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Comprehensive Approaches to Agricultural Intelligence: U-Net, YOLO, and Random Forest in Practice 3

Comprehensive Approaches to Agricultural Intelligence: U-Net...

引用

3rd international conference on Communication, Control, and intelligent Systems, CCIS 2024

作者： Gavas, Saharsh Kosamkar, Pranali Pune India Dr. Vishwanath Karad Mit World Peace University Dept Computer Engineering and Technology Pune India

ISBN: (纸本)9798331528201

This paper seeks to implement and test advanced precision agricultural monitoring and detection systems derived from machine learning, remote sensing, and sensor technologies to improve pest detection, crop yields assessment, and animal husbandry management practices. The approach involves an automated and hierarchical framework integrating cutting edge machine learning technologies with fine spatial resolution remote sensing images and sensors. For instance, the authors apply CNN (convolutional neural networks) for pest identification;the deep learning models trained on Sentinel-2 images serve as yield predictors;UWB location coordinates and accelerometric measures combined form livestock behavior monitoring. This methodology encompasses the stages of implementation, testing and finally verification of the model. A variety of machine learning systems such as Faster-PestNet or deep semantic segmentation algorithms process data while remote sensing and the sensors' data were used in predictions and classifications. This allows using different performance measures such as accuracy, precision recall rates and RMSE (root mean square error) to test the systems. © 2024 IEEE.

关键词： Semantic Segmentation

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intelligent Parking System to Prevent Unauthorized Parking of Vehicles using IoT and R-CNN with YOLO 3

Intelligent Parking System to Prevent Unauthorized Parking o...

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Vispute, Sushma Potdar, Manasi Pawara, Pranjal Patil, Shreya Patil, Satvika Ahirrao, Dinesh Bhaskar Dept. of Computer Engineering Pimpri Chinchwad College Of Engineering Pune India KCES’s College Of Engineering and Management Jalgaon India

ISBN: (纸本)9798331527549

In recent years, the proliferation of private vehicles has led to a significant challenge in finding adequate parking space, with unauthorized parking becoming a prevalent issue in many residential societies. To address this problem, we propose a novel solution that integrates machine learning techniques and motion detection technology to effectively manage parking spaces and curb unauthorized parking. Our approach utilizes advanced machine learning models such as RCNN for object detection and YOLOv8 for license plate detection, complemented by optical character recognition (OCR) for character recognition. Additionally, the ESP32-CAM webcam module is employed to capture real-time images, ensuring seamless integration with our system. This combination of technologies allows for the accurate detection of unauthorized parking instances in real time, providing efficient management of parking spaces within societies. Key features of the system include automated vehicle identification, real-time monitoring, and notification alerts, all of which contribute to a robust verification system for resident and guest vehicles. By leveraging these advanced technologies, our solution not only streamlines the parking process but also promotes a more organized and peaceful living environment. This innovative approach addresses a critical issue in urban settings, paving the way for more efficient and sustainable parking management practices. © 2025 IEEE.

关键词： Convolutional neural networks

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Upgraded Machine learning Techniques to Forecast Obesity Hazards 3

Upgraded Machine Learning Techniques to Forecast Obesity Haz...

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3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Sukanya, N.S. Asha, V. Vasumathi, M.T. Chakraborty, Sumit Sushanth, S.M. Kumari, Supriya Department of Master of Computer Applications New Horizon College of Engineering Bengaluru India

ISBN: (纸本)9798331527549

In recent years, investigators have prolonged their research on predicting obesity risks using Machine learning (ML) techniques. Obesity is an intricate condition with multiple contributing factors, such as genetics, behaviour, environment, and social factors. Predicting obesity risk through ML can aid recognise individuals who may be at risk also aids in early identification to prevent or manage the condition. There is a need for these predictive models which is a combination of various factors like age, medical history, and lifestyle and these are the probability of increasing obesity among individuals. The model aims to accurately predict the obesity risks as early as possible. Detecting obesity at an initial stage is a stimulating procedure. This paper aims to use ML models such as XGBoost, CatBoost and LightGBM to predict obesity at a primary stage. To predict the obesity, Nobeyesdad dataset from Kaggle is used in this study. The obesity prediction model classifies the data into multiple categories like normal, insufficient weight, overweight and obesity individuals. The results of the enhanced obesity prediction model generates ideal results by classifying the datasets which aids the specialists to make decisions easily. © 2025 IEEE.

关键词： Prediction models

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IoT-Enabled Reinforcement learning for Autonomous Robotic Lawn Mowers in Residential Gardens 3

IoT-Enabled Reinforcement Learning for Autonomous Robotic La...

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Helenprabha, K. Amarnath, Raveendra N. Mohankumar, N. Gopi, B. Aishwarya, N. Murugan, S. Department of Electronics and Communication Engineering R.M.D.Engineering College Tamil Nadu Chennai India Mphasis Campbell Dr MelissaTX United States Nagpur Campus Pune India Innovation Center Department of Biomedical Engineering Sona College of Technology Tamil Nadu Salem India Tamil Nadu Salem India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India

ISBN: (纸本)9798331527549

Traditional automated lawn mowers sometimes have difficulties in navigating complex garden designs and changing to changing conditions, which causes ineffective efficiency and limited coverage. This research introduces a new way to improve the efficiency and autonomy of robotic lawn mowers in residential settings by combining Reinforcement learning (RL) algorithms with Internet of Things (IoT) technologies. To help the robotic mower respond appropriately to environmental signals it presents a system that uses IoT sensors to collect data on topography, weather, and vegetation in real-time. It uses RL methods to make the mower better at mowing and navigating over time. With the autonomous mower attaining more accuracy in grass cutting while reducing energy consumption and operating expenses, our trial findings show considerable increases in coverage and efficiency compared to traditional techniques. Building on previous work in smart gardening technology to the body of understanding on IoT enabled autonomous systems for home use. The research addresses inefficient lawn management through the integration of IoT and RL, resulting in adaptive, accurate, and dependable robotic mowing for enhanced energy efficiency and coverage. © 2025 IEEE.

关键词： Reinforcement learning

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Deep learning-based Plant Species Identification using VGG16 and Transfer learning: Enhancing Accuracy in Biodiversity and Agricultural Applications 3

Deep Learning-based Plant Species Identification using VGG16...

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Kiran, K. Kaur, Arpanpreet School of Computer Science and Artificial Intelligence SR University Telangana Warangal506371 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798331527549

In this paper, the method of deep learning is applied for plant species identification, with a special emphasis on Convolutional Neural Network VGG16 for feature extraction and classification. Identification of plants is important in matters concerning biological diversity, agriculture as well as management and utilization of resources but the conventional methods used in identification are time consuming and sometimes inaccurate. Machine learning specifically deep learning is a more suitable replacement by offering a fast and accurate identity recognition. The research work works with a Kaggle dataset comprising of 24881 images of four plant species categorized into twenty-two with the objectives of increasing efficiency of the classification. We used transfer learning with weights from the ImageNet data set and obtained accuracy greater than 90% on the test data set, which shows the model’s ability to identify plant species based on leaf structure. To counter overfitting and enhance model generality, data augmentation methods were employed where the input images were rotated by 30, 45 and 60 degrees, and flipped vertically. The performances suggest that deep learning models, such as VGG 16, yield improved recognition for plant classification than conventional methods provide higher reliability. This research makes a valuable input toward a number of applications related to automated systems for agriculture and conservation, discussing possible uses of AI technique for better penetration of plant species, enhancing conservation goals, and future advancements in the agrarian field. Future research will leverage on these discoveries to make improvements on classification frameworks. © 2025 IEEE.

关键词： Plant diseases

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automated Helmet Detection and Fine Issuance System using YOLOv8 & OpenCV 3

Automated Helmet Detection and Fine Issuance System using YO...

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Praveena, Hirald Dwaraka Babu, Goruva Vardhan Yateesh, Jinka Muni Sai Guna, Degasikshi Krishna, Guduru Vamsi Tirupati India Dept of ECE Sree Vidyanikethan Engineering College Tirupati India

ISBN: (纸本)9798331527549

In densely populated countries like India, traffic violations are a major concern, especially among motorcyclists, leading to accidents and significant loss of life and property. A prevalent issue is the failure to wear helmets. To address this, a system called "Helmet Detection for Motorcyclists using YOLOv8 and OpenCV" is proposed, leveraging deep learning to automatically identify and penalize violators in real-time through traffic cameras. The system employs the You Only Look Once (YOLO) algorithm, a deep neural network-based object detection model, to identify riders without helmets. Subsequently, it detects the number plate of violators, extracting its text using the EasyOCR(Optical Character Recognition) library. This extracted data is cross-referenced with a pre-existing database to retrieve the owner’s contact details. For automation, an SMS API(Application Programming Interface) is integrated to send fine notifications to the violator’s registered mobile number. By encouraging helmet use, the system aims to minimize head injuries during accidents and improve road safety. This automated approach enhances traffic law enforcement, provides evidence of violations, and ensures efficient penalty issuance. Ultimately, the system supports safer roads by promoting compliance with traffic regulations and protecting motorcyclists' lives. © 2025 IEEE.

关键词： Deep neural networks

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Machine learning based Classifier Models for Detection of Celestial Objects 3

Machine Learning based Classifier Models for Detection of Ce...

引用

3rd IEEE international conference on intelligent Technologies, CONIT 2023

作者： Sharma, Vikrant Goel, Sandeep Jain, Anuj Kumar Vajpayee, Amit Bhandari, Rahul Tiwari, Raj Gaurang GraphicEra Hill University Uttarakhand Dehradun India Panipat Institute of Engineering & Technology Department of Information Technology Haryana India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Punjab Mohali India Chandigarh University Department of Computer Science & Engineering Punjab Mohali India

ISBN: (纸本)9798350338607

The classification of celestial objects such as stars, galaxies, and quasars is one of astronomy's most difficult and fundamental problems. Due to the technological advancement of telescopes and observatories, the classification of large volumes of data must be automated. Various machine learning techniques are currently employed for accurate classification. In this paper, a comparison of the efficacy of various classification algorithms is presented. XGBoost seems to be the most effective. © 2023 IEEE.

关键词： Machine learning

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Hive Destruction Protocol: An automated Defense System Utilizing YOLO Human Recognition and Real-Time GPS Tracking 3

Hive Destruction Protocol: An Automated Defense System Utili...

引用

3rd international conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Vyshak, T.M. Gaayana, G.R. Sarala, T. Dept. of Electronics and Communication Engineering Bnm Institute of Technology Affiliated to Vtu Bengaluru India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515911

The Hive Destruction Protocol (HDP) introduces a cutting-edge automated border defense system that enhances security operations through advanced human recognition using YOLO, precise GPS tracking, and coordinated drone responses. Soldiers are equipped with camera-integrated spectacles that facilitate the rapid identification of enemy targets via visual cues and hand gestures. Upon confirming a target, the system transmits accurate GPS coordinates in real time to autonomous drones, which are deployed to neutralize threats from multiple angles, creating confusion and minimizing detection risk. To ensure reliability and real-time processing in dynamic environments, HDP employs optimized communication protocols for secure data exchange between ground personnel and drones, allowing for seamless coordination among multiple units, each capable of independently engaging identified threats based on shared GPS data. The integration of machine learning algorithms improves the system even more flexible to changing field conditions, improving detection accuracy and robustness under various environmental constraints. Overall, the Hive Destruction Protocol represents a forward-thinking solution for border defense, leveraging artificial intelligence and automation to provide proactive and responsive capabilities in critical security applications. © 2025 IEEE.

关键词： Target drones

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Transforming Rice Disease Detection: VGG16 CNN for automated Leaf Disease Classification 3

Transforming Rice Disease Detection: VGG16 CNN for Automated...

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3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Ankushavali, Md. Sharma, Jatin School of Computer Science and Artificial Intelligence SR University Telangana Warangal506371 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798331527549

About half of the world's population depends on rice as a basic food;but many leaf diseases—including Tungro, Brown Spot, Blast, and Bacterial Blight—threatens its output. Early and accurate diagnosis of these diseases is essential for good crop management since often-manual inspection methods are slow and prone to error. This work uses the VGG16 convolutional neural network (CNN) model to examine automatic categorization of rice leaf diseases. Created from the dataset were training, validation, and testing sets to assess model performance. After training, the model showed remarkable overall accuracy 95% great precision, recall, and F1-scores over all disease categories. The VGG16 model seems to be a useful instrument for improving control and detection of rice leaf disease. Quick diagnosis of disease symptoms allows this automated approach to significantly improve crop management strategies, reduce farmer economic losses, and therefore guarantee food security. The study highlights the opportunities of deep learning technology in agricultural applications, therefore providing the route for more continuous and efficient disease detection methods in rice farming. © 2025 IEEE.

关键词： Convolutional neural networks

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Air Pollution Forecasting using Machine learning with Temporal Fusion Transformer and Graph Neural Networks 3

Air Pollution Forecasting using Machine Learning with Tempor...

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3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Rath, Subhashree Maneesha, P. Dept.of.Computer Science and Engineering New Horizon College Of Engineering Autonomous College Affiliated to VTU Bengaluru560103 India

ISBN: (纸本)9798331527549

Air pollution is a significant obstacle to achieving urban sustainability and public health, particularly in rapidly developing regions. Accurate air quality prediction is essential for proactive pollution management and policy formulation. In this study, we employ sophisticated machine learning frameworks, such as Temporal Fusion Transformer (TFT) and Graph Neural Networks (GNNs), to predict air quality levels. TFT is designed to capture temporal dependencies, making it ideal for time-series forecasting, while GNNs model spatial relationships, enabling the analysis of pollutant dispersion across monitoring stations .A synthetic dataset comprising 50 rows was generated, simulating pollutant concentrations (PM2.5, PM10, CO, NO2, SO2) and meteorological variables (temperature, humidity, wind speed). The models were assessed using criteria like R2, RMSE, and MAE. TFT achieved an R2 of 0.97 and an RMSE of 4.2 µg/m3, significantly outperforming traditional models like Random Forest and XGBoost. GNN provided spatial insights, identifying pollutant hotspots and their dispersion patterns. This research highlights the superior accuracy and scalability of TFT and GNN for air quality prediction. The results underline their potential for real-time monitoring, smart city integration, and proactive environmental management, offering a robust solution for addressing urban air pollution challenges. © 2025 IEEE.

关键词： Graph neural networks

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