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Farm Fusion: Smart Crop Recommendation system for Sustainable Farming Using Machine Learning Techniques

Farm Fusion: Smart Crop Recommendation System for Sustainabl...

引用

Disruptive Technologies (ICDT), international conference on

作者： Gobinath A Nithya Sri Santhoshini K V N S Rajeswari P Manjula Devi C Anandan M Andal L Department of Information Technology Velammal College of Engineering and Technology Madurai India Department of Electronics and Communication Engineering Velammal College of Engineering and Technology Madurai India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India RMK Engineering College Chennai India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

In the recent past, data-centric solutions have been developed to address crop choice issues, low yields and soil management. The proposed work in this paper is a web-based application, referred to as Farm Fusion designed to help the farmers for the selection of the best crops to be cultivated according to specific site characteristic. This system utilizes the following machine learning algorithms like Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest, Gaussian Naive Bayes (GNB) and gradient boosting models LightGBM, XGBoost, and CatBoost. Before suggesting the appropriate crops, the system uses an Ensemble model that integrates these algorithms. The input soil nutrient parameters included in this system are phosphorus, potassium, nitrogen while the input environmental parameters include pH, temperature, rainfall and humidity. It is a web application that uses the flask framework which is integrated with pre-trained model to recommend right crop for the farm field. This system will assist in the process of decision making process, provide sustainability and improve crop yield.

关键词： Support vector machines Machine learning algorithms Accuracy Crops Soil Nearest neighbor methods Agriculture Reliability Sustainable development Farming

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RoboCup Soccer Autonomy Uprising: How Crowds, Referees, and Humanoid Robots Are Redefining the Future of Human-Robot Interaction 25

RoboCup Soccer Autonomy Uprising: How Crowds, Referees, and ...

引用

ACM/ieee international conference on Human-Robot Interaction (HRI)

作者： Filippo Sanfilippo Timothy Wiley Rebekah Rousi Dept. of Engineering Sciences University of Agder Grimstad Norway School of Computing Technologies RMIT University Melbourne Australia School of Marketing and Communication University of Vaasa Vaasa Finland

ISBN: (数字)9798350378931

ISBN: (纸本)9798350378948

This paper explores the dynamics of Human-Robot Interaction (HRI) in public spaces, focusing on how humanoid robots engage with human crowds in the competitive RoboCup Soccer environment. We examine the role of spectatorship, where emotional engagement arises through indirect observation of engineering-driven competition, drawing parallels between human soccer and robot sports. The potential for autonomous systems to elicit collective emotions and systematically study such experiences is investigated. Using the Autonomy Levels for Unmanned systems (ALFUS) framework, we assess RoboCup soccer robots' autonomy in terms of mission complexity (MC), environmental complexity (EC), and external system independence (ESI). Additionally, the Autonomy and technology Readiness Assessment (ATRA) method supports gradual capability enhancement, providing a roadmap to higher autonomy. Based on this established methodology, we introduce the Robot-Crowd Interaction Framework (R-CIF), a novel conceptual framework defining the roles of actors involved, to connect theoretical insights with real-world applications. This work highlights the significance of crowd affectivity in robotic sports to boost public engagement and proposes directions for future research on collective emotional dynamics in HRI.

关键词： Surveys Human-robot interaction Humanoid robots Focusing Real-time systems Complexity theory Robots Standards Research and development Sports

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Radar Antenna Scanning Pattern Recognition Method Based on LSTM Network

Radar Antenna Scanning Pattern Recognition Method Based on L...

引用

ieee Information technology and Mechatronics engineering conference (ITOEC)

作者： Xiaobo Zheng Southwest China Research Institute of Electronic Equipment Chengdu China National Key Laboratory Of Electromagnetic Space Security Chengdu China

ISBN: (数字)9798331529482

ISBN: (纸本)9798331529499

Radar antenna scanning patterns include mechanical scanning, one-dimensional phased array scanning, and two-dimensional phased array scanning. When facing radar emitter whose radar type is unknown, the antenna scanning pattern is a critical factor in assessing the threat level. Traditional identification methods are based on expert features and often fail to adequately consider factors such as amplitude-frequency response and measurement errors. This paper employs short-term interpolation for noise reduction and long-term LSTM for classification and recognition. Experiments show that this method has good accuracy in identifying pulse sequences under conditions of frequency variation and measurement errors, making it valuable for practical applications.

关键词： Phased arrays Antenna measurements Measurement errors Radar measurements Simulation Reconnaissance Radar antennas system identification Pattern recognition Long short term memory

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Robust Phase Retrieval from Quantized and Noisy Measurements

Robust Phase Retrieval from Quantized and Noisy Measurements

引用

Acoustics, Speech, and Signal Processing Workshops (ICASSPW), ieee international conference on

作者： Yihong Gao Luteng Zhu Zhuoyang An Mingjie Shao Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong SAR China School of Information Science and Engineering Shandong University Qingdao China Computer School Beijing Information Science and Technology University Beijing China Key Laboratory of System and Control AMSS Chinese Academy of Sciences Beijing China

ISBN: (数字)9798331519315

ISBN: (纸本)9798331519322

The phase retrieval (PR) problem involves recovering a signal from amplitude-only measurements and has been extensively studied in various physical measurement and signal processing systems. Recently, there has been increasing interest in the quantized phase retrieval (QPR) problem, where the observed amplitude is quantized to binary values. The existing studies have focused on the noiseless case. In this paper, we address a robust QPR problem that accounts for both quantization and noise. We formulate the robust QPR problem using maximum-likelihood estimation (MLE), which results in a non-convex and challenging optimization problem due to the complexity of its likelihood function. To obtain a high-quality solution, we develop an expectation-maximization (EM) algorithm combined with the Wirtinger flow, allowing us to convert the problem into a more manageable form at each iteration. Our method is implemented on both randomly generated synthetic data and image reconstruction tasks. The results demonstrate that our approach significantly enhances signal recovery performance under the combined effects of quantization and noise. Additionally, the proposed method exhibits better runtime efficiency compared to existing approaches.

关键词： Maximum likelihood estimation Phase measurement Quantization (signal) Runtime Noise Signal processing algorithms Noise measurement Speech processing Optimization Synthetic data

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Multi-objective Optimization and Dynamic Water Level Regulation Algorithms a case study of the US Great Lakes system

Multi-objective Optimization and Dynamic Water Level Regulat...

引用

international conference on Big Data and Smart Computing (BIGCOMP)

作者： Junlin Zeng Tao Wang Yinhao Wang Xiaogang Pan College of Systems Engineering National University of Defense Technology Changsha China

ISBN: (数字)9798331529024

ISBN: (纸本)9798331529031

Global water resource management, particularly for large lakes, faces significant challenges in balancing ecological, economic, and flood control needs. Current methods struggle to address the diverse demands of stakeholders due to the complex hydrological and environmental factors involved. This paper proposes a solution using a multi-objective optimization model combined with dynamic feedback control. By applying hierarchical analysis (AHP), we assign stakeholder demand weights and develop a real-time water level prediction model using particle swarm optimization (PSO) with a shrinkage factor. The system also incorporates multidimensional feedback control via a spatio-temporal dynamic network to regulate water levels in real-time based on predictive data. Validation using the Great Lakes’ hydrological data from 2000 to 2022 shows the model’s effectiveness in handling environmental changes and maintaining water stability. The model proves to be a robust and practical tool for large-scale water resource management.

关键词： Heuristic algorithms Biological system modeling Predictive models Dynamic scheduling Stability analysis Regulation Real-time systems Feedback control Stakeholders Water resources

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Research and application of ship identification based on image processing

Research and application of ship identification based on ima...

引用

ieee Information technology and Mechatronics engineering conference (ITOEC)

作者： Daokuan Wang Xianfa Ji Aviation Maintenance Sergeant School of Air Force Engineering University XinYang China

ISBN: (数字)9798331529482

ISBN: (纸本)9798331529499

Ship name identification carries identity information. Ship data can be collected quickly by identifying the identification characters in the ship image. However, due to many factors such as light pollution, large tilt angle and environmental interference in ship image, ordinary Optical Character Recognition is difficult to process directly. Based on the self-built ship image dataset, this paper completes the preprocessing of the target image by means of gray levelization, straight square equalization and noise removal. Afterwards, the improved character region positioning algorithm and the fusion neural network model are used to realize the extraction of ship number characters. The recognition accuracy of ship number character can reach 92%. At the same time, referring to the method of highway traffic management, this identification method is used as the core function to design the ship identification system.

关键词： Training Road transportation Accuracy Mechatronics Neural networks Optical character recognition Noise Real-time systems Character recognition Marine vehicles

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Research on intelligent vehicle collision avoidance control considering road adhesion coefficient

Research on intelligent vehicle collision avoidance control ...

引用

ieee Information technology and Mechatronics engineering conference (ITOEC)

作者： Zhicheng Yu Ying Cheng School of Automobile and Transportation Tianjin University of Technology and Education Tianjin China

ISBN: (数字)9798331529482

ISBN: (纸本)9798331529499

To improve collision avoidance and stability of intelligent driving vehicles in emergencies, a control method considering road adhesion is proposed. First, a Kalman filter estimates the road adhesion coefficient in real time based on tire-road interaction. Then, a lateral and longitudinal controller is designed to track the optimal collision avoidance trajectory by applying brakes and generating the optimal steering angle. The controller includes constraints for low-adhesion surfaces to reduce instability. Finally, a joint simulation platform using Prescan, CarSim, and Simulink validates the system. Results show that, compared with the other two control strategies, the designed control strategy can reduce the vehicle's peak sideslip angle by 7.14% and 32.08% respectively in emergency situations, thus enhancing the vehicle's collision avoidance ability and driving stability.

关键词： Adhesives Intelligent vehicles Trajectory tracking Software packages Roads Stability analysis Trajectory Collision avoidance Standards Surface treatment

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A Review on Development of Farmers Disease Diagnostics and Reporting Using Machine Learning Techniques

A Review on Development of Farmers Disease Diagnostics and R...

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2025 international conference on Visual Analytics and Data Visualization, ICVADV 2025

作者： Mukhedkar, Moresh M Thorat, Vaishali V Mukhedkar, Bhakti A Jadhav, Sonali V Dawande, Nitin A Thorat, Chetana V School of Engineering and Technology D Patil University E&tc Engineering Department Pune India School of Engineering and Technology D Patil University Computer Engineering Department Pune India Pimpri Chinchwad College of Engg. SPPU University Computer Engineering Department Pune India

ISBN: (纸本)9798331521394

The Agricultural Portal is a helpful online tool designed to make farming easier and more productive. It gives farmers quick access to important agricultural information, resources, and tools. With features like weather forecasts, advice on managing pests and diseases, tips for keeping soil healthy, crop planning tools, and updates on market prices, this portal covers a wide range of needs. This paper explains how the Agricultural Portal was developed, describes its main features, and shows how it benefits farmers by boosting productivity, helping them make better decisions, and increasing their profits. Built on reliable technology, the portal can be adapted to support farmers of different sizes and locations. It's designed to be easy to use on various devices, including mobile phones and tablets, making it accessible to more people. © 2025 ieee.

关键词： Crops

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Orbiting Mouse for CAD Tools

Orbiting Mouse for CAD Tools

引用

Advanced Computing Technologies (ICoACT), international conference on

作者： K. Raguvaran M Harivigneshraja Hari Priyan M M Kalaivani Electronics and Communication Engineering K.S. Rangasamy College of Technology Namakkal India Electronics and Communication Engineering K.S. Rangasamy College of Technology Namakkal India Department of Electronics and Communication Engineering K.S. Rangasamy College of Technology Namakkal India

ISBN: (数字)9798331509859

ISBN: (纸本)9798331509866

The Orbiting Mouse is the latest input device augmenting three-dimensional control in CAD, and its designer gave special consideration to user precision, ergonomic comfort, and operational efficiency. The main body details the design and implementation of such a device, which pairs an orbital control system with a triple-axis magnetometer, plus customizable function buttons targeting CAD applications. The proposed system greatly simplifies complex 3D manipulation, thus reducing fatigue as an interface integrates seamlessly with CAD applications in the industry. This proves to be an enlightening research study on architecture, application, and potential improvements with its application to contemporary digital design workflows.

关键词： Wrist Wireless sensor networks Three-dimensional displays Navigation Magnetometers Magnetic devices Input devices Fatigue Orbits Software

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Fine-Tuned Yolo V-10X: Real-Time Object Detection for Autonomous Vehicles Under Multiple Weather Conditions

Fine-Tuned Yolo V-10X: Real-Time Object Detection for Autono...

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ieee SSD international Multi-conference on systems, Signals and Devices

作者： Safa Teboulbi Seifeddine Messaoud Mohamed Ali Hajjaji Mohamed Atri Abdellatif Mtibaa Higher Institute of Computer Sciences and Mathematics of Monastir Monastir University Monastir Tunisia Faculty of Sciences of Monastir Monastir University Monastir Tunisia Higher Institute of Applied Sciences and Technology of Sousse Sousse University Sousse Tunisia Computer Engineering Department College of Computer Science King Khalid University Abha Saudi Arabia Systems Integration & Emerging Energies Laboratory National Engineering School of Sfax Sfax University Sfax Tunisia

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Detecting objects such as vehicles, buildings, pedestrians, and road signs is indispensable to advancing the concept of autonomous and self-driving cars. Furthermore, an autonomous vehicle (AV) must accurately detect its surrounding environment to operate reliably. Most object detection (OD) techniques perform adequately under typical weather conditions, including cloudy or sunny days. However, their efficiency decreases significantly when exposed to Adverse Weather Conditions (AWCs), including days with sandstorm, rain, fog or snow. Complex and computationally costly models are required to achieve high accuracy rates. In this study, we present an improved OD system in AWCs for autonomous vehicles (AVs) using the single-stage deep learning (DL) algorithm YOLO (You Only Look Once) version 10. To evaluate our system, Vehicle Detection in Adverse Weather Nature (DAWN) dataset is used. It comprises real-world images captured under various types of AWCs. The experimental findings confirm that the suggested method is effective and surpasses state-of-the-art OD approaches under AWCs.

关键词： YOLO Adaptation models Accuracy Rain Smart cities Snow Roads Real-time systems Reliability Meteorology

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