Deep Learning Methods Used in Precision Agriculture

Vanke Meisha Academy, Shenzhen 518085, China

^* Corresponding author: randy080303@outlook.com

Abstract

Precision agriculture is an important field that aims to optimize crop yields and quality, and it is crucial for ensuring food security and sustainability. With the increasing demand for food and the limited resources available for farming, precision agriculture has become more important than ever. In this paper, we will focus on the application of deep learning techniques, such as CNN and RNN models, in precision agriculture. Our primary objectives are to provide a comprehensive overview of artificial neural networks (ANNs), including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), and to explore their applications in precision agriculture. Deep learning has shown great potential in accurately predicting crop yields and quality, and in optimizing the use of resources in farming. By achieving these objectives, we aim to contribute to the growing body of literature on the applications of deep learning in agriculture and to inspire further research in this promising field.