Progresses in Sugarcane Leaf Defect Identification: A Review

Sheshang Degadwala and Dhrumil Dave

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2024, vol. 10, issue 5, 01-11

Abstract: Sugarcane is a crucial crop for the global sugar industry, but its yield and quality can be significantly impacted by various leaf defects. Accurate and timely identification of these defects is essential for effective pest management and crop improvement. This review paper explores recent advancements in sugarcane leaf defect identification, focusing on technological progress and methodological innovations. The study covers traditional techniques, such as visual inspections and manual identification, and examines how modern approaches, including machine learning, computer vision, and remote sensing, have transformed the field. Recent progress in image processing technologies and the development of automated systems have greatly enhanced the accuracy and efficiency of defect detection. Despite these advancements, challenges remain, including variability in defect appearance, the need for large annotated datasets, and the integration of detection systems into practical agricultural practices. The review also discusses the impact of these technologies on improving disease management, optimizing yield, and supporting sustainable farming practices. By highlighting current trends and future directions, this paper aims to provide a comprehensive understanding of the state-of-the-art methods in sugarcane leaf defect identification and their implications for the agricultural industry.

Keywords: Sugarcane; Leaf Defect Identification; Image Processing; Machine Learning; Computer Vision; Remote Sensing; Pest Management (search for similar items in EconPapers)
Date: 2024
Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT2410581
References: Add references at CitEc
Citations:

Downloads: (external link)
https://ijsrcseit.com/home/article/view/CSEIT2410581 Article URL (text/html)
https://ijsrcseit.com/home/article/download/CSEIT2410581/CSEIT2410581 Full text (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:jbh:ijsrcs:v10:y2024:i5:id:288

DOI: 10.32628/CSEIT2410581

Access Statistics for this article

More articles in International Journal of Scientific Research in Computer Science, Engineering and Information Technology from International Journal of Scientific Research in Computer Science, Engineering and Information Technology
Bibliographic data for series maintained by Pankaj Sharma (USA) ().