DEEP CONVOLUTIONAL NEURAL NETWORKS FOR WEED DETECTION IN AGRICULTURAL CROPS USING OPTICAL AERIAL IMAGES

被引：0

作者：

Ramirez, W. ^{[1
]}

Achanccaray, P. ^{[1
]}

Mendoza, L. F. ^{[1
]}

Pacheco, M. A. C. ^{[1
]}

机构：

[1] Pontifical Catholic Univ Rio de Janeiro, Appl Computat Intelligence Lab, Rio De Janeiro, Brazil

来源：

2020 IEEE LATIN AMERICAN GRSS & ISPRS REMOTE SENSING CONFERENCE (LAGIRS) | 2020年

关键词：

Semantic Segmentation; Remote Sensing; Deep Neural Network; Precision Farming;

D O I：

10.1109/lagirs48042.2020.9165562

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The presence of weeds in agricultural crops has been one of the problems of greatest interest in recent years as they consume natural resources and negatively affect the agricultural process. For this purpose, a model has been implemented to segment weed in aerial images, The proposed model relies on DeepLabv3 architecture trained upon patches extracted from high-resolution aerial imagery. The dataset employed consisted in 5 high resolutionimages that describes a sugar beet agricultural field in Germany. SegNet and U-Net architectures were selected for comparison purposes. Our results demonstrate that balancing of data, together with a greater spatial context leads better results with DeepLabv3 achieving up to 0.89 and 0.81 in terms of AUC and F1-score, respectively.

引用

页码：133 / 137

页数：5

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