期刊
COMPUTERS AND ELECTRONICS IN AGRICULTURE
卷 147, 期 -, 页码 70-90出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.compag.2018.02.016
关键词
Deep learning; Agriculture; Survey; Convolutional Neural Networks; Recurrent Neural Networks; Smart fanning; Food systems
资金
- P-SPHERE project from the European Union's Horizon research and innovation programme under the Marie Skodowska-Curie grant [665919]
Deep learning constitutes a recent, modem technique for image processing and data analysis, with promising results and large potential. As deep learning has been successfully applied in various domains, it has recently entered also the domain of agriculture. In this paper, we perform a survey of 40 research efforts that employ deep learning techniques, applied to various agricultural and food production challenges. We examine the particular agricultural problems under study, the specific models and frameworks employed, the sources, nature and pre-processing of data used, and the overall performance achieved according to the metrics used at each work under study. Moreover, we study comparisons of deep learning with other existing popular techniques, in respect to differences in classification or regression performance. Our findings indicate that deep learning provides high accuracy, outperforming existing commonly used image processing techniques.
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