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Crop protection and disease detection using artificial intelligence and computer vision: a comprehensive review

Multimedia Tools and Applications 2025年第7期84卷 3723-3743页

作者： Shah, Kanish Sushra, Rajat Shah, Manan Shah, Dhairya Shah, Haard Raval, Megh Prajapati, Mitul Software Engineer Walmart SunnyvaleCA United States Atmiya Vidya Mandir Gujarat Surat India Department of Chemical Engineering School of Energy Technology Pandit Deendayal Energy University Gujarat Gandhinagar India Robotics ECE Concentration Northeastern University BostonMA United States Department of Computer Science Concentration in AI Hofstra University HempsteadNY United States Department of Chemical Engineering S. S Agrawal Institute of Engineering & Technology Gujarat Navsari India R &D Engineer Span Inspection Systems Pvt. Ltd Gujarat Gandhinagar India

The technological advancements in the field of agriculture have increased to a great extent in recent years, and many techniques have evolved from other techniques. Some methods are improved or upgraded from the previous versions by implementing a new model or using better hardware devices. This has been helpful for the farmers in increasing crop productivity, and the life expectancy of crops has also increased as the diseases inside or outside the crops can be detected much earlier, and learning at an early stage helps prevent other crops. In this paper, we have presented a study where many varieties of fruits and vegetables have been taken to determine which method was used for a particular crop. By analyzing the various works carried out by the authors, it was inferred that most of the works revolved around image processing and hyperspectral imaging. Due to this, we had also included most of the papers, particularly as the models and hardware components used were much better than other works. Then, a comparative study was done where different fruits and vegetables highlighted the two main areas: the method used and the accuracy obtained. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Crops

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Data-Driven vs Traditional Approaches to Power Transformer’s Top-Oil Temperature Estimation

arXiv

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arXiv 2025年

作者： Tembo, Francis Bragone, Federica Laneryd, Tor Barreau, Matthieu Morozovska, Kateryna Division of Decision and Control Systems Department of Intelligent Systems KTH Royal Institute of Technology Stockholm Sweden Division of Computational Science and Technology Department of Computer Science KTH Royal Institute of Technology Stockholm Sweden Hitachi Energy Research Västerås Sweden

Power transformers are subjected to electrical currents and temperature fluctuations that, if not properly controlled, can lead to major deterioration of their insulation system. Therefore, monitoring the temperature of a power transformer is fundamental to ensure a long-term operational life. Models presented in the IEC 60076-7 and IEEE standards, for example, monitor the temperature by calculating the top-oil and the hot-spot temperatures. However, these models are not very accurate and rely on the power transformers’ properties. This paper focuses on finding an alternative method to predict the top-oil temperatures given previous measurements. Given the large quantities of data available, machine learning methods for time series forecasting are analyzed and compared to the real measurements and the corresponding prediction of the IEC standard. The methods tested are Artificial Neural Networks (ANNs), Time-series Dense Encoder (TiDE), and Temporal Convolutional Networks (TCN) using different combinations of historical measurements. Each of these methods outperformed the IEC 60076-7 model and they are extended to estimate the temperature rise over ambient. To enhance prediction reliability, we explore the application of quantile regression to construct prediction intervals for the expected top-oil temperature ranges. The best-performing model successfully estimates conditional quantiles that provide sufficient coverage. Copyright © 2025, The Authors. All rights reserved.

关键词： Distribution transformers

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Optimal Sensor Placement in Power Transformers Using Physics-Informed Neural Networks

arXiv

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arXiv 2025年

作者： Li, Sirui Bragone, Federica Barreau, Matthieu Laneryd, Tor Morozovska, Kateryna Division of Decision and Control Systems Department of Intelligent Systems KTH Royal Institute of Technology Stockholm Sweden Division of Computational Science and Technology Department of Computer Science KTH Royal Institute of Technology Stockholm Sweden Hitachi Energy Research Västerås Sweden

Our work aims at simulating and predicting the temperature conditions inside a power transformer using Physics-Informed Neural Networks (PINNs). The predictions obtained are then used to determine the optimal placement for temperature sensors inside the transformer under the constraint of a limited number of sensors, enabling efficient performance monitoring. The method consists of combining PINNs with Mixed Integer Optimization Programming to obtain the optimal temperature reconstruction inside the transformer. First, we extend our PINN model for thermal modelling of power transformers to solve the heat diffusion equation from 1D to 2D space. Finally, we construct an optimal sensor placement model inside the transformer that can be applied to problems in 1D and 2D. Copyright © 2025, The Authors. All rights reserved.

关键词： Integer programming

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CallNavi, A Challenge and Empirical Study on LLM Function Calling and Routing

arXiv

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arXiv 2025年

作者： Song, Yewei Tang, Xunzhu Lothritz, Cedric Ezzini, Saad Klein, Jacques Bissyandé, Tegawendé F. Boytsov, Andrey Ble, Ulrick Goujon, Anne University of Luxembourg Luxembourg Luxembourg Institute of Science and Technology Luxembourg Department of Information and Computer Science KFUPM Saudi Arabia BGL BNP PARIBAS Luxembourg Interdisciplinary Research Center for Intelligent Manufacturing and Robotics KFUPM Saudi Arabia

API-driven chatbot systems are increasingly integral to software engineering applications, yet their effectiveness hinges on accurately generating and executing API calls. This is particularly challenging in scenarios requiring multi-step interactions with complex parameterization and nested API dependencies. Addressing these challenges, this work contributes to the evaluation and assessment of AI-based software development through three key advancements: (1) the introduction of a novel dataset specifically designed for benchmarking API function selection, parameter generation, and nested API execution;(2) an empirical evaluation of state-of-the-art language models, analyzing their performance across varying task complexities in API function generation and parameter accuracy;and (3) a hybrid approach to API routing, combining general-purpose large language models for API selection with fine-tuned models and prompt engineering for parameter generation. These innovations significantly improve API execution in chatbot systems, offering practical methodologies for enhancing software design, testing, and operational workflows in real-world software engineering contexts. © 2025, CC BY-SA.

关键词： Software design

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Policy Design in Long-Run Welfare Dynamics

arXiv

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arXiv 2025年

作者： Wu, Jiduan Abebe, Rediet Hardt, Moritz Stoica, Ana-Andreea Max Planck Institute for Intelligent Systems Tübingen AI Center Tübingen Germany Department of Computer Science ETH Zürich Switzerland ELLIS Institute Tübingen Germany

Improving social welfare is a complex challenge requiring policymakers to optimize objectives across multiple time horizons. Evaluating the impact of such policies presents a fundamental challenge, as those that appear suboptimal in the short run may yield significant long-term benefits. We tackle this challenge by analyzing the long-term dynamics of two prominent policy frameworks: Rawlsian policies, which prioritize those with the greatest need, and utilitarian policies, which maximize immediate welfare gains. Conventional wisdom suggests these policies are at odds, as Rawlsian policies are assumed to come at the cost of reducing the average social welfare, which their utilitarian counterparts directly optimize. We challenge this assumption by analyzing these policies in a sequential decision-making framework where individuals’ welfare levels stochastically decay over time, and policymakers can intervene to prevent this decay. Under reasonable assumptions, we prove that interventions following Rawlsian policies can outperform utilitarian policies in the long run, even when the latter dominate in the short run. We characterize the exact conditions under which Rawlsian policies can outperform utilitarian policies. We further illustrate our theoretical findings using simulations, which highlight the risks of evaluating policies based solely on their short-term effects. Our results underscore the necessity of considering long-term horizons in designing and evaluating welfare policies;the true efficacy of even well-established policies may only emerge over time. © 2025, CC BY-NC-ND.

关键词： Stochastic systems

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Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers

arXiv

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arXiv 2025年

作者： Singh, Siddharth Singhania, Prajwal Ranjan, Aditya Kirchenbauer, John Geiping, Jonas Wen, Yuxin Jain, Neel Hans, Abhimanyu Shu, Manli Tomar, Aditya Goldstein, Tom Bhatele, Abhinav Department of Computer Science University of Maryland College Park United States ELLIS Institute Tübingen Max Planck Institute for Intelligent Systems Tübingen Germany Department of Electrical Engineering and Computer Sciences University of California Berkeley United States

Training and fine-tuning large language models (LLMs) with hundreds of billions to trillions of parameters requires tens of thousands of GPUs, and a highly scalable software stack. In this work, we present a novel four-dimensional hybrid parallel algorithm implemented in a highly scalable, portable, open-source framework called AxoNN. We describe several performance optimizations in AxoNN to improve matrix multiply kernel performance, overlap non-blocking collectives with computation, and performance modeling to choose performance optimal configurations. These have resulted in unprecedented scaling and peak flop/s (bf16) for training of GPT-style transformer models on Perlmutter (620.1 Petaflop/s), Frontier (1.381 Exaflop/s) and Alps (1.423 Exaflop/s). While the abilities of LLMs improve with the number of trainable parameters, so do privacy and copyright risks caused by memorization of training data, which can cause disclosure of sensitive or private information at inference time. We highlight this side effect of scale through experiments that explore "catastrophic memorization," where models are sufficiently large to memorize training data in a single pass, and present an approach to prevent it. As part of this study, we demonstrate fine-tuning of a 405-billion parameter LLM using AxoNN on Frontier. Copyright © 2025, The Authors. All rights reserved.

关键词： Supercomputers

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NavG: Risk-Aware Navigation in Crowded Environments Based on Reinforcement Learning with Guidance Points

arXiv

引用

arXiv 2025年

作者： Zhang, Qianyi Luo, Wentao Liu, Boyi Zhang, Ziyang Wang, Yaoyuan Liu, Jingtai Institute of Robotics and Automatic Information System Nankai University Tianjin Key Laboratory of Intelligent Robotics Tianjin China Advanced Computing and Storage Lab Huawei 2012 Lab China Department of Electronic and Computer Engineering The Kong Kong University of Science and Technology Hong Kong

Motion planning in navigation systems is highly susceptible to upstream perceptual errors, particularly in human detection and tracking. To mitigate this issue, the concept of guidance points—a novel directional cue within a reinforcement learning-based framework—is introduced. A structured method for identifying guidance points is developed, consisting of obstacle boundary extraction, potential guidance point detection, and redundancy elimination. To integrate guidance points into the navigation pipeline, a perception-to-planning mapping strategy is proposed, unifying guidance points with other perceptual inputs and enabling the RL agent to effectively leverage the complementary relationships among raw laser data, human detection and tracking, and guidance points. Qualitative and quantitative simulations demonstrate that the proposed approach achieves the highest success rate and near-optimal travel times, greatly improving both safety and efficiency. Furthermore, real-world experiments in dynamic corridors and lobbies validate the robot’s ability to confidently navigate around obstacles and robustly avoid pedestrians. © 2025, CC BY.

关键词： Travel time

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Human Fall Detection Using Transfer Learning-Based 3D CNN 1st

Human Fall Detection Using Transfer Learning-Based 3D CNN

引用

1st International Conference on Computational Technologies and Electronics, ICCTE 2023

作者： Alam, Ekram Sufian, Abu Dutta, Paramartha Leo, Marco Department of Computer Science Gour Mahavidyalaya Old Malda India Department of Computer Science University of Gour Banga English Bazar India Department of Computer and System Sciences Visva-Bharati University Santiniketan India National Research Council of Italy Institute of Applied Sciences and Intelligent Systems Lecce Italy

ISBN: (纸本)9783031819346

Unintentional or accidental falls are one of the significant health issues in senior persons. The population of senior persons is increasing steadily. So, there is a need for an automated fall detection monitoring system. This paper introduces a vision-based fall detection system using a pre-trained 3D CNN. Unlike 2D CNN, 3D CNN extracts not only spatial but also temporal features. Proposed model leverages the original learned weights of a 3D CNN model pre-trained on the Sports1M dataset to extract the spatio-temporal features. Only the SVM classifier was trained, which saves the time required to train the 3D CNN. Stratified shuffle five split cross-validation has been used to split the dataset into training and testing data. Extracted features from the proposed 3D CNN model were fed to an SVM classifier to classify the activity as fall or ADL. Two datasets, GMDCSA and CAUCAFall, were utilized to conduct the experiment. The source code for this work can be accessed via the following link: https://***/ekramalam/HFD_3DCNN. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Fall detection

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Stealthy Attacks on Sampled-Data Control and Encryption-Based Countermeasures

引用

IEEE Transactions on Automatic Control 2025年

作者： Shang, Jun Li, Yuzhe Chen, Tongwen Tongji University Department of Control Science and Engineering Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Shanghai200092 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110004 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada

This paper investigates stealthy attacks on sampled-data control systems, where a continuous process is sampled periodically, and the resultant discrete output and control signals are transmitted through dual channels in wireless communication networks. Acknowledging the vulnerability of these transmitted data to cyber threats, we analyze two types of stealthy attacks that compromise either one or both channels. We explicitly present the necessary and sufficient conditions for the existence of stealthy single-channel attacks. To mitigate these security risks, we implement encryption-based countermeasures, including linear time-invariant and periodic encryption methods. We detail sufficient conditions under which these encryption strategies effectively thwart single- and dual-channel stealthy attacks. The effectiveness of these approaches and the theoretical findings are demonstrated through numerical simulations. © 1963-2012 IEEE.

关键词： Cyber attacks

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Comprehensive Performance Optimization of a Novel Anthropomorphic Dexterous Hand

引用

IEEE robotics and Automation Letters 2025年第7期10卷 6816-6823页

作者： Yifan Ma Weiwei Shang Fei Zhang Shunxiang Pang Shuqing Dai Zhengkun Yi Guotao Li Shuang Cong Department of Automation University of Science and Technology of China Hefei China Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China

Grasp and in-hand manipulation of dexterous hands are essential for daily operational tasks. In this study, an optimized design method for a novel anthropomorphic dexterous hand is proposed, which improves the comprehensive performance of grasp and manipulability of the dexterous hand. The constraints of multi-fingered contact force and kinematics are established with full consideration of the applicability to the objects of different sizes and the shape characteristics of finger contact surface. The performance index of in-hand manipulation ability is proposed, and the optimization problem is constructed through the technique for order preference by similarity to ideal solution (TOPSIS) to achieve multi-objective optimization of precision grasp and in-hand manipulation. Finally, simulation and experiment verify the feasibility of our prototype and optimization method. The results show that the in-hand translation space and in-hand rotation range are increased by 20% and 8.5%, respectively. Our dexterous hand has excellent abilities of multi-fingered interaction and in-hand manipulation, which can perform 33 grasp gestures and two/three-fingered in-hand manipulation. This study provides a valuable reference for the design of dexterous hands with anthropomorphic functions.

关键词： Thumb Hands Kinematics Grasping Force Performance analysis IP networks Electronics packaging Cables Accuracy

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