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Sustainable Agriculture in Food Security Integrating Satellite Data Risk Assessment by Cyberattack Detection: AI Applications

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Remote Sensing in Earth Systems Sciences 2025年第2期8卷 435-443页

作者： Kumar, M. Prema Krishnammal, N. Gupta, Manu Begum, Mairaj Unisa Sultana, Sameera Degala, Divya Priya Dept. of ECE Shri Vishnu Engineering College for Women (A) Andhra Pradesh Bhimavaram 534202 India Department of Computer Science and Engineering Dr. N.G.P. Institute of Technology Tamilnadu Coimbatore India Department of Electronics and Communication Engineering Sreenidhi Institute of Science and Technology Telangana Hyderabad India Department of CSE-AIML MALLAREDDY UNIVERSITY Telangana Hyderabad India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Globally, chronic food insecurity persists and is made worse by shocks brought on by climate change, like floods and droughts. In order to guarantee prompt assistance delivery, humanitarian programming prioritizes predicting levels of food insecurity and identifying homes that are at risk. Using complicated and diverse data, machine learning (ML) models have been widely used to promote food security. This research proposes a novel technique in sustainable agriculture-based food security analysis and satellite data cyberattack detection using machine learning models. Here, the input is collected based on satellite data, and its cyber detection has been analyzed using a convolutional spatio adversarial encoder neural network. Then, from this satellite data, the food security analysis data has been collected and processed for noise removal and normalization. Processed data has been classified using the fuzzy Q-Boltzmann ResNet transfer neural network. Experimental analysis is carried out for the food security analysis dataset in terms of data integrity, detection accuracy, average precision, F-1 score, and recall. The proposed approach yielded 97% detection accuracy, data integrity of 98%, 96% average precision, 93% recall, and 94% F-1 score. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Cyberattack detection Food security analysis Machine learning models Satellite data Sustainable agriculture

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Real-Time Seed Detection and Germination Analysis in Precision Agriculture: A Fusion Model With U-Net and CNN on Jetson Nano

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics 2023年第2期1卷 145-155页

作者： Donapati, Ramesh Reddy Cheruku, Ramalingaswamy Kodali, Prakash National Institute of Technology Warangal Department of Electronics and Communication Engineering Warangal506004 India Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering & Technology Dept. of Ece Hyderabad500090 India National Institute of Technology Warangal Department of Computer Science and Engineering Warangal506004 India

Precision agriculture involves the strategic utilization of resources, precise application of inputs, and continuous monitoring of crop health with the aim of enhancing productivity and sustainability in the field of agriculture. However, seed quality is difficult since natural differences among seed batches may affect germination rates, vigor, and crop performance. Hence, in this article, a novel fusion model for seed detection and germination is proposed. The proposed model combines the U-Net and CNN architectures for seed segmentation and classification, respectively. By harnessing U-Net's capabilities in image segmentation and CNN's strengths in classification, the proposed approach enables effective seed germination analysis. In addition, the model is specifically optimized for real-time processing and applications by implementing it on the NVIDIA Jetson Nano embedded GPU platform. The proposed fusion model achieved 0.91 pixel accuracy, 0.84 intersection over union, and 0.90 precision. The proposed model demonstrated excellent predictive ability when compared with the ResNet50, Inception, and LeNet. In addition, the proposed model requires less number of trainable parameters after LeNet. Further, the proposed model tested in real time and achieved 0.26 ms latency. © 2023 IEEE.

关键词： Crops

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An Approach for XAI Visualizations for Explainability of Alzheimer’s Detection

An Approach for XAI Visualizations for Explainability of Alz...

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International Conference on Networking, Embedded and Wireless Systems (ICNEWS)

作者： Shraddha Khanapur Chirag B Bharadwaj Raghav Bhardwaj Jyothi S Nayak Dept. of Computer Science and Engineering BMS College of Engineering Bengaluru India

ISBN: (数字)9798350367461

ISBN: (纸本)9798350367478

In the field of medical image analysis, the integration of machine learning (ML) models has significantly enhanced the process of identifying and diagnosing Alzheimer’s disease. However, the opacity of these models, often termed as "black boxes," raises concerns regarding their interpretability and reliability in clinical settings. Explainable artificial intelligence (XAI) techniques have emerged as crucial tools to address these challenges by clarifying the reasoning behind model outputs. This paper offers an extensive review of literature concerning the employment of Explainable Artificial Intelligence (XAI) methodologies, such as SHAP, LIME, GradCAM, DeepLIFT, Saliency Maps, and LRP to augment the comprehensibility of ML models applied in the detection of Alzheimer’s disease.

关键词： Wireless communication Analytical models Image analysis Explainable AI Soft sensors Transforms Predictive models Data models Alzheimer's disease Medical diagnostic imaging

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Gaze Detection and Text Extraction from Images using CNN with DBSCAN-based Filter Design

Gaze Detection and Text Extraction from Images using CNN wit...

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International Conference on Advanced computer Control, ICACC

作者： Jithin Jose Ajeesh Ramanujan Dept. Of Computer Science And Engineering College Of Engineering Trivandrum Trivandrum India

ISBN: (数字)9798350379136

ISBN: (纸本)9798350379143

Gaze detection and text extraction are pivotal technologies in the domain of human-computer interaction and computer vision, enabling applications such as assistive technologies, user interface optimization, and automated content analysis. This paper presents a novel method that identifies the user’s gaze direction and extracts the necessary portion of text, which constitutes the user’s region of interest, using a Convolutional Neural Network (CNN) integrated with a spatial attention mechanism. A Density-Based Spatial Clustering of Applications with Noise (DBSCAN)-based filter is incorporated into the system to remove textual noise from the extracted image, thereby improving the model’s accuracy and reducing computation time. The proposed method demonstrates reduced dependency on the CNN compared to previous approaches, resulting in enhanced real-time operation capability and lower latency. Computational optimization and real-time performance were thoroughly evaluated, showing that our system not only achieves high accuracy in gaze detection and text extraction but also maintains efficient processing speeds suitable for dynamic applications. This advancement offers significant improvements for applications in assistive technology, educational tools, and interactive media, where precise and efficient gaze detection and text extraction are critical.

关键词： Human computer interaction computer vision Accuracy Content management Noise Assistive technologies Media Real-time systems Convolutional neural networks Optimization

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Mitigating Crop Losses: AI-enabled Disease Detection in Tomato Plants 20

Mitigating Crop Losses: AI-enabled Disease Detection in Toma...

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20th International Conference on Frontiers of Information Technology, FIT 2023

作者： Ashfaq, Hassan Arshad, Usama Ali, Raja Hashim Ul Abideen, Zain Shah, Muhammad Huzaifa Ali Khan, Talha Ijaz, Ali Zeeshan Ali, Nisar Siddique, Abu Bakar Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain University of Regina Faculty of Electronic Systems Engineering Regina Canada

ISBN: (纸本)9798350395785

Fostering crop health is vital for global food security, underscoring the need for effective disease detection. This research introduces an innovative artificial intelligence (AI) model designed to enhance the detection and diagnosis of diseases in tomato plants, particularly focusing on Early Blight and Late Blight. Significantly, our model leverages cutting-edge image processing techniques to improve disease detection efficiency, outperforming traditional methods in terms of speed and accuracy. Our results demonstrate an impressive model accuracy of 92.58% on training data and 86.83% on validation data, showing the effectiveness of AI in diagnosing plant diseases. These high accuracy rates underline the potential of our model for timely disease classification, allowing for immediate and appropriate interventions. However, our research also identified a potential overfitting problem in the model's performance. To address this, we propose using regularization and data augmentation techniques to enhance the model's generalizability on unseen data. Additionally, we delve into inherent challenges that plague AI-based plant disease detection, such as the scarcity of diverse datasets and the difficulty of achieving broad generalizability across different plant species. In identifying potential solutions for these issues, our research lays the groundwork for the wider and more practical implementation of AI technologies in agriculture. © 2023 IEEE.

关键词： Crops

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Mass Flow Rate of the Flow of Steady-State Viscous Liquid from a Small Vessel into a Vertical Tube

Mass Flow Rate of the Flow of Steady-State Viscous Liquid fr...

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2023 IEEE International Conference on Smart Information Systems and Technologies, SIST 2023

作者： Alipova, Bakhyt International Information Technology University Dept. of Mathematical and Computer Modelling Almaty Kazakhstan University of Kentucky Dept. of Mechanical and Aerospace Engineering LexingtonKY United States

ISBN: (纸本)9798350335040

The problem of calculating the mass flow rate of the flow of steady-state viscous liquid from a small vessel into a vertical tube is considered. Definite calculations of a complex tube with a moving liquid are given. The velocity of the liquid movement, the pressure change are calculated, the laminar property of the liquid movement in this case is confirmed. 11This research was funded by the Science Committee of the Ministry of Education and Science of the Republic of Kazakhstan (Grant No. AP14871641). © 2023 IEEE.

关键词： Mass transfer

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Improve Video Conferencing Quality with Deep Reinforcement Learning 36

Improve Video Conferencing Quality with Deep Reinforcement L...

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36th IEEE/IFIP Network Operations and Management Symposium, NOMS 2023

作者： Tu, Nguyen Van Ko, Kyungchan Ryu, Sangwoo Ha, Sangtae Hong, James Won-Ki Postech Dept. of Computer Science and Engineering Pohang Korea Republic of Postech Graduate School of Artificial Intelligence Pohang Korea Republic of University of Colorado Boulder Dept. of Computer Science CO United States

ISBN: (纸本)9781665477161

Many studies have applied machine learning to bitrate control to increase Quality of Experience (QoE) of video streaming services in highly dynamic networks. However, their solutions mainly focused on HTTP adaptive streaming with one-to-one connections. This paper studies video conferencing applications where multi-party, full-duplex communication happens among participants. In particular, we propose Muno, a Deep Reinforcement Learning (DRL)-based bandwidth prediction framework for multi-party video conferencing systems. Muno learns and predicts an appropriate bitrate for each connection in a multi-party conferencing call. We trained Muno to maximize the QoE of individual connections by constructing a feedback loop between a media server and DRL servers. Our experimental results show that Muno achieves a higher video streaming rate and lower delay compared to state-of-the-art rulebased algorithms. © 2023 IEEE.

关键词： Reinforcement learning

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Assessment Technology for Physiotherapy Practices using Deep Learning

Assessment Technology for Physiotherapy Practices using Deep...

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Advancements in Smart, Secure and Intelligent Computing (ASSIC), International Conference on

作者： Nandhini Vineeth Darshan P M Deepu K Arihant Daragaj Ananth G Prabhu Dept. of Computer Science and engineering BMS College of Engineering Bangalore India

The innovative technology for physiotherapy assessment employing deep learning aims to offer a precise and streamlined approach to evaluating physiotherapy needs. Conventional manual assessment techniques utilized in physiotherapy often consume time and are susceptible to human inaccuracies, resulting in erroneous diagnoses and treatment strategies. This technology tackles these challenges by employing sophisticated deep learning algorithms that identify angles and offer auditory guidance to patients based on their posture. The process commences with capturing the patient's video through a webcam and extracting frames utilizing OpenCV. Following this, the frames undergo processing via a media pipe library, a sophisticated tool designed for body key point detection. These identified key points are subsequently utilized to connect essential body segments for a particular exercise and compute the angular measurements between them. Using these measurements, the system assesses the correctness of the posture, offering audio feedback with repetition counts for accurate postures and tailored guidance for incorrect ones. The audio cues are exercise-specific, delivering precise instructions to aid the patient in refining their posture. The system also keeps track of patient's progress over time and provides a visual display of their improvement. This helps the patient monitor their progress and provides motivation for them to continue with their therapy. The use of learning algorithms and the media pipe library makes this solution an accurate, efficient, and cost-effective method for physiotherapy assessments.

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An Open Hyperspectral Dataset with Sea-Land-Cloud Ground-Truth from the Hypso-1 Satellite 13

An Open Hyperspectral Dataset with Sea-Land-Cloud Ground-Tru...

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13th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2023

作者： Justo, Jon A. Garrett, Joseph Langer, Dennis D. Henriksen, Marie B. Ionescu, Radu T. Johansen, Tor A. Norwegian University of Science and Technology Dept. Engineering Cybernetics Trondheim Norway Norwegian University of Science and Technology Dept. Marine Technology Trondheim Norway University of Bucharest Dept. of Computer Science Romania

ISBN: (纸本)9798350395570

Hyperspectral Imaging, employed in satellites for space remote sensing, like HYPSO-1, faces constraints due to few labeled data sets, affecting the training of AI models demanding these ground-truth annotations. In this work, we introduce The HYPSO-1 Sea-Land-Cloud-Labeled Dataset, an open dataset with 200 diverse hyperspectral images from the HYPSO-1 mission, available in both raw and calibrated forms for scientific research in Earth observation. Moreover, 38 of these images from different countries include ground-truth labels at pixel-level totaling about 25 million spectral signatures labeled for sea/land/cloud categories. To demonstrate the potential of the dataset and its labeled subset, we have additionally optimized a deep learning model (1D Fully Convolutional Network), achieving superior performance to the current state of the art. The complete dataset, ground-truth labels, deep learning model, and software code are openly accessible for download at the website https://***/hypso1_sea_land_clouds_dataset/. © 2023 IEEE.

关键词： Remote sensing

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A Recent Survey on Bone Cancer Detection Using Deep Learning Techniques

A Recent Survey on Bone Cancer Detection Using Deep Learning...

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International Conference on Advances in Information Technology (ICAIT)

作者： Murugan S Shravani S Patil Shree Deeksha V Rohith K Reddy Shravanth Kumar K N Dept. Of Computer Science And Engineering Sri Venkateshwara College Of Engineering Bengaluru

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

The emergence of deep learning methods in medical image analysis has transformed the detection and categorization of bone tumors, presenting encouraging pathways for enhanced diagnosis and treatment strategizing. This literature review synthesizes recent advancements in utilizing deep learning architectures, particularly convolutional neural networks (CNNs), for the automatic interpretation of various imaging modalities including X-rays, CT scans, MRI scans, and bone scintigrams in the context of bone cancer detection and classification. Each study discussed presents unique methodologies combining traditional image processing techniques also the augmentation techniques with deep learning models to accurately identify, categorize, and segment bone lesions. Despite the encouraging results in terms of accuracy, sensitivity, and specificity, several common limitations are identified across the studies, including small sample sizes, potential biases, limited validation on diverse datasets, and a lack of thorough comparisons with existing techniques or expert interpretations. Addressing these limitations through larger and more diverse datasets, rigorous validation protocols, uncertainty quantification, and consideration of clinical outcomes would enhance the reliability, generalizability, and clinical utility of the proposed deep learning-based methodologies. Overall, these studies underscore the immense potential of deep learning approaches in advancing the field of bone cancer diagnosis and management, paving the way for more robust and clinically applicable tools to assist medical professionals.

关键词： Deep learning Image segmentation Uncertainty Magnetic resonance imaging X-rays Sensitivity and specificity Bones Cancer detection Medical diagnostic imaging Cancer

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