Publications

@article{sapkota_object_2026,

title = {Object detection with multimodal large vision-language models: An in-depth review},

author = {Ranjan Sapkota and Manoj Karkee},

url = {https://www.sciencedirect.com/science/article/pii/S1566253525006475},

doi = {10.1016/j.inffus.2025.103575},

issn = {1566-2535},

year  = {2026},

date = {2026-02-01},

urldate = {2026-02-01},

journal = {Information Fusion},

volume = {126},

pages = {103575},

abstract = {The fusion of language and vision in large vision-language models (LVLMs) has revolutionized deep learning-based object detection by enhancing adaptability, contextual reasoning, and generalization beyond traditional architectures. This in-depth review presents a structured exploration of the state-of-the-art in LVLMs, systematically organized through a three-step research review process. First, we discuss the functioning of vision language models (VLMs) for object detection, describing how these models harness natural language processing (NLP) and computer vision (CV) techniques to revolutionize object detection and localization. We then explain the architectural innovations, training paradigms, and output flexibility of recent LVLMs for object detection, highlighting how they achieve advanced contextual understanding for object detection. The review thoroughly examines the approaches used in integration of visual and textual information, demonstrating the progress made in object detection using VLMs that facilitate more sophisticated object detection and localization strategies. Furthermore, this review presents comprehensive visualizations demonstrating LVLMs’ effectiveness in diverse scenarios including localization and segmentation, and then compares their real-time performance, adaptability, and complexity to traditional deep learning systems. Based on the review analysis, its is expected that LVLMs will soon meet or surpass the performance of conventional methods in object detection. However, because of the unique and complimentary characteristics of traditional deep learning approaches and LVLMS, it is anticipated that hybrid approaches integrating both types of object detection models will be utilized in the future to maximize the speed, reliability and robotiness of the systems. Moreover, the review also identifies a few major limitations of the current LVLM modes, proposes solutions to address those challenges, and presents a clear roadmap for the future advancement in this field. We conclude, based on this study, that the recent advancement in LVLMs have made and will continue to make a transformative impact on object detection and automated applications in the future.},

keywords = {Information fusion, Language and vision fusion, Large language models, Object detection, Vision-language models},

pubstate = {published},

tppubtype = {article}

}

@article{sapkota_multi-modal_2025,

title = {Multi-Modal LLMs in Agriculture: A Comprehensive Review},

author = {Ranjan Sapkota and Rizwan Qureshi and Muhammad Usman Hadi and Syed Zohaib Hassan and Ferhat Sadak and Maged Shoman and Muhammad Sajjad and Fayaz Ali Dharejo and Achyut Paudel and Jiajia Li and Zhichao Meng and John Shutske and Manoj Karkee},

url = {https://ieeexplore.ieee.org/document/11173627},

doi = {10.1109/TASE.2025.3612154},

issn = {1558-3783},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

journal = {IEEE Transactions on Automation Science and Engineering},

volume = {22},

pages = {22510\textendash22540},

abstract = {Given the rapid emergence and applications of Multi-Modal Large Language Models (MM-LLMs) across various scientific fields, insights regarding their applicability in agriculture are still only partially explored. This paper conducts an in-depth review of MM-LLMs in agriculture, focusing on understanding how MM-LLMs can be developed and implemented to optimize agricultural processes, increase efficiency, and reduce costs. Recent studies have explored the capabilities of MM-LLMs in agricultural information processing and decision-making. Despite these advancements, significant gaps persist, particularly in addressing domain-specific challenges such as variable data quality and availability, integration with existing agricultural systems, and the creation of robust training datasets that accurately represent complex agricultural environments. Moreover, a comprehensive understanding of the capabilities, challenges, and limitations of MM-LLMs in agricultural information processing and application is still missing. Exploring these areas is crucial to providing the community with a broader perspective and a clearer understanding of MM-LLMs’ applications, establishing a benchmark for the current state and emerging trends in this field. To bridge this gap, this survey reviews the progress of MM-LLMs and their utilization in agriculture, with an additional focus on 11 key research questions (RQs), where 4 RQs are general and 7 RQs are agriculture focused. By addressing these RQs, this review outlines the current opportunities and challenges, limitations, and future roadmap for MM-LLMs in agriculture. The findings indicate that multi-modal MM-LLMs not only simplify complex agricultural challenges but also significantly enhance decision-making and improve the efficiency of agricultural image processing. These advancements position MM-LLMs as an essential tool for the future of farming. For continued research and understanding, an organized and regularly updated list of papers on MM-LLMs is available at https://github.com/JiajiaLi04/Multi-Modal-LLMs-in-Agriculture Note to Practitioners\textemdashMotivated by the need to optimize agricultural practices, this paper investigates the use of Large Language Models (MM-LLMs) to improve efficiency and decision-making in agriculture. We delve into critical RQs to reveal the capabilities and challenges of MM-LLMs, and their potential applications in the agricultural sector. Looking ahead, our findings suggest a promising future for the integration of MM-LLMs in agriculture, potentially revolutionizing how we manage and operate farms.},

keywords = {Agriculture, Analytical models, ChatGPT, Computational modeling, Computer vision, Data models, Deep learning, Farming, generative artificial intelligence, Hidden Markov models, Large language models (LLMs), Machine Learning, Precision agriculture, Reviews, Training, Transformers, Translation, Vision-language models},

pubstate = {published},

tppubtype = {article}

}