LGN-YOLO: A Leaf-Oriented Region-of-Interest Generation Method for Cotton Top Buds in Fields

Yufei Xie and Liping Chen ()
Additional contact information
Yufei Xie: College of Information Engineering, Tarim University, Alaer 843300, China
Liping Chen: College of Information Engineering, Tarim University, Alaer 843300, China

Agriculture, 2025, vol. 15, issue 12, 1-22

Abstract: As small-sized targets, cotton top buds pose challenges for traditional full-image search methods, leading to high sparsity in the feature matrix and resulting in problems such as slow detection speeds and wasted computational resources. Therefore, it is difficult to meet the dual requirements of real-time performance and accuracy for field automatic topping operations. To address the low feature density and redundant information in traditional full-image search methods for small cotton top buds, this study proposes LGN-YOLO, a leaf-morphology-based region-of-interest (ROI) generation network based on an improved version of YOLOv11n. The network leverages young-leaf features around top buds to determine their approximate distribution area and integrates linear programming in the detection head to model the spatial relationship between young leaves and top buds. Experiments show that it achieves a detection accuracy of over 90% for young cotton leaves in the field and can accurately identify the morphology of young leaves. The ROI generation accuracy reaches 63.7%, and the search range compression ratio exceeds 90%, suggesting that the model possesses a strong capability to integrate target features and that the output ROI retains relatively complete top-bud feature information. The ROI generation speed reaches 138.2 frames per second, meeting the real-time requirements of automated topping equipment. Using the ROI output by this method as the detection region can address the problem of feature sparsity in small targets during traditional detection, achieve pre-detection region optimization, and thus reduce the cost of mining detailed features.

Keywords: young leaf; ROI; cotton topping; cotton top bud; detection algorithm; YOLO (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/15/12/1254/pdf (application/pdf)
https://www.mdpi.com/2077-0472/15/12/1254/ (text/html)

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:gam:jagris:v:15:y:2025:i:12:p:1254-:d:1675535

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().