A Short Review on Deep Learning in Agriculture

Abstract

Agriculture is a vital part of the global economy, and recent breakthroughs in deep learning technology are showing great potential to revolutionize this industry. This paper takes a close look at the expanding research on how deep learning and machine learning are being used to tackle agricultural challenges, especially through image processing and computer vision. The paper explores various deep neural network architectures and machine learning methods currently being used in agriculture, offering a summary of the latest advancements. It highlights the wide range of applications for deep learning in this field, such as managing irrigation systems, detecting weeds, recognizing patterns, and identifying crop diseases. In addition, the paper delves into the specific deep learning models utilized, the data sources they rely on, the metrics used to measure their performance, and the hardware that supports these technologies. It also considers the potential for real-time applications, particularly with autonomous robotic platforms. The survey underscores that deep learning techniques significantly outperform traditional machine learning methods in terms of accuracy, showcasing their superior ability to enhance agricultural practices.