An artificial intelligence-based weed detection system is a computerized system designed to automatically identify and classify different types of weeds in agricultural fields. The system utilizes advanced computer vi...
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ISBN:
(纸本)9789819720521;9789819720538
An artificial intelligence-based weed detection system is a computerized system designed to automatically identify and classify different types of weeds in agricultural fields. The system utilizes advanced computer vision techniques and machine learning algorithms to accurately detect and differentiate weeds from crops or other elements in the field. The weed detection system typically consists of hardware components such as cameras or drones which capture high-resolution images or videos of the agricultural area. These images are then analyzed by the artificial intelligence algorithms which have been trained on large datasets of weed images to recognize and distinguish various weed species. This paper examines the pivotal role of AI in weed detection, a critical aspect of farming that determines crop yield and health. Through a comprehensive review, we shed light on the diverse AI-driven techniques including image recognition using Deep Learning, real-time automation, data augmentation, multispectral imaging, and predictive analysis, among others. The ability of AI to distinguish between crops and weeds, often in real-time and under varied environmental conditions, underscores its transformative potential. As weed management represents a significant challenge in agriculture, the precise and proactive capabilities offered by AI can lead to optimized herbicide usage, reduced costs, and enhanced crop productivity.
Wavelet theory has been widely applied in imageprocessing, and machine learning techniques have permeated various fields, significant improvements in image denoising remain possible. This paper introduces a novel ima...
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The accurate identification of large and medium slag in power plant slag transport system has an important impact on the safety, efficiency, environmental protection and economic benefits of power plant. The timely pr...
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The development of conversational artificial intelligence (AI) is examined in this research paper, with a focus on how speech and image recognition technologies can be combined to transform and interact with systems. ...
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The most prevalent and dangerous kind of cancer in humans is skin cancer. Melanoma is a kind of skin cancer that is fatal. One of the deadliest cancers in the world, melanoma will spread to other body parts if it is n...
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ISBN:
(纸本)9798350375480;9798350375497
The most prevalent and dangerous kind of cancer in humans is skin cancer. Melanoma is a kind of skin cancer that is fatal. One of the deadliest cancers in the world, melanoma will spread to other body parts if it is not detected at an early stage. It is easily curable if detected in its early stages. The goal's highlights the issues of a global increase in skin cancer cases, excessive medical expenses, and an exponential rise in the effect of death from delayed diagnosis due to missed opportunities for early intervention. The biopsy procedure is the official way for melanoma diagnosis and detection. This approach can be extremely painful and time-consuming. In the modern years, deep learning techniques have shown promise in automating the detection process, offering the potential for more accurate and timely diagnoses. This system is a deep learning-based predictive algorithm that uses dermoscopic image analysis to dynamically forecast melanoma skin cancer. The main objectives of this research are to find skin cancer more accurately and quickly in its early stages. This work provides a computer-aided detection technique for melanoma early detection. Our research work suggests using InceptionV3 and Resnet50 algorithms, along with imageprocessing techniques, to create an effective diagnosis system in this study. An image of the impacted skin is captured and subjected to a few pre-processing methods to produce an improved and smoothed image. Next, the picture is subjected to morphological, thresholding, and grey scaling techniques during the data augmentation phase. It categorizes the provided image as either Stage 1 or Stage 2 melanoma. An impressive 84.5% accuracy is attained. Clinical diagnosis of many disorders is increasingly dependent on harmless clinical computer vision or medical imageprocessing. These methods offer an automatic evaluation of images tool for a quick and precise assessment. Overall, the suggested technique is a major improvement in the identif
The proceedings contain 118 papers. The topics discussed include: comparative analysis of neural network-based routing algorithms for wireless sensor networks;leveraging ShuffleNet and LLaVA-Phi for state-of-the-art i...
ISBN:
(纸本)9798350385199
The proceedings contain 118 papers. The topics discussed include: comparative analysis of neural network-based routing algorithms for wireless sensor networks;leveraging ShuffleNet and LLaVA-Phi for state-of-the-art image deblurring and description for mobile devices;spotting deep fakes: exploring detection techniques for image recognition;a transfer learning approach for enhancing road surface classification in intelligent driving systems;advanced U-Net++ architecture for precise brain tumor segmentation in MRI images: a robust solution for medical image analysis;a modeling and control of load frequency for interconnected thermal/hydro/nuclear power system using pi and fuzzy controller;systematic exploration of GAN-generated image recognition techniques;controlling mouse cursor through eye movement;improvement of power quality in distribution network at low voltage side by using DVR;facial emotion recognition using image filtering techniques;and enhancing financial trading strategies: a comprehensive sentiment analysis pipeline.
Lung cancer being one of the catastrophic diseases is haunting mankind from past seven decades. Unfortunately, early detection of lung cancer is unlikely, hence leading to highest mortality rates. However, various ima...
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Leaf Area Index (LAI) holds significant importance as a specific characteristic of Leaf Areas in the field of smart agriculture. This study explores the estimation of LAI using a multi-spectral image from WorldView 3 ...
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ISBN:
(纸本)9798350386202;9798350386196
Leaf Area Index (LAI) holds significant importance as a specific characteristic of Leaf Areas in the field of smart agriculture. This study explores the estimation of LAI using a multi-spectral image from WorldView 3 satellite. The image combines 8 VNIR bands and has a spatial resolution of 1.24m. To overcome the limited amount of available data, the image was split into smaller subsets called paxels, resulting in 500 paxels for training and testing. For enhancing machine learning models. performance, the standardisation of a dataset is made, after that, a Multilayer Perceptron with a specific architecture aimed to predict LAI from the multiple bands is trained. The achieved results showed promising performance in LAI prediction. Overall, the study demonstrates the potential of using satellite imagery and machine learning algorithms to improve our understanding of crop health and productivity.
This article, a morphological imageprocessing algorithm is developed for early detection of viral hepatitis from microscopic blood images. It is mentioned that it can be used to accurately determine each boundary det...
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The proceedings contain 46 papers. The topics discussed include: continuous and non-contact extraction of respiratory signal and rate from thermal video;phonocardiogram signal denoising using dictionary learning;prese...
ISBN:
(纸本)9798331532543
The proceedings contain 46 papers. The topics discussed include: continuous and non-contact extraction of respiratory signal and rate from thermal video;phonocardiogram signal denoising using dictionary learning;presenting a method for gluing error detection using imageprocessing;alleviating undesired distance effect in spatio-temporal based video anomaly detection;real-time audio analysis for detection of apnea intervals in health-care system;classification of electric motors faults using Fourier-based features and self-organizing maps;validation of CMA simulation in analyzing the impact of electromagnetic waves on drone electronic boards;a hybrid approach for sentiment analysis of Arabic tweets;and line segmentation in Persian texts in double columns using hierarchical clustering algorithms.
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