Technological Innovation in Disease Detection and Management in Sugarcane Planting

Abstract

The objective of this study is to systematically examine recent technological innovations in disease detection and management within sugarcane cultivation. It seeks to identify key advancements in digital imaging, molecular diagnostics, and genetic engineering that have significantly improved the detection, monitoring, and control of sugarcane diseases, aiming to enhance overall crop health and productivity. This study identifies several crucial technologies that have reshaped disease management strategies in sugarcane cultivation. It highlights the effectiveness of machine learning algorithms and remote sensing technology in detecting and diagnosing plant diseases at early stages. Developments in molecular diagnostics have allowed for rapid and precise pathogen identification. Additionally, genetic engineering has contributed to the creation of disease-resistant sugarcane varieties, thereby reducing dependency on chemical treatments. Integration of these technologies has led to improved disease surveillance and management, resulting in healthier crops and increased yields. The convergence of machine learning, remote sensing, molecular diagnostics, and genetic engineering represents a transformative shift in managing sugarcane diseases. These technologies not only enhance the ability to detect and manage diseases more efficiently but also contribute to sustainable agriculture practices by reducing chemical use and improving crop resilience. Continued innovation and integration of these technologies hold the promise of further gains in productivity and sustainability in sugarcane agriculture.