Bioscience Method 2024, Vol.15, No.2, 58-65 http://bioscipublisher.com/index.php/bm 58 Research Report Open Access Technological Innovation in Disease Detection and Management in Sugarcane Planting Ameng Li CRO Service Station, Sanya Tihitar SciTech Breeding Service Inc., Sanya, 572025, Hainan, China Corresponding email: ameng.li@hitar.org Bioscience Method, 2024, Vol.15, No.2 doi: 10.5376/bm.2024.15.0007 Received: 15 Jan., 2024 Accepted: 25 Feb., 2024 Published: 15 Mar., 2024 Copyright © 2024 Li, This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Preferred citation for this article: Li A.M., 2024, Technological innovation in disease detection and management in sugarcane planting, Bioscience Method, 15(2): 58-65 (doi: 10.5376/bm.2024.15.0007) 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. Keywords Sugarcane cultivation; Disease detection; Machine learning; Remote sensing; Molecular diagnostics; Genetic engineering; Sustainable agriculture Sugarcane is a critical agricultural commodity with a significant role in the global economy, providing raw material for sugar production and biofuels, among other products. The cultivation of sugarcane is not without challenges, particularly in the form of diseases that can severely impact yield and quality. Common diseases affecting sugarcane include those caused by fungi, bacteria, viruses, and phytoplasmas, which can lead to substantial economic losses. The traditional methods for disease detection, which often rely on visual inspection, are labor-intensive and can be inaccurate. Moreover, the asymptomatic nature of some diseases makes early detection difficult, necessitating more advanced and reliable methods. Technological innovations in disease detection and management are therefore essential to sustain and improve sugarcane production. Recent advancements in machine learning and image processing techniques have shown promise in addressing these challenges. Support vector machines (SVM) and machine vision technology have been utilized to detect sugarcane borer diseases with high accuracy, demonstrating the potential of these methods to replace laborious manual selection processes. Similarly, machine learning classifiers applied to multispectral images from unmanned aerial vehicles (UAVs) have been effective in detecting white leaf disease in sugarcane, even at early stages (Narmilan et al., 2022). Deep learning frameworks have also been explored for their ability to identify diseased sugarcane plants by analyzing leaf and stem characteristics, with some models achieving high levels of accuracy (Srivastava et al., 2020). The Internet of Things (IoT) has been integrated into agricultural practices, enabling targeted disease management and reducing the environmental impact of excessive pesticide use (Thangadurai et al.,
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