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Abstract: Abstract: Deep learning (DL) is a kind of sophisticated data analysis and image processing technology, with good results and great potential. DL has been applied to many different fields, and it is also being applied to the agricultural field. Deep learning algorithms have revolutionized various industries, including agriculture, by enabling advanced image processing and accurate predictions. This paper explores a wide-ranging review of research papers and articles on deep learning algorithms for image processing and predictions in the field of agriculture. It analyzed works on an overview of convolutional neural networks (CNNs), recurrent neural networks (RNNs), and their variants, highlighting their applications in crop yield estimation, disease detection; weed identification, and irrigation management. Additionally, the paper discusses the challenges and potential future directions of deep learning algorithms in agriculture.