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Deep Transfer Learning Based Detection and Classification of Citrus Plant Diseases

computers, Materials & Continua 2023年第7期76卷 895-914页

作者： Shah Faisal Kashif Javed Sara Ali Areej Alasiry Mehrez Marzougui Muhammad Attique Khan Jae-Hyuk Cha Department of Robotics and Artificial Intelligence SMME NUSTIslamabadPakistan College of Computer Science King Khalid UniversityAbha61413Saudi Arabia Department of Computer Science HITEC UniversityTaxilaPakistan Department of Computer Science Hanyang UniversitySeoul04763Korea

Citrus fruit crops are among the world’s most important agricultural products,but pests and diseases impact their cultivation,resulting in yield and quality *** vision and machine learning have been widely used to detect and classify plant diseases over the last decade,allowing for early disease detection and improving agricultural *** paper presented an automatic system for the early detection and classification of citrus plant diseases based on a deep learning(DL)model,which improved accuracy while decreasing computational *** most recent transfer learning-based models were applied to the Citrus Plant Dataset to improve classification *** transfer learning,this study successfully proposed a Convolutional Neural Network(CNN)-based pre-trained model(EfficientNetB3,ResNet50,MobiNetV2,and InceptionV3)for the identification and categorization of citrus plant *** evaluate the architecture’s performance,this study discovered that transferring an EfficientNetb3 model resulted in the highest training,validating,and testing accuracies,which were 99.43%,99.48%,and 99.58%,*** identifying and categorizing citrus plant diseases,the proposed CNN model outperforms other cuttingedge CNN model architectures developed previously in the literature.

关键词： Citrus diseases classification deep learning transfer learning efficientNetB3 mobileNetV2 ResNet50 InceptionV3

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SS3DM: Benchmarking Street-View Surface Reconstruction with a Synthetic 3D Mesh Dataset 38

SS3DM: Benchmarking Street-View Surface Reconstruction with ...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Hu, Yubin Wen, Kairui Zhou, Heng Guo, Xiaoyang Liu, Yong-Jin Department of Computer Science and Technology Tsinghua University China Horizon Robotics China

Reconstructing accurate 3D surfaces for street-view scenarios is crucial for applications such as digital entertainment and autonomous driving simulation. However, existing street-view datasets, including KITTI, Waymo, and nuScenes, only offer noisy LiDAR points as ground-truth data for geometric evaluation of reconstructed surfaces. These geometric ground-truths often lack the necessary precision to evaluate surface positions and do not provide data for assessing surface normals. To overcome these challenges, we introduce the SS3DM dataset, comprising precise Synthetic Street-view 3D Mesh models exported from the CARLA simulator. These mesh models facilitate accurate position evaluation and include normal vectors for evaluating surface normal. To simulate the input data in realistic driving scenarios for 3D reconstruction, we virtually drive a vehicle equipped with six RGB cameras and five LiDAR sensors in diverse outdoor scenes. Leveraging this dataset, we establish a benchmark for state-of-the-art surface reconstruction methods, providing a comprehensive evaluation of the associated challenges. For more information, visit our homepage at https://***. © 2024 Neural information processing systems foundation. All rights reserved.

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Investigating the effects of embodiment on presence and perception in remote physician video consultations: a between-participants study comparing a tablet and a telepresence robot

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i-com 2025年第1期24卷 213-226页

作者： Schulz, Dominik Unbehaun, David Doernbach, Tobias Clausthal University of Technology Computer Science Institute Clausthal-Zellerfeld Germany Ostfalia University of Applied Sciences Human-Centered Robotics Lab Wolfenbuettel Germany

Increasing life expectancy and low birth rates have led to a larger aging population requiring more care, especially in rural areas. Information and communications technology may enhance older adults' quality of life and digital health services. Assistive robotic systems, especially telepresence robots, are increasingly used in healthcare. These robots represent a significantly higher embodiment level than tablets, as they can provide the remote person with an additional body. This work explores the effects of different embodiment levels in video consultation, focusing on Presence and Perception from a Human-computer Interaction (HCI) perspective. For this purpose, between-participants experiments were conducted in which 18 participants assumed the role of the patient and engaged in a video consultation with a remote, medically experienced person acting as the physician. A shortened medical check-up simulated the real assessment. Qualitative and quantitative data were collected during and after the experiment focusing on presence and interaction. Results show no significant influence of embodiment level in a video consultation from a HCI perspective. However, a trend towards more natural communication with stronger embodiment was observed, suggesting it may positively influence interaction. © 2025 the author(s), published by De Gruyter, Berlin/Boston.

关键词： Electronic health record

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Transfer Learning Based Yogic Posture Recognition System Using Deep Pre-trained Features

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SN computer science 2024年第6期5卷 746页

作者： Rajendran, Arun Kumar Sethuraman, Sibi Chakkaravarthy School of Computer Science and Engineering VIT-AP University School of Computer Science & amp Engineering and Center of Excellence Artificial Intelligence and Robotics (AIR) VIT-AP University

Yoga is a healthy exercise that focuses on physical, psychological, and divine connections. However, engaging in yoga while adopting poor postures might result in health issues like muscle discomfort and sprains. Discovering the specific posture amid an activity is still a very challenging and complicated task in artificial intelligence. With the aid of transfer learning, a model which can identify the yogic posture from images has been constructed in this work. In this approach, sixteen postures of yogic data are collected, and then pre-processing techniques are used to resize the images. This work employs the transfer learning method to extract the features from popular convolutional neural network architectures such as VGG16, VGG19, InceptionV3, Xception, MobileNet, and MobileNetV2;all are pre-trained on the ImageNet database. Extracted features are fed to machine learning models like Naive Bayes (NB), Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) for classification. The linear SVM-based classifier, trained on features extracted by MobileNet, performs better than its rivals with an accuracy of 99.92%. In total, six transfer learning models are exploited in this yogic posture classification approach and yield encouraging results in terms of prediction accuracy. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： InceptionV3 MobileNet MobileNetV2 NB RF SVM Transfer learning VGG16 VGG19 Xception XGBoost Yoga posture classification

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Incomplete and Asymmetric Information in General Lotto Games

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IEEE Transactions on Automatic Control 2024年第6期70卷 3617-3632页

作者： Paarporn, Keith Chandan, Rahul Alizadeh, Mahnoosh Marden, Jason R. University of Colorado Department of Computer Science Colorado Springs United States Amazon Robotics United States University of California Department of Electrical and Computer Engineering Santa BarbaraCA United States

In this paper, we consider incomplete and asymmetric information formulations of the General Lotto game, which describes two opposing players that strategically allocate limited resources over multiple contests. In particular, we consider scenarios where one of the player's resource budget is common knowledge while the other player's is private. Our main contribution provides complete equilibrium characterizations in the scenario where the private resource budget is drawn from a Bernoulli distribution. We then leverage these characterizations to analyze a resource assignment problem where a commander must decide how to assign resources to sub-colonels that compete against opponents in separate General Lotto games. While optimal deterministic policies have previously been characterized in the literature, we broaden the context by deriving optimal randomized policies, which induce asymmetric information General Lotto games. Leveraging our equilibrium characterizations, we demonstrate that randomization can offer a four-fold improvement in the commander's performance over the optimal deterministic assignments. © 1963-2012 IEEE.

关键词： Budget control

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libBICOS - An Open Source GPU-Accelerated Library implementing BInary COrrespondence Search for 3D Reconstruction

libBICOS - An Open Source GPU-Accelerated Library implementi...

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2024 Optical 3D Metrology, O3DM 2024

作者： Liebender, Christoph Bleier, Michael Nüchter, Andreas Computer Science XVII - Robotics Julius-Maximilians-Universität Würzburg Germany

In this paper, we present an implementation and publish an open source library for binary correspondence search (BICOS), an efficient method for accurate 3D reconstruction from structured light stereo imagery. Starting with two stacks of stereo-rectified images of a scene illuminated by a statistical light pattern the proposed method solves the problem of a pixelwise correspondence search. Our GPU-accelerated implementation reduces the latency of disparity computation using 7MP images on modern hardware down to 20 milliseconds. Based on the algorithm introduced by Dietrich et al. (2019), we extend their approach by increasing the descriptor size while augmenting postprocessing to increase its applicability on other types of pattern projections. Lastly, we provide benchmarks and example reconstructions using a stereo camera setup combined with an off-the-shelf projector to validate the algorithm’s performance. While many state-of-the-art single-shot stereo implementations are included in common computer vision libraries, high performance multi-shot methods are not broadly available. By publishing this method as a freely available library, in both a CUDA and CPU implementation, we hope for others to quickly gain traction in this field. The source code with build instructions and command-line tooling is available at https://***/JMUWrobotics/libBICOS under the GNU LGPLv3. © Author(s) 2024.

关键词： Pixels

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A handheld stereo vision and LiDAR system for outdoor dense RGB-D mapping using depth map completion based on learned priors 8

A handheld stereo vision and LiDAR system for outdoor dense ...

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8th International ISPRS Workshop LowCost 3D - Sensors, Algorithms, Applications

作者： Bleier, Michael Yuan, Yijun Nüchter, Andreas Computer Science XVII: Robotics Julius-Maximilians-Universität Würzburg Germany

This paper proposes a handheld mapping system consisting of a stereo camera setup combined with low-cost automotive LiDAR. The prototype system is applicable to various types of mobile monocular, stereo vision and LiDAR data collection and processing. Capturing dense RGB-D data outdoors with low-cost sensors is challenging, especially when low latency is required. Readily available commercial RGB-D sensors are typically limited to a range of less than 10 m, which is too small to capture large outdoor structures. Currently available low-cost automotive LiDAR scanners feature a suitable range but provide only sparse data. To enable low-latency dense RGB-D scans we augment the sparse LiDAR data with the RGB data stream based on learned models. We apply monocular depth estimation based on a single image and apply scale correction based on learned priors and sparse automotive LiDAR scans. Using the laser scan data, accurate metric information is incorporated directly into the scale estimation stage. For validation, the learning-based depth map completion is compared to traditional LiDAR mapping using scan matching on an outdoor data set acquired with the proposed handheld. While the model-based regression of the sparse LiDAR data produces significantly less accurate results in our experiments, it is able to compute dense RGB-D data from a single sparse 3D scan and monocular RGB image with low latency. Copyright: © 2024 Raniero Beber et al.

关键词： Mapping

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2812 | An Interaction-Aware Two-Level Robotic Planning and Control System for Vegetation Override 21

2812 | An Interaction-Aware Two-Level Robotic Planning and C...

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21st International and 12th Asia-Pacific Regional Conference of the International Society for Terrain-Vehicle Systems, ISTVS 2024

作者： Noren, Charles Shirose, Burhanuddin Vundurthy, Bhaskar Scherer, Sebastian Travers, Matthew The Robotics Institute School of Computer Science Carnegie Mellon University PA United States

ISBN: (纸本)9781942112570

During off-road operations, mobile robotic platforms often encounter objects that influence the platform's route. As determining the outcome of an interaction with an object (e.g., overriding) is difficult, many robotic planners are designed to avoid interactions with all environmental objects. Yet, this object-adverse planning behavior is not reflected in the actions of expert human operators, who may interact with objects in order to find a viable path towards their goal. This work intends to emulate that human operator intuition. Our objective is to improve the performance of robotic traversals in off-road terrains through the development of a planning paradigm that allows certain safe contact with environmental objects. Specifically, we design a two-level hierarchical planning and control system which couples a contact-informed regional motion planner with contact-constrained local nonlinear trajectory optimization techniques. The approach is demonstrated for classes of vegetative objects which are characterized by existing parameterized collision models from the terramechanics community. The top level of the hierarchy combines sampling-based planning techniques with a set of override (velocity) constraints derived from these collision models during the search for a minimum-time trajectory towards the platform's goal. This minimum-time trajectory is then passed to the lower-level of the architecture, which utilizes direct collocation and model predictive control techniques to ensure that the velocity constraints are enforced during trajectory execution. The capabilities of the architecture are shown both in simulation and onboard a robotic platform, where vegetation is reasoned about and then overridden depending on the environment, platform, and object geometry. © 2024 International Society for Terrain-Vehicle Systems. All rights reserved.

关键词： Off road vehicles

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Rapid Assessment of MANET Reliability Trends

Rapid Assessment of MANET Reliability Trends

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2023 International Conference on Industrial Engineering, Applications and Manufacturing, ICIEAM 2023

作者： Gvozdev, Vladimir Davlieva, Aliya Galimov, Robert Ufa University of Science and Technology Department of Computer Science and Robotics Ufa Russia

ISBN: (纸本)9781665475952

The topological features of distributed infocommunication networks are among the key factors affecting such information safety characteristics as stability;reliability and resistance to failures and attacks. In the framework of this work, the postulate is postulated that the consequences of the security threats implementation are disconnections between the Wireless Mobile ad hoc network nodes. The metric characteristic of the consequences is a change in the system reliability indicators. The paper substantiates the possibility of using well-known methods for identifying trends to build estimates of the integral security characteristics of the Wireless Mobile ad hoc network;apparatus of order statistics;statistic indexes;linguistic variables. © 2023 IEEE.

关键词： Reliability

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TECMH:Transformer-Based Cross-Modal Hashing For Fine-Grained Image-Text Retrieval

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computers, Materials & Continua 2023年第5期75卷 3713-3728页

作者： Qiqi Li Longfei Ma Zheng Jiang Mingyong Li Bo Jin College of Computer and Information Science Chongqing Normal UniversityChongqing401331China Institute of Systems and Robotics(ISR) Department of Electrical and Computer Engineering(DEEC)University of CoimbraCoimbraPortugal

In recent years,cross-modal hash retrieval has become a popular research field because of its advantages of high efficiency and low ***-modal retrieval technology can be applied to search engines,crossmodalmedical processing,*** existing main method is to use amulti-label matching paradigm to finish the retrieval ***,such methods do not use fine-grained information in the multi-modal data,which may lead to suboptimal *** avoid cross-modal matching turning into label matching,this paper proposes an end-to-end fine-grained cross-modal hash retrieval method,which can focus more on the fine-grained semantic information of multi-modal ***,the method refines the image features and no longer uses multiple labels to represent text features but uses BERT for ***,this method uses the inference capabilities of the transformer encoder to generate global fine-grained ***,in order to better judge the effect of the fine-grained model,this paper uses the datasets in the image text matching field instead of the traditional label-matching *** article experiment on Microsoft COCO(MS-COCO)and Flickr30K datasets and compare it with the previous *** experimental results show that this method can obtain more advanced results in the cross-modal hash retrieval field.

关键词： Deep learning cross-modal retrieval hash learning transformer

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