Deep Learning Advancements in Agriculture: A Survey

Deep neural technology has a significant contribution to the invention of various systems to enhance crop farming by preventing damage caused by various crop diseases. This study explores the implementation of deep learning in the agricultural domain within some aspects: 1. Importance of deep learning in agriculture, 2. Method- ology and Aspects, and 3. Evaluation Matrices. In order to train convolution neural networks (CNN), databases are required, whereas evaluation matrices evaluate the architecture performance to check the effectiveness of the databases. This paper effectively explains all the databases and methods utilized in deep learning for crop disease identification and crop classification. This research also mentions the future aspects of deep neural methods to implement in the crop health monitoring system.