期刊
REMOTE SENSING
卷 11, 期 10, 页码 -出版社
MDPI
DOI: 10.3390/rs11101157
关键词
convolutional neural network; crop segmentation; Ficus carica; unmanned aerial vehicles
类别
资金
- Consejo Nacional de Ciencia y Tecnologia (CONACyT) of Mexico
- SEP-PRODEP [103.5/15/11069]
- Laboratorio Nacional de Supercomputo del Sureste de Mexico, CONACyT
Crop segmentation is an important task in Precision Agriculture, where the use of aerial robots with an on-board camera has contributed to the development of new solution alternatives. We address the problem of fig plant segmentation in top-view RGB (Red-Green-Blue) images of a crop grown under open-field difficult circumstances of complex lighting conditions and non-ideal crop maintenance practices defined by local farmers. We present a Convolutional Neural Network (CNN) with an encoder-decoder architecture that classifies each pixel as crop or non-crop using only raw colour images as input. Our approach achieves a mean accuracy of 93.85% despite the complexity of the background and a highly variable visual appearance of the leaves. We make available our CNN code to the research community, as well as the aerial image data set and a hand-made ground truth segmentation with pixel precision to facilitate the comparison among different algorithms.
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