A fully convolutional network for weed mapping of unmanned aerial vehicle (UAV) imagery

Appropriate Site Specific Weed Management (SSWM) is crucial to ensure the crop yields. Within SSWM of large-scale area, remote sensing is a key technology to provide accurate weed distribution information. Compared with satellite and piloted aircraft remote sensing, unmanned aerial vehicle (UAV) is capable of capturing high spatial resolution imagery, which will provide more detailed information for weed mapping. The objective of this paper is to generate an accurate weed cover map based on UAV imagery. The UAV RGB imagery was collected in 2017 October over the rice field located in South China. The Fully Convolutional Network (FCN) method was proposed for weed mapping of the collected imagery. Transfer learning was used to improve generalization capability, and skip architecture was applied to increase the prediction accuracy. After that, the performance of FCN architecture was compared with Patch_based CNN algorithm and Pixel_based CNN method. Experimental results showed that our FCN method outperformed others, both in terms of accuracy and efficiency. The overall accuracy of the FCN approach was up to 0.935 and the accuracy for weed recognition was 0.883, which means that this algorithm is capable of generating accurate weed cover maps for the evaluated UAV imagery.

[1]  Xanthoula Eirini Pantazi,et al.  Novelty Detection Classifiers in Weed Mapping: Silybum marianum Detection on UAV Multispectral Images , 2017, Sensors.

[2]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[3]  Amy Loutfi,et al.  Classification and Segmentation of Satellite Orthoimagery Using Convolutional Neural Networks , 2016, Remote. Sens..

[4]  Pierre Alliez,et al.  High-Resolution Semantic Labeling with Convolutional Neural Networks , 2016 .

[5]  Geoffrey E. Hinton,et al.  Machine Learning for Aerial Image Labeling , 2013 .

[6]  Trevor Darrell,et al.  DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition , 2013, ICML.

[7]  A. Orth,et al.  The lethal effects of Cyperus iria on Aedes aegypti. , 1998, Journal of the American Mosquito Control Association.

[8]  Trevor Darrell,et al.  Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[10]  F. Castaldi,et al.  Assessing the potential of images from unmanned aerial vehicles (UAV) to support herbicide patch spraying in maize , 2017, Precision Agriculture.

[11]  Xiuwen Liu,et al.  A patch-based convolutional neural network for remote sensing image classification , 2017, Neural Networks.

[12]  Chen Zhiqiang.  Breeding and application of high-quality and diseaseresistant rice variety, huahang No.31 , 2013 .

[13]  Li Fei-Fei,et al.  ImageNet: A large-scale hierarchical image database , 2009, CVPR.

[14]  Pierre Alliez,et al.  Convolutional Neural Networks for Large-Scale Remote-Sensing Image Classification , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[15]  Xanthoula Eirini Pantazi,et al.  Application of Multilayer Perceptron with Automatic Relevance Determination on Weed Mapping Using UAV Multispectral Imagery , 2017, Sensors.

[16]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[17]  Pedro Antonio Gutiérrez,et al.  A semi-supervised system for weed mapping in sunflower crops using unmanned aerial vehicles and a crop row detection method , 2015, Appl. Soft Comput..

[18]  F. López-Granados,et al.  Weed Mapping in Early-Season Maize Fields Using Object-Based Analysis of Unmanned Aerial Vehicle (UAV) Images , 2013, PloS one.

[19]  F. López-Granados,et al.  Early season weed mapping in sunflower using UAV technology: variability of herbicide treatment maps against weed thresholds , 2016, Precision Agriculture.

[20]  Jamie Sherrah,et al.  Fully Convolutional Networks for Dense Semantic Labelling of High-Resolution Aerial Imagery , 2016, ArXiv.

[21]  Farid Melgani,et al.  A Convolutional Neural Network Approach for Assisting Avalanche Search and Rescue Operations with UAV Imagery , 2017, Remote. Sens..

[22]  Gui-Song Xia,et al.  Transferring Deep Convolutional Neural Networks for the Scene Classification of High-Resolution Remote Sensing Imagery , 2015, Remote. Sens..

[23]  Yubin Lan,et al.  Review: Current status and future directions of precision aerial application for site-specific crop management in the USA , 2010 .

[24]  Francisca López-Granados,et al.  Airborne multi-spectral imagery for mapping cruciferous weeds in cereal and legume crops , 2011, Precision Agriculture.

[25]  Gabriela Csurka,et al.  What is a good evaluation measure for semantic segmentation? , 2013, BMVC.

[26]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[27]  Lang Pan,et al.  Mechanism of resistance to cyhalofop-butyl in Chinese sprangletop (Leptochloa chinensis (L.) Nees). , 2017, Pesticide biochemistry and physiology.

[28]  Gang Fu,et al.  Classification for High Resolution Remote Sensing Imagery Using a Fully Convolutional Network , 2017, Remote. Sens..

[29]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[30]  C. H. Koger,et al.  Detecting Late-Season Weed Infestations in Soybean (Glycine max)1 , 2003, Weed Technology.

[31]  Schwartz Am,et al.  The lethal effects of Cyperus iria on Aedes aegypti , 1998 .

[32]  Lei Wang,et al.  HEp-2 Cell Image Classification With Deep Convolutional Neural Networks , 2015, IEEE Journal of Biomedical and Health Informatics.