Microscopic imaging provides essential visual evidence for studying plant biology and pathology at the cellular and subcellular levels. However, existing benchmarks on vision-language models primarily focus on macroscopic plant imagery, while the microscopic domain remains underexplored. To address this gap, we present PlantMicro, a comprehensive benchmark for evaluating vision-language models (VLMs) in microscopic plant imagery. PlantMicro integrates more than 5,000 images collected across diverse hosts, biological domains, and imaging modalities. Building on this diversity, we design a set of complementary tasks that capture different facets of microscopic image understanding. To support these tasks, we construct over 9,000 VQA pairs that systematically evaluate the capabilities of VLMs. Experiments on PlantMicro show that current VLMs struggle with fine-grained recognition and biologically grounded reasoning. For example, GPT-5 achieves 34.93% accuracy on the pathogen classification task, which is only modestly above the random-guessing baseline. The results highlight a significant gap in current VLMs' ability to comprehend plant microscopic images. PlantMicro provides a standardized foundation for advancing VLMs toward reliable and comprehensive microscopy-level plant understanding.
From Macro to Micro: Benchmarking Microscopic Spatial Intelligence on Molecules via Vision-Language Models
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学北京校区人工智能学院）；Beijing Key Laboratory of Research on Large Models and Intelligent Governance（北京大型模型与智能治理研究重点实验室）；Engineering Research Center of Next-Generation Intelligent Search and Recommendation, MOE（下一代智能搜索与推荐工程研究中心）；Dept. of Comp. Sci. & Tech., Tsinghua University（清华大学计算机科学与技术系）；Institute for AI Industry Research (AIR), Tsinghua University（清华大学人工智能产业研究院）；SKL-ESPC & SEPKL-AERM, College of Environmental Sciences and Engineering, Peking University（环境科学与工程学院，北京大学）；MAIS, Institute of Automation, Chinese Academy of Sciences（自动化研究所，中国科学院）；DAMO Academy, Alibaba Group, Hangzhou, China（阿里巴巴集团 DAMO Academy，中国杭州）；Hupan Lab, Hangzhou, China（杭州实验室）
LeafNet: A Large-Scale Dataset and Comprehensive Benchmark for Foundational Vision-Language Understanding of Plant Diseases
School of Electrical Engineering, Korea University（韩国大学电气工程学院）；Department of Integrative Biology, The University of Texas at Austin（德克萨斯大学奥斯汀分校整合生物学系）；Department of Software Engineering, FPT University（FPT大学软件工程系）
Maize Seedling Detection Dataset (MSDD): A Curated High-Resolution RGB Dataset for Seedling Maize Detection and Benchmarking with YOLOv9, YOLO11, YOLOv12 and Faster-RCNN
Department of Electrical Engineering and Computer Science, University of Missouri（电气工程与计算机科学系，密苏里大学）
PlantSeg: A Large-Scale In-the-wild Dataset for Plant Disease Segmentation
The University of Queensland（昆士兰大学）；School of Electrical Engineering and Computer Science, The University of Queensland（昆士兰大学电气工程与计算机科学学院）；School of Agriculture and Food Sustainability, The University of Queensland（昆士兰大学农业与食品可持续发展学院）；CSIRO（联邦科学与工业研究组织）
Plant parasitic nematodes cause damage to crop plants on a global scale. Robust detection on image data is a prerequisite for monitoring such nematodes, as well as for many biological studies involving the nematode C. elegans, a common model organism. Here, we propose a framework for detecting worm-shaped objects in microscopic images that is based on convolutional neural networks (CNNs). We annotate nematodes with curved lines along the body, which is more suitable for worm-shaped objects than bounding boxes. The trained model predicts worm skeletons and body endpoints. The endpoints serve to…
One of the critical biotic stress factors paddy farmers face is diseases caused by bacteria, fungi, and other organisms. These diseases affect plants' health severely and lead to significant crop loss. Most of these diseases can be identified by regularly observing the leaves and stems under expert supervision. In a country with vast agricultural regions and limited crop protection experts, manual identification of paddy diseases is challenging. Thus, to add a solution to this problem, it is necessary to automate the disease identification process and provide easily accessible decision support…
Towards scalable organ level 3D plant segmentation: Bridging the data algorithm computing gap
Department of Biological and Environmental Engineering（生物与环境工程系）；Horticulture Section, School of Integrative Plant Science（植物科学整合学院园艺系）；School of Electrical and Computer Engineering（电气与计算机工程学院）
Cornell University（康奈尔大学）；Caltech（加州理工学院）；Google（谷歌）；University of Edinburgh（爱丁堡大学）
Recent progress in self-supervised learning has resulted in models that are capable of extracting rich representations from image collections without requiring any explicit label supervision. However, to date the vast majority of these approaches have restricted themselves to training on standard benchmark datasets such as ImageNet. We argue that fine-grained visual categorization problems, such as plant and animal species classification, provide an informative testbed for self-supervised learning. In order to facilitate progress in this area we present two new natural world visual classificat…
Institute of Imaging & Computer Vision, RWTH Aachen University（亚琛工业大学成像与计算机视觉研究所）；Julius Kühn Institute: Federal Research Centre for Cultivated Plants（尤利乌斯·库恩研究所（联邦作物研究中心））；LemnaTec GmbH（LemnaTec有限公司）
The beet cyst nematode (BCN) Heterodera schachtii is a plant pest responsible for crop loss on a global scale. Here, we introduce a high-throughput system based on computer vision that allows quantifying BCN infestation and characterizing nematode cysts through phenotyping. After recording microscopic images of soil extracts in a standardized setting, an instance segmentation algorithm serves to detect nematode cysts in these samples. Going beyond fast and precise cyst counting, the image-based approach enables quantification of cyst density and phenotyping of morphological features of cysts u…
University of Cambridge（剑桥大学）；ENGIE Lab CRIGEN（昂吉集团CRIGEN实验室）
A number of industrial applications, such as smart grids, power plant operation, hybrid system management or energy trading, could benefit from improved short-term solar forecasting, addressing the intermittent energy production from solar panels. However, current approaches to modelling the cloud cover dynamics from sky images still lack precision regarding the spatial configuration of clouds, their temporal dynamics and physical interactions with solar radiation. Benefiting from a growing number of large datasets, data driven methods are being developed to address these limitations with prom…
MambaMIC: An Efficient Baseline for Microscopic Image Classification with State Space Models
Nanjing Agricultural University（南京农业大学）；National University of Defense Technology（国防科技大学）；Xiangtan University（湘潭大学）；Nanjing University of Posts and Telecommunications（南京邮电大学）；Soochow University（苏州大学）
针对显微图像分类中全局建模与效率难以兼顾的问题，提出 MambaMIC 双分支状态空间骨干，通过局部卷积、全局 SSM 及特征调制交互模块实现五个数据集上的 SOTA 性能。
Vines-DB: An RGB image dataset for multi-species ornamental vine segmentation
Department of Plants, Soils and Climate, Utah State University（植物、土壤与气候系，犹他州立大学）；Department of Applied Sciences, Technology and Education, Utah State University（应用科学、科技与教育系，犹他州立大学）
This paper proposes a novel selective autoencoder approach within the framework of deep convolutional networks. The crux of the idea is to train a deep convolutional autoencoder to suppress undesired parts of an image frame while allowing the desired parts resulting in efficient object detection. The efficacy of the framework is demonstrated on a critical plant science problem. In the United States, approximately $1 billion is lost per annum due to a nematode infection on soybean plants. Currently, plant-pathologists rely on labor-intensive and time-consuming identification of Soybean Cyst Nem…
Global Rice Multi-Class Segmentation Dataset (RiceSEG): A Comprehensive and Diverse High-Resolution RGB-Annotated Images for the Development and Benchmarking of Rice Segmentation Algorithms
Nanjing Agricultural University（南京农业大学）；The University of Tokyo（东京大学）；Professor Jayashankar Telangana Agricultural State University（贾亚尚卡尔·特伦甘纳农业州立大学）；Kyoto University（京都大学）；Huazhong University of Science and Technology（华中科技大学）；Huazhong Agricultural University（华中农业大学）；Chinese Academy of Agricultural Sciences（中国农业科学院）；Chinese Academy of Science（中国科学院）；Shenyang Agricultural University（沈阳农业大学）；Jilin Academy of Agricultural Sciences（吉林省农业科学院）；Yuan Long Ping High-Tech Agriculture Co., Ltd.（袁隆平高科技农业有限公司）
