APPLICATION OF DEEP LEARNING-BASED TECHNIQUES FOR PRECISION AGRICULTURE

Abstract

Agriculture is a vital industry that adds significantly to the global economy. Researchers are currently starting to investigate the prospect of integrating deep learning techniques and machine learning into agriculture, due to recent developments in technologies for deep learning. The paper examines several deep neural network designs and machine learning techniques used in agriculture, including irrigation, weeding, pattern recognition, and crop disease identification. The primary goal of this study is to determine multiple uses of deep learning in agriculture and to summarise existing state-of-the-art approaches. The review addresses the particular deep learning algorithms utilized, the sources of data used, study achievement, the equipment used, and the possibility for immediate application as well as integration with autonomous mechanical platforms. According to the results of the chapter, the use of deep learning research outperforms typical machine learning techniques in terms of reliability. In general, the study indicates the enormous potential of deep learning and machine learning in agriculture and the necessity for additional study in this field. It may be able to improve agricultural efficiency, decrease waste, and raise the yields of crops by utilizing the potential of these methods, ultimately enhancing the worldwide availability of food