Agriculture-Vision: A Large Aerial Image Database for Agricultural Pattern Analysis
UIUC（伊利诺伊大学厄巴纳-香槟分校）；Intelinair；University of Oregon（俄勒冈大学）
Mang Tik Chiu, Xingqian Xu, Yunchao Wei, Zilong Huang, Alexander Schwing, Robert Brunner, Hrant Khachatrian, Hovnatan Karapetyan, Ivan Dozier, Greg Rose, David … 展开作者
Mang Tik Chiu, Xingqian Xu, Yunchao Wei, Zilong Huang, Alexander Schwing, Robert Brunner, Hrant Khachatrian, Hovnatan Karapetyan, Ivan Dozier, Greg Rose, David Wilson, Adrian Tudor, Naira Hovakimyan, Thomas S. Huang, Honghui Shi
The success of deep learning in visual recognition tasks has driven advancements in multiple fields of research. Particularly, increasing attention has been drawn towards its application in agriculture. Nevertheless, while visual pattern recognition on farmlands carries enormous economic values, little progress has been made to merge computer vision and crop sciences due to the lack of suitable agricultural image datasets. Meanwhile, problems in agriculture also pose new challenges in computer vision. For example, semantic segmentation of aerial farmland images requires inference over extremely large-size images with extreme annotation sparsity. These challenges are not present in most of the common object datasets, and we show that they are more challenging than many other aerial image datasets. To encourage research in computer vision for agriculture, we present Agriculture-Vision: a large-scale aerial farmland image dataset for semantic segmentation of agricultural patterns. We collected 94,986 high-quality aerial images from 3,432 farmlands across the US, where each image consists of RGB and Near-infrared (NIR) channels with resolution as high as 10 cm per pixel. We annotate nine types of field anomaly patterns that are most important to farmers. As a pilot study of aerial agricultural semantic segmentation, we perform comprehensive experiments using popular semantic segmentation models; we also propose an effective model designed for aerial agricultural pattern recognition. Our experiments demonstrate several challenges Agriculture-Vision poses to both the computer vision and agriculture communities. Future versions of this dataset will include even more aerial images, anomaly patterns and image channels. More information at https://www.agriculture-vision.com.
The 1st Agriculture-Vision Challenge: Methods and Results
UIUC（伊利诺伊大学厄巴纳-香槟分校）；Intelinair；University of Oregon（俄勒冈大学）；Agency for Defense Development（国防发展局）；DGIST（大邱庆北科学技术院）；Norwegian Computing Center（挪威计算中心）；UiT The Arctic University of Norway（特罗姆瑟大学－挪威北极大学）；Tongji University（同济大学）；Stanford University（斯坦福大学）；Chegg, Inc.（Chegg公司）；Chonnam National University（全南大学）；Chosun University（朝鲜大学）；SGGS Institute of Engineering and Technology（SGGS工程技术学院）；Tsinghua University（清华大学）
AgroTools: A Benchmark for Tool-Augmented Multimodal Agents in Agriculture
Sun Yat-Sen University（中山大学）；Southwest University（西南大学）；Northeastern University（东北大学）；National Supercomputing Center in Shenzhen（深圳国家超算中心）；Southwest Jiaotong University（西南交通大学）；China Agricultural University（中国农业大学）；Tsinghua University（清华大学）
AgroGPT: Efficient Agricultural Vision-Language Model with Expert Tuning
Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Georgia Institute of Technology（佐治亚理工学院）；Johns Hopkins University（约翰斯·霍普金斯大学）
Extended Agriculture-Vision: An Extension of a Large Aerial Image Dataset for Agricultural Pattern Analysis
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Intelinair
对于遥感和地球观测数据的大部分机器学习工作来说，一个关键的挑战是难以获得大量准确标记的数据。这对于语义分割任务来说尤其如此，因为在大规模收集精确、准确的像素级注释方面存在令人难以置信的困难，所以在遥感领域中不太常见。最近的努力通过创建监督数据集以及应用自我监督方法来解决这些挑战。我们继续在这两方面作出努力。首先，我们生成并发布了一个改进版本的视觉数据集（Chiu et al.，2020年b），以包括原始的，全视野的图像更大的实验灵活性。其次，我们扩展了这个数据集，发布了3600个大的，高分辨率（10厘米/像素），全场，红-绿-蓝和近红外图像进行预训练。第三，我们将最初构建在Simplified框架上的Pixel-to-Propagation Module Xie et al.（2021 b）并入MoCo-V2 Chen et al.（2020年b）。最后，我们通过对下游分类和语义分割任务的不同对比学习方法进行基准测试，证明了这些数据的有用性。我们在基于MoCo-V2的不同框架内探索了CNN和Swin Transformer Liu等人（2021 a）的架构。总之，这些方法使我们能够更好地从航空图像中检测整个领域的关键农业模式，以便农民可以及时提醒有问题的地区，为他们的管理决策提供信息。此外，这些数据集的发布将支持农业遥感中计算机视觉的多种研究途径。
AgroBench: Vision-Language Model Benchmark in Agriculture
The University of Osaka（大阪大学）；Kyoto University（京都大学）；Tokyo Institute of Technology（东京工业大学）；National Institute of Advanced Industrial Science and Technology (AIST)（日本产业技术综合研究所 (AIST)）；University of Oxford（牛津大学）；OMRON SINIC X（欧姆龙思尼克斯）
Concept of a Sensor Test Environment for Dusty Agricultural Conditions
Technical University of Munich（慕尼黑工业大学）；Munich Institute of Robotics and Machine Intelligence (MIRMI)（慕尼黑机器人与机器智能研究所（MIRMI））；Baden-Wuerttemberg Cooperative State University (DHBW) Ravensburg（巴登-符腾堡州合作教育大学（DHBW）拉芬斯堡分校）
AgriDoctor: A Multimodal Intelligent Assistant for Agriculture
State Key Laboratory of Multimodal Artificial Intelligence Systems（多模态人工智能系统国家重点实验室）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
Generative Adversarial Networks for Image Augmentation in Agriculture: A Systematic Review
Mississippi State University（密西西比州立大学）；Michigan State University（密歇根州立大学）；United States Department of Agriculture, Agricultural Research Service（美国农业部农业研究局）
Large Language Models and Foundation Models in Smart Agriculture: Basics, Opportunities, and Challenges
Michigan State University（密歇根州立大学）；Jeonbuk National University（国立全北大学）；North Carolina State University（北卡罗来纳州立大学）；Southeast University（东南大学）；Nanyang Technological University（南洋理工大学）
EcoWeedNet: A Lightweight and Automated Weed Detection Method for Sustainable Next-Generation Agricultural Consumer Electronics
Computer Engineering Department, King Fahd University of Petroleum and Minerals（国王法赫德石油与矿物大学计算机工程系）；SDAIA-KFUPM Joint Research Center on Artificial Intelligence, IRC for Intelligent Secure Systems and Department of Computer Engineering（人工智能联合研究中心、智能安全系统研究所及计算机工程系）
Agri-LLaVA: Knowledge-Infused Large Multimodal Assistant on Agricultural Pests and Diseases
China Three Gorges University（三峡大学）；College of Computer and Information Technology, China Three Gorges University（三峡大学计算机与信息学院）；University of Birmingham（伯明翰大学）；Southern University of Science and Technology（南方科技大学）
VLLFL: A Vision-Language Model Based Lightweight Federated Learning Framework for Smart Agriculture
Department of Electrical and Computer Engineering, The University of Alabama（电气与计算机工程系，阿拉巴马大学）；Electrical and Computer Engineering, Michigan State University（电气与计算机工程，密歇根州立大学）；Agricultural and Biological Engineering, Mississippi State University（农业与生物工程，密苏里州立大学）；Department of Computer Science, University of Alabama（计算机科学系，阿拉巴马大学）；USDA-ARS Genetics and Sustainbale Agriculture（美国农业部ARS基因与可持续农业）
