Harnessing image processing for precision disease diagnosis in sugar beet agriculture

Abstract

Sugar beet, a sugar crop, faces a persistent threat from foliar and root diseases, leading to substantial yield losses. Traditional methods of disease identification and severity assessment are often time-consuming, error-prone, and impractical, particularly in large production areas. In response to this challenge, researchers have recently turned to innovative solutions involving image processing and machine learning techniques for efficient disease detection in sugar beet plants. Image processing technology has emerged as a rapid and precise disease identification technology in sugar beet. By capitalizing on the ability of image processing to differentiate coloured objects, this approach facilitates the accurate determination of disease severity, enabling timely intervention measures. The urgency of developing faster and more practical methods becomes evident, highlighting the need to decrease human errors in identifying plant diseases and assessing their severity and progression. This review showcases the potential of image processing technology in revolutionizing disease detection strategies for sugar beet crops. The ability to swiftly and accurately determine disease outbreak, severity, and progression addresses a critical gap in current agricultural practices. Image processing technology holds promise as a practical and efficient solution for large-scale disease management in sugar beet cultivation, paving the way for sustainable and high-yield sugar production.