期刊
COMPUTERS AND ELECTRONICS IN AGRICULTURE
卷 174, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.compag.2020.105450
关键词
Weed recognition; Graph convolutional network; Deep learning; Semi-supervised learning; Smart farming
资金
- Major Scientific and Technological Innovation Project of Shandong Province [2019JZZY010716]
- Major Agricultural Application Technology Innovation Project of Shandong Province [SD2019NJ001]
- Shandong Key RD Program [2015GNC112004]
- National Key RD Program [2017YFD0700500]
- Shandong Natural Science Fund Project [ZR2018MC017]
Weeding is an effective way to increase crop yields. Reliable and accurate weed recognition is a prerequisite for achieving high-precision site-specific weed control in precision agriculture. To improve weed and crop recognition accuracy, a CNN feature based graph convolutional network (GCN) based approach is proposed. A GCN graph was constructed based on extracted weed CNN features and their Euclidean distances. Based on the semi-supervised learning, the GCN graph enriched the model by exploiting labeled and unlabeled image features, and testing samples obtain label information from labeled weed data by performing propagation over the graph. The proposed GCN-ResNet-101 approach achieved 97.80%, 99.37%, 98.93% and 96.51% recognition accuracies on four different weed datasets respectively, which outperformed the state-of-the-art methods (AlexNet, VGG16 and ResNet-101). Additionally, the runtime of the proposed approach also satisfies the real-time requirement of field weed control. The proposed CNN feature based GCN approach is favorable for multi-class crops and weeds recognition with limited labeled data, which is a promising approach in dealing with similar agricultural recognition tasks. Furthermore, the used datasets and source code are publicly available to facilitate the research in the recognition of field weeds.
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