This paper describes a novel method of training a semantic segmentation model for scene recognition of agricultural mobile robots exploiting publicly available datasets of outdoor scenes that are different from the target greenhouse environments. Semantic segmentation models require abundant labels given by tedious manual annotation. A method to work around it is unsupervised domain adaptation (UDA) that transfers knowledge from labeled source datasets to unlabeled target datasets. However, the effectiveness of existing methods is not well studied in adaptation between heterogeneous environments, such as urban scenes and greenhouses. In this paper, we propose a method to train a semantic segmentation model for greenhouse images without manually labeled datasets of greenhouse images. The core of our idea is to use multiple rich image datasets of different environments with segmentation labels to generate pseudo-labels for the target images to effectively transfer the knowledge from multiple sources and realize a precise training of semantic segmentation. Along with the pseudo-label generation, we introduce state-of-the-art methods to deal with noise in the pseudo-labels to further improve the performance. We demonstrate in experiments with multiple greenhouse datasets that our proposed method improves the performance compared to the single-source baselines and an existing approach.
Design of a six wheel suspension and a three-axis linear actuation mechanism for a laser weeding robot
Robotics and Intelligent Systems Engineering (RISE) Lab（机器人与智能系统工程实验室）；Department of Robotics and Artificial Intelligence（机器人与人工智能系）；School of Mechanical and Manufacturing Engineering (SMME)（机械与制造工程学院）；National University of Sciences and Technology (NUST)（国立科学与技术大学）
HouseExpo: A Large-scale 2D Indoor Layout Dataset for Learning-based Algorithms on Mobile Robots
The Chinese University of Hong Kong（香港中文大学）；Southern University of Science and Technology（南方科技大学）；Shenzhen Research Institute of the Chinese University of Hong Kong（香港中文大学深圳研究院）
A ROS2 Benchmarking Framework for Hierarchical Control Strategies in Mobile Robots for Mediterranean Greenhouses
Department of Informatics, CIESOL, ceiA3, Universidad de Almería（信息学院、CIESOL、ceiA3、阿尔梅里亚大学）；Department of Computer Science and Automation, National University of Distance Education (UNED)（计算机科学与自动化系、远程教育国家大学（UNED））；Department of Engineering, CIESOL, ceiA3, Universidad de Almería（工程学院、CIESOL、ceiA3、阿尔梅里亚大学）
A Ground Mobile Robot for Autonomous Terrestrial Laser Scanning-Based Field Phenotyping
School of Electrical and Computer Engineering（电气与计算机工程学院）；The University of Georgia（佐治亚大学）；Bio-Sensing, Automation and Intelligence Laboratory（生物传感、自动化与智能实验室）；Agricultural & Biological Engineering（农业与生物工程）；The University of Florida（佛罗里达大学）
DAS-SK: An Adaptive Model Integrating Dual Atrous Separable and Selective Kernel CNN for Agriculture Semantic Segmentation
Electrical & Computer Engineering Department of Lakehead University（拉夫莱德大学电气与计算机工程系）；Faculty of Software Engineering of Lakehead University（拉夫莱德大学软件工程系）；Faculty of Applied Science of the University of Alabama（阿拉巴马大学应用科学系）；School of Computer Science Engineering and Information Systems, Vellore Institute of Technology（维洛雷理工学院计算机科学工程与信息系统学院）
Probabilistic modelling and safety assurance of an agriculture robot providing light-treatment
Robotics Group, Faculty of Science & Technology, Norwegian University of Life Sciences (NMBU)（挪威生命科学大学机器人小组，科学与技术学院）；Dept. of Computer Science, University of York（约克大学计算机科学系）；Dept. of Mechatronics, Faculty of Engineering and Science, University of Agder (UiA)（阿格德大学工程与科学学院机电系）；Saga Robotics AS（Saga机器人公司）
Warped Hypertime Representations for Long-term Autonomy of Mobile Robots
Artificial Intelligence Center, Faculty of Electrical Engineering, Czech Technical University（人工智能中心，电气工程系，捷克技术大学）；Lincoln Centre for Autonomous Systems, University of Lincoln（自主系统林肯中心，林肯大学）；Technical University of Cartagena（卡塔赫纳技术大学）
Determination of building flood risk maps from LiDAR mobile mapping data
Leibniz University Hannover（莱布尼茨汉诺威大学）；bpi Hannover–Beratende Ingenieure（汉诺威咨询工程师有限公司）；Federal Institute for Geosciences and Natural Resources（联邦地球科学与自然资源研究所）；Shandong University of Science and Technology（山东科技大学）；China University of Geosciences（中国地质大学）
MobilePlantViT: A Mobile-friendly Hybrid ViT for Generalized Plant Disease Image Classification
University of Asia Pacific（亚太大学）；Bangladesh University of Business and Technology（孟加拉国商业与技术大学）；Techno International New Town（科技国际新城大学）；American International University-Bangladesh（孟加拉国美国国际大学）
Empowering Agricultural Insights: RiceLeafBD -- A Novel Dataset and Optimal Model Selection for Rice Leaf Disease Diagnosis through Transfer Learning Technique
Leading University（领先大学）；Daffodil International University（水仙国际大学）；Queensborough Community College（皇后borough社区学院）
Low Cost, High Efficiency: LiDAR Place Recognition in Vineyards with Matryoshka Representation Learning
University Institute for Engineering Research, Miguel Hernández University（工程研究院，米格尔·赫尔内亚大学）；Department of Electronics and Communications, Politecnico di Torino（电子与通信系，托里诺理工学院）
Weed Recognition using Deep Learning Techniques on Class-imbalanced Imagery
Murdoch University（默多克大学）；Department of Primary Industries and Regional Development, Western Australia（西澳大利亚州初级产业与区域发展部）；University of South Australia（南澳大学）
Semi-Supervised Weed Detection for Rapid Deployment and Enhanced Efficiency
College of Science and Engineering, James Cook University（詹姆斯库克大学科学与工程学院）；AutoWeed Pty Ltd（AutoWeed私人有限公司）；Agriculture Technology and Adoption Centre, James Cook University（詹姆斯库克大学农业技术与推广中心）
Analysis of Convolutional Neural Network-based Image Classifications: A Multi-Featured Application for Rice Leaf Disease Prediction and Recommendations for Farmers
Nepal Engineering College（尼泊尔工程学院）；Pokhara University（博卡拉大学）；Tribhuvan University（特里布万大学）；Institute of Engineering（工程学院）
