Plant species classification using deep convolutional neural network

Biosystems Engineering

Volumn 151, Issue , 2016, Pages 72-80

  Dyrmann, Mads ^a   Karstoft, Henrik ^b   Midtiby, Henrik Skov ^a  

^a UNIVERSITY OF SOUTHERN DENMARK   (Denmark)

^b AARHUS UNIVERSITY   (Denmark)

Author keywords

Convolutional Neural; Deep learning; Networks; Plant classification; Weed control

Indexed keywords

CONVOLUTION; E-LEARNING; LIGHTING; NETWORKS (CIRCUITS); NEURAL NETWORKS;

AGRICULTURAL FIELDS; CLASSIFICATION ACCURACY; CONTROLLED CONDITIONS; CONVOLUTIONAL NEURAL; CONVOLUTIONAL NEURAL NETWORK; DEEP LEARNING; LIGHTING CONDITIONS; PLANT CLASSIFICATION;

WEED CONTROL;

EID: 84989955034     PISSN: 15375110     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.biosystemseng.2016.08.024     Document Type: Article

References (24)

1. Aarhus University - Department of Agroecology, SEGES, Ukrudtsnøglen. 2015 plantevaernonline.dlbr.dk.
2. Åstrand, B., Baerveldt, A.J., An agricultural mobile robot with vision based perception for mechanical weed control. Autonomous Robots 13 (2002), 21–35 http://link.springer.com/article/10.1023/A:1015674004201.
3. Cao, X., A practical theory for designing very deep convolutional neural networks Classifier Level. 2015.
4. Dieleman, S., Schlüter, J., Raffel, C., Olson, E., Sønderby, S.K., Nouri, D., et al. Lasagne: First release. 2015 http://dx.doi.org/10.5281/zenodo.27878.
5. Dyrmann, M., Christiansen, P., Automated classification of seedlings using computer vision. Tech. Rep. 2014, Aarhus University, Aarhus http://plant_recognition.sdu.dk/files/ACSUCV2015.pdf.
6. Giselsson, T.M., Real time crops and weeds classification from top down images of plant canopies. 2010, University of Southern Denmark http://thomasg.dk/files/masterThesis.pdf.
7. Glorot, X., Bengio, Y., Understanding the difficulty of training deep feed-forward neural networks. Proceedings of the 13th International Conference on Artificial Intelligence and Statistics (AISTATS), 9, 2010, 249–256 http://machinelearning.wustl.edu/mlpapers/paper_files/AISTATS2010_GlorotB10.pdf.
8. Golzarian, M.R., Frick, R.A., Classification of images of wheat, ryegrass and brome grass species at early growth stages using principal component analysis. Plant Methods, 7(1), 2011, 28 http://www.plantmethods.com/content/7/1/28.
9. He, K., Zhang, X., Ren, S., Sun, J., Deep residual learning for image recognition. Vol. 7, dec 2015 (3) http://arxiv.org/abs/1512.03385.
10. Hodgson, J.M., The nature, ecology, and control of Canada thistle. Technical Bulletins 171614, 1968, United States Department of Agriculture, Economic Research Service http://EconPapers.repec.org/RePEc:ags:uerstb:171614.
11. Ioffe, S., Szegedy, C., Batch normalization: Accelerating deep network training by reducing internal covariate shift. 2015, Arxiv http://arxiv.org/abs/1502.03167.
12. Jørgensen, L.N., Noe, E., Langvad, A.-m., Rydahl, P., Jensen, J.E., Ørum, J.E., et al. Vurdering af Planteværn Onlines økonomiske og miljømæssige effekt. (Assessment of Crop Protection Online's economic and environmental effect), 115, 2007, Bekæmpelsesmiddelforskning fra Miljøstyrelsen.
13. Kazmi, W.A., Computer vision based weed detection and 3D plant imaging., 2014, Aalborg University Ph.D. thesis.
14. Krizhevsky, A., Sutskever, I., Hinton, G.E., Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 2012, 1–9.
15. Meier, U., Growth stages of mono-and dicotyledonous plants – BBCH monograph. 2001, Federal Biological Research Centre for Agriculture and Forestry http://www.bba.de/veroeff/bbch/bbcheng.pdf.
16. Minervini, M., Abdelsamea, M.M., Tsaftaris, S.A., Image-based plant phenotyping with incremental learning and active contours. Ecological Informatics 23 (2014), 35–48 (Special Issue on Multimedia in Ecology and Environment) http://doi.org/10.1016/j.ecoinf.2013.07.004.
17. Oerke, E.-C., Crop losses to pests. The Journal of Agricultural Science, 144(01), 2006, 31.
18. RoboWeedSupport, Roboweedsupport.com. 2015 http://roboweedsupport.com/.
19. Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., et al. ImageNet large Scale visual recognition challenge. International Journal of Computer Vision (IJCV) 115:3 (2015), 211–252.
20. Scharr, H., Minervini, M., Fischbach, A., Tsaftaris, S.A., Annotated image datasets of rosette plants. Forschungszentrum Julich, 2014 (Technical Report No. FZJ-2014–03837).
21. Simonyan, K., Zisserman, A., Very deep convolutional networks for large-scale image recognition. arXiv, sep 2014, 1–13 http://arxiv.org/abs/1409.1556.
22. Slaughter, D.C., Giles, D.K., Fennimore, S.A., Smith, R.F., Multispectral machine vision identification of lettuce and weed seedlings for automated weed control. Weed Technology 22:2 (2008), 378–384 http://www.bioone.org/doi/abs/10.1614/WT-07-104.1.
23. Søgaard, H.T., Weed classification by active shape models. Biosystems Engineering 91:3 (jul 2005), 271–281 http://linkinghub.elsevier.com/retrieve/pii/S1537511005000772.
24. Woebbecke, D.M., Meyer, G.E., Bargen, K.V., Mortensen, D.A., Color indices for weed identification under various soil, residue, and lighting conditions. Transactions of the ASAE 38:1 (1995), 259–269.