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Article

Weed Growth Stage Estimator Using Deep Convolutional Neural Networks

1
Department of Engineering-Signal Processing, Faculty of Science and Technology, Aarhus University, DK-8000 Aarhus C, Denmark
2
Department of Biosystems Engineering, University of Tehran, Tehran 1417466191, Iran
3
IPM Consult ApS, DK-4295 Stenlille, Denmark
4
Department of Agroecology, Aarhus University, DK-4200 Slagelse, Denmark
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(5), 1580; https://doi.org/10.3390/s18051580
Received: 28 February 2018 / Revised: 7 May 2018 / Accepted: 15 May 2018 / Published: 16 May 2018
This study outlines a new method of automatically estimating weed species and growth stages (from cotyledon until eight leaves are visible) of in situ images covering 18 weed species or families. Images of weeds growing within a variety of crops were gathered across variable environmental conditions with regards to soil types, resolution and light settings. Then, 9649 of these images were used for training the computer, which automatically divided the weeds into nine growth classes. The performance of this proposed convolutional neural network approach was evaluated on a further set of 2516 images, which also varied in term of crop, soil type, image resolution and light conditions. The overall performance of this approach achieved a maximum accuracy of 78% for identifying Polygonum spp. and a minimum accuracy of 46% for blackgrass. In addition, it achieved an average 70% accuracy rate in estimating the number of leaves and 96% accuracy when accepting a deviation of two leaves. These results show that this new method of using deep convolutional neural networks has a relatively high ability to estimate early growth stages across a wide variety of weed species. View Full-Text
Keywords: computer vision; growth stage; leaf counting; convolutional neural network; deep learning computer vision; growth stage; leaf counting; convolutional neural network; deep learning
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MDPI and ACS Style

Teimouri, N.; Dyrmann, M.; Nielsen, P.R.; Mathiassen, S.K.; Somerville, G.J.; Jørgensen, R.N. Weed Growth Stage Estimator Using Deep Convolutional Neural Networks. Sensors 2018, 18, 1580. https://doi.org/10.3390/s18051580

AMA Style

Teimouri N, Dyrmann M, Nielsen PR, Mathiassen SK, Somerville GJ, Jørgensen RN. Weed Growth Stage Estimator Using Deep Convolutional Neural Networks. Sensors. 2018; 18(5):1580. https://doi.org/10.3390/s18051580

Chicago/Turabian Style

Teimouri, Nima, Mads Dyrmann, Per R. Nielsen, Solvejg K. Mathiassen, Gayle J. Somerville, and Rasmus N. Jørgensen 2018. "Weed Growth Stage Estimator Using Deep Convolutional Neural Networks" Sensors 18, no. 5: 1580. https://doi.org/10.3390/s18051580

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