外文摘要：In recent years, the agricultural sector has undergone a revolutionary shift toward "smart farming", integrating advanced technologies to strengthen crop health and productivity significantly. This paradigm shift holds profound implications for food safety and the broader economy. At the forefront of this transformation is deep learning, a subset of artificial intelligence based on artificial neural networks, has emerged as a powerful tool in detection and classification tasks. Specifically, Convolutional Neural Networks (CNNs), a specialized type of deep learning and computer vision models, demonstrated remarkable proficiency in analyzing crop imagery, whether sourced from satellites, aircraft, or terrestrial cameras. These networks often leverage vegetation indices and multispectral imagery to enhance their analytical capabilities. Such model contribute to the development of systems that could enhance agricultural. This review encapsulates the current state of the art in using CNNs in agriculture. It details the image types utilized within this context, including, but not limited to, multispectral images and vegetation indices. Furthermore, it catalogs accessible online datasets pertinent to this field. Collectively, this paper underscores the pivotal role of CNNs in agriculture and highlights the transformative impact of multispectral images in this rapidly evolving domain.
外文关键词：deep learning;vegetation index;multispectral images;Smart farming;Smart agriculture;agricultural datasets
作者：El Sakka, Mohammad;Mothe, Josiane;Ivanovici, Mihai
作者单位：Univ Toulouse;Transilvania Univ Brasov
期刊名称：EUROPEAN JOURNAL OF REMOTE SENSING
期刊影响因子：0.0
出版年份：2024
出版刊次：57(1)
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台