Vision-Language Models in Agriculture: A Review of Multimodal Reasoning for Crop Management

LITERATURE REVIEW: Vision-Language Models in Agriculture: A Review of Multimodal Reasoning for Crop Management ABSTRACT: The escalating challenge of global food security demands innovative technological solutions for sustainable and efficient agriculture. While traditional artificial intelligence has advanced agricultural image processing, it often fails to capture the deep semantic context necessary for complex decision-making. This review focuses on the transformative potential of Vision-Language Models (VLMs), which integrate visual perception with natural language understanding to enable sophisticated multimodal reasoning. By synthesizing recent literature, this review highlights key findings showing that VLMs are being successfully applied to critical crop management tasks including interactive disease diagnosis, temporal growth monitoring, farmer assistance Q&A systems, and embodied robotic actions. The development of domain-specific datasets (e.g., ALive, CDDM, AgriMM) and ecologically valid benchmarks (e.g., AGMMU, MIRAGE) is rapidly advancing the field. However, significant challenges persist, including the scarcity of high-quality data, high computational costs, model robustness, and practical deployment hurdles like the sim-to-real gap. The implications of VLM adoption are profound, promising to enhance decision-making, improve resource efficiency, and support site-specific management. Future research must prioritize improved domain adaptation, model efficiency for edge deployment, enhanced human-AI collaboration, and more advanced reasoning capabilities to realize the full potential of this technology. KEY CONTRIBUTIONS:- Comprehensive review of Vision-Language Model applications in agriculture- Analysis of VLM architectures and their advantages for agricultural AI- Survey of key applications: disease diagnosis, growth monitoring, farmer Q&A, robotics- Critical evaluation of current successes and challenges- Identification of future research priorities for agricultural VLMs KEYWORDS: Vision-Language Models, Multimodal AI, Precision Agriculture, Agricultural AI, Computer Vision, Natural Language Processing, Crop Management, Literature Review