study, deep convolutional neural networks for semantic segmentation (or pixel wise 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 were 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 layers were 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 66% These preliminary results indicate great potential in deep convolutional networks for segmentation of plant species in cluttered RGB images.