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Plant Disease detection using machine learning and CNN on leaf images 1

Plant Disease detection using Machine learning and CNN on le...

引用

1st International conference on Advanced Computing and Emerging Technologies, ACET 2024

作者： Bhargava, Rashmi Upadhyay, Santosh Kumar Sharma, Rakshita Srivastava, Nittyansh Mavi, Harsh Ajay Kumar Garg Engineering College Ghaziabad India

ISBN: (纸本)9798350367720

This paper proposes a system for detection of the diseases in plants using machine learning and image processing technique to analyze the images of leaves and fruits. Leveraging convolutional neural networks (CNNs) and various machine learning approaches, our system aims to accurately detect and classify plant diseases for improved agricultural management. We draw inspiration from previous studies, incorporating methodologies such as data augmentation, feature extraction, and outlier identification to enhance detection accuracy and efficiency. The proposed system is evaluated using diverse datasets, encompassing a range of plant species and disease types, to ensure robustness and generalization. Through experimentation, we demonstrate the efficiency of CNNs and algorithms of machine learning in detecting diseases and classifying plant species, highlighting their potential for real-world applications in agriculture. Additionally, we focused on the importance of selection of dataset, evaluation metrics, and optimization of model for achieving reliable disease detection results. © 2024 IEEE.

关键词： Adversarial machine learning

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Gesture Recognition of Filipino Sign Language Using Convolutional and Long-Short Term Memory neural Network 1

引用

Intelligent Systems conference (IntelliSys)

作者： Cayme, Karl Jensen F. Retutal, Vince Andrei B. Salubre, Miguel Edwin P. Canete, Luis Gerardo S. Astillo, Philip Virgil B. Univ San Carlos Cebu Philippines

ISBN: (数字)9783031477249

ISBN: (纸本)9783031477232;9783031477249

Sign language is a form of communication prominently used by the deaf-mute community to convey their ideas and thoughts. In the Philippines, local signers use Filipino Sign Language (FSL) derived from the well-known American Sign Language (ASL). Despite the recent formalization of FSL as the country's official sign language, there is still minimal familiarity among the public. That said, Sign Language Recognition (SLR) systems integrated with machine learning applications have been developed to understand FSL better. However, the prevalent limitations of most of these systems are that it only involves static signs and asynchronous recognition. This study aimed to take this solution further and overcome existing limitations by developing a model capable of recognizing FSL gestures in real-time usable for applications such as in government service centers. To this end, the study proposes the deep learning algorithm Convolutional and Long Short-Term Memory neural networks in system capturing of real-time signs from a signer. The proponents considered 15 signs related to common greetings and business transactions. A total of 450 video recordings were collected for the signs with each having an equal number of samples. The collected data underwent cleaning, preprocessing, and augmentation before training. The proposed model's performance was analyzed with the following classification metrics: Accuracy, Precision, Recall, and F1-Score, and was able to achieve 95% accuracy and a macro-average of 0.95 precision, 0.95 Recall, and 0.95 F1-Score. Furthermore, the model had a comparable accuracy and loss between validation and test data-a 95.18% accuracy and 0.13629 loss on validation while 95.93% accuracy and loss of 0.1478 on the test. With that said, the proposed model was well-fit for classifying the 15 signs that involve upper body movements.

关键词： Computer vision CNN-LSTM Deep learning Filipino sign language image processing Sign language recognition system machine learning

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UplinkNet: Practical Commercial 5G Standalone (SA) Uplink Throughput Prediction

UplinkNet: Practical Commercial 5G Standalone (SA) Uplink Th...

引用

2024 conference on Visual Communications and image processing

作者： Arunruangsirilert, Kasidis Katto, Jiro Waseda Univ Dept Comp Sci & Commun Engn Tokyo Japan

ISBN: (纸本)9798331529543;9798331529550

While 5G New Radio (NR) networks offer significant uplink throughput improvements, these gains are primarily realized when User Equipment (UE) connects to high-frequency millimeter wave (mmWave) bands. The growing demand for uplink-intensive applications, such as real-time UHD 4K/8K video streaming and Virtual Reality (VR)/Augmented Reality (AR) content, highlights the need for accurate uplink throughput prediction to optimize user Quality of Experience (QoE). In this paper, we introduce UplinkNet, a compact neural network designed to predict future uplink throughput using past throughput and RF parameters available through the Android API. With a model size limited to approximately 4,000 parameters, UplinkNet is suitable for IoT and low-power devices. The network was trained on real-world drive test data from commercial 5G Standalone (SA) networks in Tokyo, Japan, and Bangkok, Thailand, across various mobility conditions. To ensure practical implementation, the model uses only Android API data and was evaluated on unseen data against other models. Results show that UplinkNet achieves an average prediction accuracy of 98.9% and an RMSE of 5.22 Mbps, outperforming all other models while maintaining a compact size and low computational cost.

关键词： 5G Standalone machine learning Throughput Prediction Radio Access Network Wireless Communication

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Cross-Dataset Generalization in -Based Plant Disease Recognition 2

Cross-Dataset Generalization in -Based Plant Disease Recogni...

引用

2nd International conference on Artificial Intelligence and machine learning applications, AIMLA 2024

作者： Sathiyapriya, N. Ram Shankar, S. Suresh Krishna, R. Viknesh, V. K.S.Rangasamy College of Technology Department of Information Technology Namakkal Tamil Nadu Tiruchengode India

ISBN: (数字)9798350349221

ISBN: (纸本)9798350349221

Plant diseases recognition large crop losses and have negative economic effects, which makes them a serious danger to the world's food security. Early and accurate disease diagnosis is essential for efficient disease control and mitigation. Convolutional neural networks (CNNs) are a viable method for diagnosing plant diseases based on visual symptoms because of their impressive performance in image recognition tasks in recent years. In this work, we present a novel CNN algorithm created especially for the diagnosis of plant diseases. The accuracy and resilience of disease classification are increased by the suggested CNN architecture's tuning, which is designed to extract the intricate patterns and variations of plant illnesses from photos. To enhance the model's capacity for generalisation, extensive datasets including various plant species and disease symptoms are gathered and subjected to pre-processing techniques that standardise image quality. Using a stochastic gradient descent technique on the pre-processed CNN weights, the training procedure The model's hyper parameters are fine-tuned to prevent overfitting and improve performance on a separate validation set. An independent test data set is used to thoroughly assess the trained CNN in order to calculate a confusion matrix and quantify accuracy, recall, F1 score, and precision. © 2024 IEEE.

关键词： Convolutional neural networks

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Wavelet Based image Restoration via Multi-Scale Decomposition 2

Wavelet Based Image Restoration via Multi-Scale Decompositio...

引用

2nd International conference on Artificial Intelligence and machine learning applications, AIMLA 2024

作者： Gunasekar, M. Akshay Kumar, R.S. Hariyini, R. Logeshwaran, K. Kowshik, T. Balaji, J. Prasannakumar, K.R. Kongu Engineering College Department of Information Technology Tamil Nadu Erode India

ISBN: (数字)9798350349221

ISBN: (纸本)9798350349221

image denoising is one of the fundamental task in digital image processing, aimed at enhancing the visual quality of images by removing unwanted noise. image quality can be compromised by different noise sources, including Gaussian, salt-and-pepper, and speckle noise. This paper proposes a hybrid image denoising approach that integrates convolutional neural networks (CNNs) and wavelet transform. The proposed approach first applies a CNN to the noisy image to remove high-level noise, such as textures and patterns. After denoising, the image undergoes decomposition through the wavelet transform, breaking it into various frequency sub-bands. The wavelet coefficients in the high-frequency subbands are then thresholded to remove low-level noise, such as Gaussian noise. Finally, the image is reconstructed using the inverse wavelet transform. The proposed approach has several advantages over traditional image denoising methods. It is able to remove both high-level and low-level noise effectively. It is more robust to different types of noise and able to preserve fine image details. © 2024 IEEE.

关键词： image reconstruction

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Design of Automatic Production Algorithm for Ceramic Patterns Based on machine learning

Design of Automatic Production Algorithm for Ceramic Pattern...

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2024 International conference on Electrical Drives, Power Electronics and Engineering, EDPEE 2024

作者： He, Yueming Xu, Xinyue Fuzhou University Xiamen Academy Of Arts And Design Fujian Xiamen361021 China

ISBN: (纸本)9798350395631

With the growth of high technology, the digitization of art and cultural relics is no longer a problem. At present, ceramic enterprises are gradually evolving from large-scale customization to personalized customization, and the demand for upgrading the intelligent manufacturing industry is increasing. A personalized ceramic pattern generation algorithm is on the verge of emergence. With the theoretical growth of neural networks and the improvement of hardware computing power, machine learning (ML) and deep learning (DL) have achieved remarkable achievements in multiple fields. image feature extraction technology and automatic generation technology have both received great attention from the academic community. Generative Adversarial networks (GANs) have promoted research applications in image generation and restoration, text generation, video prediction, and style transfer, demonstrating their enormous potential in image processing. This article designs an ML based algorithm for automated production of ceramic patterns. By using ML technology, useful features are automatically extracted from a large amount of ceramic pattern data, and these features are used to train the model, thereby generating new ceramic patterns. The experimental results show that the algorithm can be well applied to practical cases and effectively achieve automated production of ceramic patterns. © 2024 IEEE.

关键词： Deep learning

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Optimized 1D Convolutional neural Network for Efficient Intrusion Detection in IoT networks 8

Optimized 1D Convolutional Neural Network for Efficient Intr...

引用

IEEE 8th International conference on Signal and image processing applications (IEEE ICSIPA)

作者： Umair, Muhammad Tan, Wooi-Haw Foo, Yee-Loo Multimedia Univ Fac Engn Cyberjaya Malaysia

ISBN: (纸本)9798350352368

In this study, we present an optimized convolutional neural network model for classification of cyber-attacks from network traffic data, using the N-BaIoT dataset. The proposed model consists of convolutional, max pooling and dense layers. Different architectural models have been experimented with different settings such as utilizing 3,5,7,9 and 11 convolutional layers. Among these settings, the model with 5 convolutional layers outperformed the others with test accuracy of 99.91% and an average recall, precision and F1 score of 0.99. Along with this, the proposed model has been evaluated by class wise classification report and confusion matrices. Furthermore, the model performance complexity has also been discussed and measured in GFLOPS (Giga Floating Point Operations Per Second), it shows that the proposed model with best and optimized settings achieved a value of 3.158.

关键词： Deep learning N-BaIoT Intrusion detection 1D CNN

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Crop Disease Detection using CNN Architecture VGG16 1

Crop Disease Detection using CNN Architecture VGG16

引用

1st IEEE International conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Raj, Sandeep Kaur, Surinder Singh, Neetu Gupta, Monica Dronacharya College of Engineering Department of Artificial Intelligence and Machine Learning Haryana Farrukh Nagar India Bharati Vidyapeeth's College of Engineering Department of Information Technology New Delhi India Bharati Vidyapeeth's College of Engineering Department of Electronics and Communication Engineering New Delhi India

ISBN: (纸本)9798350356816

The Agriculture plays a major part in any nation for economy through the production of various crops and it's an essential source of income in India. The plant disease is one of the leading task of agriculture. When the infections occur in crops result in reduced quantity and quality of production of crops which will result in economic loss. Therefore, plague needs to be identified as quickly as possible for the healthy production of crops and plants. Various methods and solutions were proposed for crop disease detection by the researchers using image processing, machine learning, and deep learning. The deep learning plays an essential part in the field of image processing using convolution neural networks and their architecture. In this experiment, we have examined the CNN, Transfer learning VGG16 model on the plant-village dataset which was available on the Kaggle. The dataset(records) includes images of various potato, tomato and pepper crops of 15 different classes. The detection was done using the leaf samples. Our goal is to detect the disease in plants with accurate without taking so much time so that people can take care of their plants and apply the appropriate treatment. In future this VGG16 can be used to identify specific objects, animals, plants, and more in a picture. to identify, recognition and classification of image, that will use in many applications. Medical, Banking, Transportations well as Agriculture. © 2024 IEEE.

关键词： Convolutional neural networks

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Application of image sharpening based quality assessment model - Extraction of Traditional Chinese Embroidery Patterns as an Example 5

Application of image sharpening based quality assessment mod...

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5th International conference on Computer Engineering and Application (ICCEA)

作者： Jiang, Yueyao Ming, Yue Hubei Univ Technol Coll Art & Design Wuhan Peoples R China

ISBN: (纸本)9798350386783;9798350386776

Today's computer software has made many impressive advances in the field of digital image processing of tattoos. The digital image processing of traditional patterns can not only help design researchers better understand and analyze the structure and evolution of traditional patterns, but also provide new ideas and methods for the application and innovation of patterns. Through computer software, we can perform accurate edge detection, texture extraction, color analysis and morphological reconstruction of traditional patterns, making the characteristics and beauty of traditional patterns better displayed and conveyed. However, traditional image quality assessment methods often fail to adequately reflect the complexity of visual perception;therefore, this study proposes a deep learning-based assessment model to more accurately assess the effect of image sharpening. By building a dataset covering a wide range of image types, including traditional Unsharp Masking and high-pass filter sharpening as well as deep learning-based methods, we simulate real-world application scenarios. Using a Convolutional neural Network (CNN) structure and incorporating an attention mechanism, the model focuses on the detail changes in the sharpened region of the image. The experimental results demonstrate the superior performance of our model by comparing traditional PSNR, SSIM and deep learning evaluation methods. Our study provides an effective tool for the field of image processing and shows the direction for future research on image quality assessment.

关键词： convolutional neural networks deep learning image sharpening quality traditional embroidery patterns

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Modular Sumcheck Proofs with applications to machine learning and image processing 23

Modular Sumcheck Proofs with Applications to Machine Learnin...

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30th ACM SIGSAC conference on Computer and Communications Security (ACM CCS)

作者： Balbas, David Fiore, Dario Gonzalez Vasco, Maria Isabel Robissout, Damien Soriente, Claudio IMDEA Software Inst Madrid Spain Univ Politecn Madrid Madrid Spain Univ Carlos III Madrid Madrid Spain NEC Labs Europe Madrid Spain

ISBN: (纸本)9798400700507

Cryptographic proof systems provide integrity, fairness, and privacy in applications that outsource data processing tasks. However, general-purpose proof systems do not scale well to large inputs. At the same time, ad-hoc solutions for concrete applications-e.g., machine learning or image processing-are more efficient but lack modularity, hence they are hard to extend or to compose with other tools of a data-processing pipeline. In this paper, we combine the performance of tailored solutions with the versatility of general-purpose proof systems. We do so by introducing a modular framework for verifiable computation of sequential operations. The main tool of our framework is a new information-theoretic primitive called Verifiable Evaluation Scheme on Fingerprinted Data (VE) that captures the properties of diverse sumcheck-based interactive proofs, including the well-established GKR protocol. Thus, we show how to compose VEs for specific functions to obtain verifiability of a data-processing pipeline. We propose a novel VE for convolution operations that can handle multiple input-output channels and batching, and we use it in our framework to build proofs for (convolutional) neural networks and image processing. We realize a prototype implementation of our proof systems, and show that we achieve up to 5x faster proving time and 10x shorter proofs compared to the state-of-the-art, in addition to asymptotic improvements.

关键词： Proof Systems Verifiable Computation Zero-Knowledge Proofs machine learning Convolutional neural networks image processing

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