DepthCropSeg++: Scaling a Crop Segmentation Foundation Model With Depth-Labeled Data
MetaPheno Laboratory（MetaPheno实验室）；PhenoTrait Technology Co., Ltd.（PhenoTrait技术有限公司）；Wuhan Digital Engineering Institute（武汉数字工程研究院）；National Key Laboratory of Multispectral Information Intelligent Processing Technology（国家多光谱信息智能处理技术重点实验室）；School of Artificial Intelligence and Automation, Huazhong University of Science and Technology（华中科技大学人工智能与自动化学院）；Hubei Key Laboratory of Intelligent Robot（湖北省智能机器人重点实验室）；School of Computer Science and Engineering, School of Artificial Intelligence, Wuhan Institute of Technology（武汉理工大学计算机科学与工程学院、人工智能学院）
DepthCropSeg++: a foundation model for crop segmentation, capable of segmenting different crop species under open in-field environment. Crop segmentation is a fundamental task for modern agriculture, which closely relates to many downstream tasks such as plant phenotyping, density estimation, and weed control. In the era of foundation models, a number of generic large language and vision models have been developed. These models have demonstrated remarkable real world generalization due to significant model capacity and largescale datasets. However, current crop segmentation models mostly learn from limited data due to expensive pixel-level labelling cost, often performing well only under specific crop types or controlled environment. In this work, we follow the vein of our previous work DepthCropSeg, an almost unsupervised approach to crop segmentation, to scale up a cross-species and crossscene crop segmentation dataset, with 28,406 images across 30+ species and 15 environmental conditions. We also build upon a state-of-the-art semantic segmentation architecture ViT-Adapter architecture, enhance it with dynamic upsampling for improved detail awareness, and train the model with a two-stage selftraining pipeline. To systematically validate model performance, we conduct comprehensive experiments to justify the effectiveness and generalization capabilities across multiple crop datasets. Results demonstrate that DepthCropSeg++ achieves 93.11% mIoU on a comprehensive testing set, outperforming both supervised baselines and general-purpose vision foundation models like Segmentation Anything Model (SAM) by significant margins (+0.36% and +48.57% respectively). The model particularly excels in challenging scenarios including night-time environment (86.90% mIoU), high-density canopies (90.09% mIoU), and unseen crop varieties (90.09% mIoU), indicating a new state of the art for crop segmentation.
Pseudo-Labeling by Multi-Policy Viewfinder Network for Image Cropping
Huazhong University of Science and Technology（华中科技大学）；School of Artificial Intelligence and Automation（人工智能与自动化学院）；Key Laboratory of Image Processing and Intelligent Control, Ministry of Education（图像处理与智能控制教育部重点实验室）
Productive Crop Field Detection: A New Dataset and Deep Learning Benchmark Results
Federal University of São Carlos（圣卡洛斯联邦大学）；John Deere（约翰迪尔）
在精准农业中，检测高产农田是一项必不可少的实践，使农民能够单独评估操作性能，并比较不同的种子品种，农药和肥料。然而，手动识别生产领域往往是一项耗时且容易出错的任务。以前的研究探索了使用先进的机器学习算法检测农田的不同方法，但它们通常缺乏高质量的标记数据。在这种情况下，我们提出了一个高质量的数据集，该数据集由机器操作生成，并与随时间推移跟踪的Sentinel-2图像相结合。据我们所知，这是第一个使用该技术来克服缺乏标记样本的问题。接下来，我们应用未标记数据的半监督分类和最先进的监督和自监督深度学习方法来自动检测高产农田。最后，结果证明了正未标记学习的高准确性，这完全符合我们对正样本具有高置信度的问题。考虑到存在精确的数据集和对比学习（考虑到我们没有全面的标记数据集可用的情况），在Triplet Loss Siamese中发现了最佳性能。
A Sensor Agnostic Domain Generalization Framework for Leveraging Geospatial Foundation Models: Enhancing Semantic Segmentation viaSynergistic Pseudo-Labeling and Generative Learning
University of Houston（休斯敦大学）；Purdue University（普渡大学）
Multi-Year Vector Dynamic Time Warping Based Crop Mapping
Scientific and Technological Research Council of Turkey (TÜBİTAK) Space Technologies Research Institute (UZAY)（土耳其科学技术研究理事会（TÜBİTAK）空间技术研究所）；Informatics Institute, Middle East Technical University（中东技术大学信息学研究所）
Benchmarking foundation models for hyperspectral image classification: Application to cereal crop type mapping
Analytics Lab (A-Lab), UM6P, Rabat 11103, Morocco（安利实验室（A-Lab））；Center for Remote Sensing Applications (CRSA), UM6P, Ben Guerir 43150, Morocco（远程传感应用中心（CRSA））；Friedrich Schiller University Jena, Department of Geography, Jena 07743, Germany（耶拿弗里德里希·施勒格尔大学）；Dept. of Cartography（制图部门）
Benchmarking for Practice: Few-Shot Time-Series Crop-Type Classification on the EuroCropsML Dataset
Technical University of Munich (TUM)（慕尼黑技术大学）；TUM School of Engineering and Design（慕尼黑技术大学工程与设计学院）；Department of Aerospace and Geodesy（航空航天与大地测量系）；Chair of Remote Sensing Technology（遥感技术教授团）；Munich Data Science Institute (MDSI)（慕尼黑数据科学研究所）；ELLIS Unit Jena（耶纳ELLIS单元）；University of Jena（耶纳大学）；ETH Zurich（苏黎世联邦理工学院）；Department of Mathematics（数学系）；Zuse Institute Berlin（柏林Zuse研究所）
On Domain-Specific Pre-Training for Effective Semantic Perception in Agricultural Robotics
University of Bonn（波恩大学）；University of Oxford（牛津大学）；Lamarr Institute for Machine Learning and Artificial Intelligence（拉马尔机器学习与人工智能研究所）；La Sapienza University of Rome（罗马第一大学）
Calibrating Label Distribution for Class-Imbalanced Barely-Supervised Knee Segmentation
The Hong Kong University of Science and Technology（香港科技大学）；Shanghai Jiao Tong University（上海交通大学）；The Hong Kong University of Science and Technology Shenzhen Research Institute（香港科技大学深圳研究院）
WeedVision: Multi-Stage Growth and Classification of Weeds using DETR and RetinaNet for Precision Agriculture
School of Computing（计算机学院）；Southern Illinois University Carbondale（南伊利诺伊大学卡本代尔分校）；Department of Plant Soils and Agricultural Systems（植物土壤与农业系统系）；School of Agricultural Sciences（农业科学学院）；School of Biological Sciences（生物科学学院）
Deep Learning for automated multi-scale functional field boundaries extraction using multi-date Sentinel-2 and PlanetScope imagery: Case Study of Netherlands and Pakistan
Low-Resource Crop Classification from Multi-Spectral Time Series Using Lossless Compressors
College of Information and Electrical Engineering, China Agricultural University（中国农业大学信息与电气工程学院）；College of Science, China Agriculture University（中国农业大学理学院）
In the Search for Optimal Multi-view Learning Models for Crop Classification with Global Remote Sensing Data
Department of Computer Science, University of Kaiserslautern-Landau (RPTU)（科斯拉特兰大学计算机科学系）；SDS, German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）
Fields of The World: A Machine Learning Benchmark Dataset For Global Agricultural Field Boundary Segmentation
Arizona State University（亚利桑那州立大学）；Microsoft AI for Good Research Lab（微软人工智能造福社会研究实验室）；Taylor Geospatial Institute（泰勒地理空间研究所）；Washington University in St Louis（圣路易斯华盛顿大学）；Taylor Geospatial Engine（泰勒地理空间引擎）
Domain Adaptation for Big Data in Agricultural Image Analysis: A Comprehensive Review
School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology（光学电子与计算机工程学院，上海理工大学）；School of Electronics and Electrical Engineering, Shanghai University of Engineering Science（电子电气工程学院，上海工程技术大学）；School of Electrical Engineering, Korea University（电气工程学院，韩国大学）；Future Information Innovation Institute, Fudan University（未来信息创新研究院，复旦大学）
