Abstract
The development and improvement to map agricultural land cover are major challenges for researchers. For the sustainable development of agronomics spatial information about agricultural practices plays a vital role. Remote sensing satellite imagery is a valuable aid in providing and understanding this spatial distribution of agricultural practices. The aim of this paper is to provide a better understanding of capabilities of satellite images for agricultural land cover mapping through the use of deep learning techniques. The global coverage , rich spectral and spatial information and repetitive nature of remote sensing(RS) data have made them effective tools for mapping crop extents and yield prediction. This paper explores wide ranging review of research papers and articles on deep learning algorithms for image processing and predictions in the field of agriculture. The DL algorithms has attained remarkable success in different fields of RS and its use in crop monitoring. This review systematically identified 40 research papers from peer reviewed scientific publications related to sensors, platforms, input features, training data, spatial distribution of study sites. This article provides a concise summary of major DL algorithms, including concepts, limitations, implementation, to help researchers in agriculture to gain a holistic picture of major DL techniques quickly.
Published Version
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