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GeoAgriGuard: AI-Driven Pest and Disease Management with Remote Sensing for Global Food Security

Remote Sensing in Earth Systems sciences 2025年第2期8卷 409-422页

作者： Sharada, K. Choudhary, Shailee Lohmor Harikrishna, T. Dixit, Rinku Sharma Suman, Sanjay Kumar Ayyappa Chakravarthi, M. Bhagyalakshmi, L. Department of Computer Science and Engineering GITAM (Deemed to Be University) AP Visakhapatnam India Department of Artificial Intelligence and Machine Learning New Delhi Institute of Management Delhi India Department of AIML Annamacharya University Andhra Pradesh Rajampet India Department of ECE St. Martin’s Engineering College Telangana Secunderabad India Departmentt of CSE-Data Science KSR Institute of Technology and Science KKR & AP Guntur India Department of ECE Rajalakshmi Engineering College TN Chennai India

Agricultural productivity is increasingly threatened by the growing frequency and severity of pest and disease outbreaks, exacerbated by climate change and evolving environmental conditions. The GeoAgriGuard AI-Driven Pest and Disease Management System integrates remote sensing technologies with advanced AI models to address pest and disease outbreaks in agriculture. Utilizing multi-spectral and hyper-spectral satellite and drone imagery, the system processes crop health data through comprehensive preprocessing steps, including radiometric, geometric, and atmospheric corrections. Vegetation indices such as NDVI and NDWI further enhance the data quality, ensuring accurate detection of crop stress. A hybrid deep learning ensemble, combining models like ResNet, Transformer, DenseNet, and AutoEncoders, processes this data to predict and classify pest and disease occurrences. The proposed model achieved a remarkable accuracy of 97.81%, significantly outperforming existing models such as SVM (82.45%) and CNN (90.12%). By integrating these AI predictions with GIS-based spatial analysis, the system generates real-time risk maps, enabling farmers to take timely and localized actions. Performance metrics, including precision (96.72%), recall (95.83%), and F1-score (96.27%), demonstrate the system’s reliability in real-world applications. GeoAgriGuard provides a scalable, data-driven solution for enhancing crop protection and improving global food security. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： AI-driven agriculture Crop disease Deep learning Food security GIS NDVI Pest detection Precision agriculture Remote sensing Satellite imagery

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A Hybrid Deep Learning Framework for Efficient Network Intrusion Detection Systems using Data Augmentation

A Hybrid Deep Learning Framework for Efficient Network Intru...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Priya Shirley Muller K Madhavi Naga Durga Saile K B Prasanthi B Jayaram Goski Sathish Department of Mathematics Sathyabama Institute of Science and Technology Chennai India Department of Mathematics Mallareddy Engineering College (Autonomous) Hyderabad India Department of Computer Science and Engineering (AIML& IoT) VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad India Departmentt of Computer Science (AI&ML) CVR College of Engineering Hyderabad India Department of Computer Science and Engineering RMK Engineering College Gummidipoondi India Department of Information Technology St.Martins Engineering College (Autonomous) Hyderabad India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Because of their critical position as the initial barrier against harmful actions, intrusion detection systems (IDSs) rely substantially on the efficacy of cybersecurity in protecting corporate communication. There are several limits to machine learning approaches that be solved by utilizing other deep learning architectures, even though they are widely used for intrusion detection. In addition, unbalanced datasets frequently make it difficult to evaluate the suggested models, which prevents a thorough evaluation of the models' effectiveness. This study seeks to tackle these difficulties by improving the performance of numerous deep-learning(DL) structures for IDS using data augmentation approaches. The experimental results shown that even a basic CNN-based architecture may produce very efficient network-attack-detection, but more complicated designs only managed to show slight performance increases. The results show that cybersecurity frameworks can easily incorporate the suggested deep learning-based intrusion detection approaches, which improves the capacity to identify and counteract complex network threats. The outcomes of this research exhibit that the accuracy of intrusion detection models is highly dependent on the dataset's quality and quantity, with augmented CIC-IDS-2017 showing an accuracy of up to 92%.

关键词： Deep learning Accuracy Communication systems Network intrusion detection Complex networks Data augmentation Integrated circuit modeling computer crime

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Smart Harvesting Solutions: Robotics in Fruit and Vegetable Harvesting for Reduced Labor Dependency

Smart Harvesting Solutions: Robotics in Fruit and Vegetable ...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： V Sathiyasuntharam S.Navaneethan Yamuna Devi S Aman Grewal Ishan Sandhu R.Alarmelu Mangai Departmentt of CSE School of Engineering and Technology Sharda University Greater Noida Uttar Pradesh India Department of ECE Saveetha Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Panimalar Engineering College Chennai Tamil Nadu India Department of Computer Science Donald Bren School of Information and Computer Sciences University of California Irvine California United States of America Department of Computer Science and Engineering School of Engineering Santa Clara University California United States of America Department of Computer Science Dr. SNS Rajalakshmi College of Arts and Science Coimbatore Tamil Nadu India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Traditional agricultural harvest has a large inefficiency and a dependence on labor, as there is a serious shortage of manpower in many regions. Most mechanized systems are not precise, incur high production damage rates, and cannot flexibly harvest multiple crops or be selective based on ripeness. The paper presents a robotic harvesting system that aims to automatically harvest fruit and vegetables with high reliability, using computer vision, Convolutional Neural Network (CNN), depth sensors, and reinforcement learning. Adaptive arm control minimizes produce handling while maximizing yield and classification reducing waste by classifying ripeness and implicitly planning optimal picking sequence. Experimental results show a remarkable performance boost with 95% accuracy in detecting mature fruits, 50% reduction in time/acre for harvesting & 5% damage rates which significantly brings down operational price. These provide a sustainable, scalable solution to address agricultural requirements and improve both the quality of yield and farm labor.

关键词： computer vision Costs Decision making Crops Robot sensing systems Agriculture Sensor systems Sensors Convolutional neural networks Sustainable development

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On unsupervised segmentation of color texture images

On unsupervised segmentation of color texture images

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International Conference on High Performance Computing

作者： R. Krishnamoorthi P. Bhattacharyya Departmentt. of Computer Science and Engineering Indian Institute of Technology Kharagpur India

The authors propose an orthogonal polynomial based color texture model for color texture segmentation. The proposed model is based on a complete set of difference operators which are easily configurable from a set of orthogonal polynomials. The operators are employed to represent a color image region as a linear combination of the operator's responses towards texture and noise. A simple statistical design of experiments paradigm is used for separating out the responses towards color texture from the responses towards noise. A local descriptor for color texture called cpronum is computed by encoding only the significant responses towards texture. Finally a global descriptor for color texture called cprospectrum is obtained by measuring the frequency of occurrences of the cpronums in the color image. The usage of the proposed color texture representation scheme is highlighted in unsupervised color texture segmentation.

关键词： Image segmentation Colored noise Color Polynomials Equations computer science Image coding Frequency measurement Image processing computer vision

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Analysis of the effect of size of omega network on its fault tolerance behaviour in presence of multiple faults

Analysis of the effect of size of omega network on its fault...

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IEEE Region 10 International Conference TENCON

作者： S. Das K. Chaudhuri Departmentt. of Computer Science & Technology B.E. College (D.U.) Howrah India Departmentt of Computer Science & Engineering Jadavpur University Calcutta India

The paper deals with comprehensive treatment of the fault tolerant characteristics of a class of nonredundant multistage interconnection network. The observations and results presented are around omega networks, as a representative member of the class of topologically equivalent nonredundant multistage interconnection network. As fault model, the paper considers that the multiple switching element fault may be in the same stage or in different stages. Several relations and expressions on different parameters and size of the network are derived to estimate the behaviour of the network of different spread towards variety of fault distribution.< >

关键词： Fault tolerance Intelligent networks Redundancy Fault detection Switches Costs Network topology Multiprocessor interconnection networks

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Adoption of Precision Agriculture Process with Wireless Environment Plant Disease Monitoring System using Smartphone 7

Adoption of Precision Agriculture Process with Wireless Envi...

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7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023

作者： Rajabalayanan, S.R. Barkunan, S.R. Nirmaliswarran, S.R. Hindusthan Institute of Technology Departmentt of Mechanical Engineering Coimbatore India K.Ramakrishnan College of Technology Department of Ece Tiruchirappalli India Psg Institute of Technology and Applied Research Department of Computer Science & Engineering Coimbatore India

ISBN: (纸本)9798350341485

Agriculture is a backbone of India. Precision agriculture focuses on delivering tools for harvest data, task coordination, and growth updates, thereby enhancing agricultural productivity through the incorporation of sensor network technologies to monitor various farming activities. This studyr introduces a solar-powered wireless system for detecting plant diseases in the environment. A comprehensive wireless sensing system is showcased, comprising sensors for temperature, humidity, and soil moisture, along with a microcontroller, IR transmitter and receiver, camera, and GSM module, enabling extended periods of environmental monitoring ARM processor used for ADC and sends the collected data to mobile via GSM. A camera is periodically enabled for capturing the plant picture for diseases monitoring and IR unit used for the purpose of plant growth monitoring. It use soil energy to power the temperature sensor to develop plant growth monitoring system and plant disease monitoring, also develop android application to monitor such conditions by using this technology to achieve a green electronics system. © 2023 IEEE.

关键词： Temperature sensors

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Local convexity preserving rational cubic spline curves

Local convexity preserving rational cubic spline curves

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International Conference on Information Visualisation (IV)

作者： M. Sarfraz M. Hussain Z. Habib Information &Computer Science Departmentt. KFUPM Dhahran Dhahran Saudi Arabia Departmentt.of Mathematics Punjab University Lahore Pakistan National College of Textile Engineering Faisalabad Pakistan

A scheme for generating plane curves which interpolates given data is described. A curve is obtained by patching together rational cubics and straight-line segments which, in general, is C/sup 1/ continuous. It is a local scheme which controls the shape of the curve and preserves the shape of the data by being local convexity-preserving. A particular scheme is suggested which selects the tangent vectors required at each interpolation point for generating a curve. An algorithm is presented which constructs a curve by interpolating the given data points. This scheme provides a visually pleasant display of the curve's presentation. An extra feature of this curve scheme is that it allows subsequent interactive alteration of the shape of the default curve by changing the shape control parameters and the shape-preserving parameters associated with each curve segment. Thus, this feature is useful for further enhancing the user satisfaction, if desired.

关键词： Spline Shape control Interpolation Visualization

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Scheduling of DSP data flow graphs with processing times characterized by fuzzy sets

Scheduling of DSP data flow graphs with processing times cha...

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Canadian Conference on Electrical and computer engineering (CCECE)

作者： A. Itradat M.O. Ahmad A. Shatnawi Departmentt of Electrical and Computer Engineering Concordia University Montreal QUE Canada Department of Computer Engineering Jordan University of Science and Technology Irbid Jordan

In recent years a great deal of research has been conducted in the area of scheduling DSP data flow graphs onto multiprocessing systems. Most of the static scheduling techniques assume the worst case or the best case computational delay of the functional units used in the target architecture. This assumption is not realistic, since some of the computational times of the DSP tasks may be imprecise due to the fact that during early design phases, the characteristics of the final implementation of the functional units are not be known. In this paper, the impreciseness of the processing times of the functional units is taken into consideration by considering them as fuzzy sets, and then using fuzzy arithmetic to build the time schedule. The range of control steps (mobility) which represents the possible firing times of a task is determined, and a fuzzy rule base is employed to infer the degree of acceptability in selecting a certain control step within this range.

关键词： Digital signal processing Flow graphs Fuzzy sets Processor scheduling Signal processing High level synthesis Circuit synthesis Signal synthesis Speech synthesis Multiprocessing systems

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Design and development of Delay Tolerant Network: A case study

Design and development of Delay Tolerant Network: A case stu...

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Nirma University International Conference on engineering (NUiCONE)

作者： R. S. Mangrulkar Mohammad Atique B.D.College of Engineering Sevagram Wardha Maharashtra India P.G. Departmentt of Computer Science S.G.B.Amravati University Maharashtra India

Routing is an issue of great interest for opportunistic network called Delay Tolerant Network. The majority of routing schemes that have been proposed till now are broadly classified as flooding and forwarding strategies. All these are categorized on the basis of the important characteristic of any routing protocol i.e. message transmission capability. In this paper, author summarize work as case study in Bapurao Deshmukh College of engineering, Sevagram for designing and developing Delay Tolerant Network: A Self Capable Network for routing message through the intermediate node devices to the destination. These devices are capable of routing original message of source node by using flooding technique to deliver ultimately to the targeted receiver of the original message. The hardware device which is designed is successfully mounted and demonstrated to study the working of delay tolerant network. The devices are capable of transmitting, receiving and storing messages.

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Adoption of Precision Agriculture Process with Wireless Environment Plant Disease Monitoring System using Smartphone

Adoption of Precision Agriculture Process with Wireless Envi...

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： S. R. Rajabalayanan S. R. Barkunan S. R. Nirmaliswarran Departmentt of Mechanical Engineering Hindusthan Institute of Technology Coimbatore India Department of ECE K.Ramakrishnan College of Technology Tiruchirappalli India Department of Computer Science & Engineering PSG Institute of Technology and Applied Research Coimbatore India

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