Applications of smart agriculture for environmental protection using deep learning techniques

Abstract

DL, short for Deep Learning, is a cutting-edge approach that merges advanced techniques in image processing and data analysis with the power of big data analysis. Its potential is enormous and has already found practical applications in several fields, including autonomous driving, automatic speech recognition, medical research, image restoration, natural language processing, and, among others. DL has been recently introduced in agriculture showing promising results in solving various farming problems like disease detection, automated plant and fruit identification, and counting. This study presents a comprehensive review of research using DL techniques in farming, including crop monitoring, crop mapping, weed and pest detection and management, irrigation, fruit grading, reorganizations of species and herbicide identification. Furthermore, different DL techniques applied in various fields are analyzed and compared with existing techniques. It was found that DL outperforms traditional image processing technology in terms of accuracy, both in classification and regression. Additionally, the study suggests that DL can be applied beyond detections, classification tasks to yield production, and disease segmentation in agriculture.