Weed Growth Stage Estimator Using Deep Convolutional Neural Networks

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...

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Bibliographic Details
Published in:	Sensors (Basel, Switzerland) Vol. 18; no. 5; p. 1580
Main Authors:	Teimouri, Nima, Dyrmann, Mads, Nielsen, Per Rydahl, Mathiassen, Solvejg Kopp, Somerville, Gayle J, Jørgensen, Rasmus Nyholm
Format:	Journal Article
Language:	English
Published:	Switzerland MDPI AG 16-05-2018 MDPI
Subjects:	Accuracy computer vision convolutional neural network deep learning Estimation growth stage Image Processing, Computer-Assisted leaf counting Neural networks Neural Networks (Computer) Plant Leaves - anatomy & histology Plant Leaves - physiology Poaceae - anatomy & histology Poaceae - growth & development Poaceae - physiology Polygonum - anatomy & histology Polygonum - growth & development Polygonum - physiology Weeds deep learning growth stage leaf counting convolutional neural network computer vision
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Summary:	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 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.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1424-8220 1424-8220
DOI:	10.3390/s18051580