Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{199684,
        author = {K Shankar and A Mohan and J Shiva Sai and Mohd Ameenuddin Ahmed},
        title = {YOLO Based Lightweight Deep Learning Framework for Fine-grained Cotton Disease and Pest Detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {15305-15311},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=199684},
        abstract = {Cotton is one of the most important cash crops in the world. However, the crop undergoes a significant amount of yield loss (more than 30%) due to undiagnosed diseases and small pests like armyworms and aphids. Though the YOLO algorithm was successfully implemented in solving the problem of pest detection using deep learning techniques, the algorithm does not work well in the presence of real-world conditions. There are also "Tiny Object Deficiency" problems because of the removal of small objects during down sampling of features and "Field Robustness Gap" problems because of the presence of clutter and illumination conditions. This paper aims at presenting an extensive literature survey on the recent advancements in lightweight deep learning techniques used in solving the problems of pest detection. The literature survey aims at filling the research gap in the field of pest detection using the combined power of advanced preprocessing techniques and architectures. To bridge the research gap in the field of pest detection using the combined power of advanced preprocessing techniques and architectures, a new framework called "YOLOv11-SR" is proposed based on the combined power of the YOLOv11 algorithm and the advanced preprocessing of RealESRGAN.},
        keywords = {Cotton Pest Detection, Tiny Object Detection, YOLOv11, Super Resolution, RealESRGAN, RepViT-A, LDConv, DDC.},
        month = {April},
        }