Deep Learning-Based Agriculture Monitoring and Forecasting

Abstract

Smart agriculture has become crucial in meeting the increasing dietary needs of a growing population, particularly in countries where agriculture has significant economic impact. Deep learning techniques, such as convolutional neural networks and recurrent neural networks, have been extensively researched and applied in agriculture in recent years. In this study, recent research articles on deep learning in agriculture over the past five years are analyzed to identify key contributions and challenges. The study has also explored agriculture parameters monitored by the internet of things and used them to train the deep learning algorithms for analysis. The study compares various factors across different studies, including the agriculture area of focus, dataset used, deep learning model and framework, data preprocessing and augmentation methods, and accuracy of results.