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Crop protection and disease detection using artificial intelligence and computer vision: a comprehensive review

Multimedia Tools and Applications 2025年第7期84卷 3723-3743页

作者： Shah, Kanish Sushra, Rajat Shah, Manan Shah, Dhairya Shah, Haard Raval, Megh Prajapati, Mitul Software Engineer Walmart SunnyvaleCA United States Atmiya Vidya Mandir Gujarat Surat India Department of Chemical Engineering School of Energy Technology Pandit Deendayal Energy University Gujarat Gandhinagar India Robotics ECE Concentration Northeastern University BostonMA United States Department of Computer Science Concentration in AI Hofstra University HempsteadNY United States Department of Chemical Engineering S. S Agrawal Institute of Engineering & Technology Gujarat Navsari India R &D Engineer Span Inspection Systems Pvt. Ltd Gujarat Gandhinagar India

The technological advancements in the field of agriculture have increased to a great extent in recent years, and many techniques have evolved from other techniques. Some methods are improved or upgraded from the previous versions by implementing a new model or using better hardware devices. This has been helpful for the farmers in increasing crop productivity, and the life expectancy of crops has also increased as the diseases inside or outside the crops can be detected much earlier, and learning at an early stage helps prevent other crops. In this paper, we have presented a study where many varieties of fruits and vegetables have been taken to determine which method was used for a particular crop. By analyzing the various works carried out by the authors, it was inferred that most of the works revolved around image processing and hyperspectral imaging. Due to this, we had also included most of the papers, particularly as the models and hardware components used were much better than other works. Then, a comparative study was done where different fruits and vegetables highlighted the two main areas: the method used and the accuracy obtained. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Crops

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Optimizing Existing SHM systems: Retasking as a Self-Healing Solution for Improved Fault Tolerance

Optimizing Existing SHM Systems: Retasking as a Self-Healing...

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Aaron Mano Cherian D. Ajitha Avanish Gouraha Dipanshu Mandal School of Computer Science and Engineering (SCOPE) Vellore Institute of Technology Vellore Tamilnadu India Department of Software Systems School of Computer Science and Engineering (SCOPE) Vellore Institute of Technology Vellore Tamilnadu India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

This paper focuses on self-healing algorithms in structural health monitoring (SHM) systems centered around the enhancement of resilience and adaptability of the systems. In this study, imports from existing methods (clustering, Fault Tolerant Multiple Redundancy (FTMR) and reinforcement learning) are analyzed against the choice of creating a novel retasking algorithm designed for dynamic resource redistribution and optimal monitoring coverage. Unlike conventional methods, retasking will allow adapting the coverage in real time, whereby system down time will be reduced, with less computational load achieved through task redistribution through functional sensors. Findings showed that retasking improved reliability and scalability of the SHM systems drastically, providing a simple yet powerful resolution towards modern infrastructure monitoring. This study stresses the retasking capability to redefine self-healing in the SHM systems for future directions in infrastructure safety.

关键词： Training Heuristic algorithms Fault tolerant systems Redundancy Clustering algorithms Reinforcement learning Solids Real-time systems Monitoring Stress

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Harnessing the Power of AI with Deep Learning: Innovations and Implications

Harnessing the Power of AI with Deep Learning: Innovations a...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Upender, Talluri Sathiyamoorthy, C. Udayakumar, K. Kirubakaran, D. Purushothaman, V. Raja, Kiruba Thangam CMR College of Engineering and Technology Department of CSE Medchal Telangana Hyderabad India Department of Commerce Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of CSE Tamil Nadu Chennai India R.M.K. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Chennai Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India VIT University Department of Software and Systems Engineering Tamil Nadu Vellore India

ISBN: (纸本)9798331512965

The rapid advancements in artificial intelligence (AI) and deep learning have revolutionized various sectors, enabling unprecedented levels of innovation and efficiency. This paper delves into the transformative impact of deep learning across multiple domains, highlighting key innovations and their broader implications. By examining cutting-edge applications in healthcare, finance, autonomous systems, and natural language processing, the study elucidates how deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are pushing the boundaries of what's possible. Moreover, the paper explores the integration of deep learning with emerging technologies such as the Internet of Things (IoT), edge computing, and quantum computing, underscoring the synergistic potential of these combinations. The role of deep learning in predictive analytics and personalized solutions is discussed, showcasing its ability to enhance decision-making processes and improve user experiences. Furthermore, the paper discusses the ethical considerations, challenges, and future prospects associated with the widespread adoption of deep learning technologies. The insights provided aim to foster a deeper understanding of how harnessing AI with deep learning can drive progress while addressing critical societal and technological issues. © 2025 IEEE.

关键词： Convolutional neural networks

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VisioSLR: A Vision Data-Driven Framework for Sign language Video Recognition and Performance Evaluation on Fine-Tuned YOLO Models

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Procedia computer science 2025年 257卷 85-92页

作者： Leila Ismail Nada Shahin Henok Tesfaye Alain Hennebelle Intelligent Distributed Computing and Systems (INDUCE) Lab Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates Independent Researcher

With the emergence of AI for good, there has been an increasing interest in building computer vision data-driven deep learning inclusive AI solutions. Sign language Recognition (SLR) has gained attention recently. It is an essential component of a sign-to-text translation system to support the deaf and hard-of-hearing population. This paper presents a computer VISIOn data-driven deep learning framework for Sign language video Recognition (VisoSLR). VisioSLR provides a precise measurement of translating signs for developing an end-to-end computational translation system. Considering the scarcity of sign language datasets, which hinders the development of an accurate recognition model, we evaluate the performance of our framework by fine-tuning the very well-known YOLO models, which are built from a signs-unrelated collection of images and videos, using a small-sized sign language dataset. Gathering a sign language dataset for signs training would involve an enormous amount of time to collect and annotate videos in different environmental setups and multiple signers, in addition to the training time of a model. Numerical evaluations of VisioSLR show that our framework recognizes signs with a mean average precision of 97.4%, 97.1%, and 95.5% and 11, 12, and 12 milliseconds of recognition time on YOLOv8m, YOLOv9m, and YOLOv11m, respectively.

关键词： Artificial intelligence computer vision Deep learning Fine-tuning Machine translation Neural machine translation Sign language Sign language recognition Transfer learning YOLO

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Variational quantum classifier-based early identification and classification of chronic kidney disease using sparse autoencoder and LASSO shrinkage

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PeerJ computer science 2025年 11卷

作者： Parthasarathi, P. Alshahrani, Haya Mesfer Venkatachalam, K. Cho, Jaehyuk Department of Computer Science and Engineering Bannari Amman Institute of Technology Erode India Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Saudi Arabia Department of Software Engineering Jeonbuk National University Jeonju Korea Republic of Department of Software Engineering Division of Electronics Information Engineering Jeonbuk National University Jeonju Korea Republic of

The two leading causes of chronic kidney disease (CKD) are excessive blood pressure and diabetes. Researchers worldwide utilize the rate of globular filtration and kidney inflammation biomarkers to identify chronic kidney disease that gradually reduces renal function. The mortality rate for CKD is high, and thus, a person with this illness is more likely to pass away at a younger age. Healthcare professionals must diagnose the various illnesses connected to this deadly disease as promptly as possible to lighten the impact of CKD. A quantum machine learning (QML) based technique is presented in this research to help with the early diagnosis and prognosis of CKD. The proposed research comprises four phases: data pre-processing, data augmentation, feature selection, and classification. In the first phase, Kalman filter and data normalization techniques are applied to handle the missing and noisy data. In the second phase, data augmentation uses sparse autoencoders to balance the data for smaller classes. In the third phase, LASSO shrinkage is used to select the significant features in the dataset. Variational Quantum classifiers, a supervised QML technique, are employed in the classification phase to classify chronic kidney diseases. The proposed system has been evaluated on the UCI dataset, which comprises 400 CKD patients in the early stages with 25 attributes. The suggested system was assessed using F1-score, precision, recall, and accuracy as evaluation metrics. With a 99.2% classification accuracy, it was found that this model performed better than the other traditional classifiers used for chronic kidney disease classification. Copyright 2025 Parthasarathi et al. Distributed under Creative Commons CC-BY 4.0

关键词： Blood pressure

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A Comparison of Deep Learning and Machine Learning Methods for Water Quality Prediction

A Comparison of Deep Learning and Machine Learning Methods f...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： N. Srinivasa Gupta B. Valarmathi Ishaan Hiwanj Department of Manufacturing Engineering School of Mechanical Engineering Vellore Institute of Technology Vellore Tamil Nadu India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore Tamil Nadu India Department of Information Technology School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore Tamil Nadu India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Predicting water quality is essential to preserving human health and environmental sustain ability. Traditional water quality assessment methods often face scalability and real-time monitoring limitations. With accuracies of 62%, 72 %, 83 %, 69%, 63 %, 66%, 71 %, 63 %, and 64%, respectively, the current techniques utilized were Logistic Regression, Decision Trees, Random Forest Regressor, Extreme Gradient Boosting, Naive Bayes, K-nearest neighbors, Support Vector Machine, AdaBoost, and Bagging [9]. This study addresses these challenges by leveraging Adaptive Synthetic Sampling (ADASYN) to balance the dataset and evaluating model performance on datasets of 5,000 and 10,000 entries per class. A robust dataset obtained from Kaggle was used, with five models - Long Short-Term Memory (LSTM), Feed Forward Neural Network (FFNN), Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), and Random Forest - evaluated and compared. The proposed methods demonstrate significant improvements in accuracy, with XGBoost achieving the highest accuracy of 95.53%, followed by Random Forest at 93.98%. This work underscores the importance of advanced machine learning techniques in addressing the limitations of traditional methods, enhancing accuracy, scalability, and adaptability in water quality prediction. These findings contribute to advancing environmental monitoring and management practices with reliable, data-driven insights.

关键词： Support vector machines Adaptation models Accuracy Scalability Water quality Boosting Reliability Random forests Long short term memory Regression tree analysis

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A novel facial expression recognition framework using deep learning based dynamic cross-domain dual attention network

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PeerJ computer science 2025年 11卷 e2866-e2866页

作者： Alzahrani, Ahmed Omar Alghamdi, Ahmed Mohammed Ashraf, M. Usman Ilyas, Iqra Sarwar, Nadeem Alzahrani, Abdulrahman Alarood, Alaa Abdul Salam Department of Information Systems and Technology College of Computer Science and Engineering University of Jeddah Makkah Jeddah Saudi Arabia Department of Software Engineering College of Computer Science and Engineering University of Jeddah Makkah Jeddah Saudi Arabia Department of Computer Science Government College Women University Sialkot Punjab Sialkot Pakistan Department of Computer Science Bahria University Punjab Lahore Pakistan

Variations in domain targets have recently posed significant challenges for facial expression recognition tasks, primarily due to domain shifts. Current methods focus largely on global feature adoption to achieve domain-invariant learning;however, transferring local features across diverse domains remains an ongoing challenge. Additionally, during training on target datasets, these methods often suffer from reduced feature representation in the target domain due to insufficient discriminative supervision. To tackle these challenges, we propose a dynamic cross-domain dual attention network for facial expression recognition. Our model is specifically designed to learn domain-invariant features through separate modules for global and local adversarial learning. We also introduce a semantic-aware module to generate pseudo-labels, which computes semantic labels from both global and local features. We assess our model’s effectiveness through extensive experiments on the Real-world Affective Faces Database (RAF-DB), FER-PLUS, AffectNet, Expression in the Wild (ExpW), SFEW 2.0, and Japanese Female Facial Expression (JAFFE) datasets. The results demonstrate that our scheme outperforms the existing state-of-the-art methods by attaining recognition accuracies 93.18, 92.35, 82.13, 78.37, 72.47, 70.68 respectively. Copyright 2025 Alzahrani et al. Distributed under Creative Commons CC-BY 4.0

关键词： Deep learning

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Exploratory Data Analysis and Predicting Indian Crime Against Women Using Machine Learning Approaches

Exploratory Data Analysis and Predicting Indian Crime Agains...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： N. Srinivasa Gupta B. Valarmathi K. Santhi T.Chellatamilan Sameeksha Nanda Department of Manufacturing School of Mechanical Engineering Vellore Institute of Technology Vellore Tamil Nadu India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore Tamil Nadu India Department of Analytics School of Computer Science and Engineering Vellore Institute of Technology Vellore Tamil Nadu India Department of Information Technology School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore Tamil Nadu India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Crime forecasting is a critical endeavor aimed at preventing future criminal actions, specifically the rising rate of violence against women in India. This study proposes the model to predict crimes committed against women using data from the National Crime Records Bureau's (NCRB) monthly crime report. The dataset encompasses district and state-wise crime activities from 2001 to 2014. The proposed work objectives are identifying factors influencing these crimes and developing predictive models using machine learning (ML). The study employs Exploratory Data Analysis (EDA), to enhance the predictive power of the models. The suggested study uses XGBoost, Random Forest (RF), and Neural Network (NN). The neural network algorithm outperforms the Random Forest and XGBoost techniques among these three. The Random Forest algorithm has the worst performance. After comparing the accuracies of various methods, such as XGBoost (92%), and Random Forest (89.84%) classifiers using imbalanced datasets (without employing Synthetic Minority Oversampling Technique (SMOTE) technique), it was found that the neural network algorithm (95.81%) delivers the highest accuracy. This study looks at model accuracies and finds that, after applying the SMOTE algorithm, neural network algorithms performed best, with an accuracy of 96.76%, outperforming the accuracies of the remaining models, which included the Random Forest (93.08%) and XG-Boost (95%).

关键词： Radio frequency Technological innovation Accuracy Machine learning algorithms Data analysis Predictive models Prediction algorithms Data models Classification algorithms Random forests

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Refractive Index Biosensor–Based Detection of Mycobacterium Tuberculosis Using Sea Lion Political Optimizer and Deep Learning

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Plasmonics 2025年 1-19页

作者： Ragavan, V. K. Senthil Nithya, N.S. Vengala, Anantharamaiah Subramanian, Balambigai Ambhika, C. Sri Shanmugha College of Engineering and Technology Tamil Nadu Sankari Salem (D.T.) 637 304 India Department of Software Systems School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of C S E K L E F Andhra Pradesh Vaddeswaram Guntur 522502 India Dept of ECE Karpagam College of Engineering Tamil Nadu Coimbatore India Department of AIML R.M.D Engineering College RSM Nagar Tamil Nadu Kavarapetai India

Mycobacterium tuberculosis (MTB) detection is a major global health concern, requiring accurate and reliable diagnostic tools. According to the World Health Organization (WHO), the global burden of TB is staggering, with approximately 10 million new cases reported annually, leading to a devastating 1.5 million fatalities worldwide every year. The timely detection and treatment of TB are critical components in the fight against this disease, as they play a vital role in preventing its transmission, minimizing the risk of complications and deaths, and enhancing the overall quality of life for affected individuals. Hence, this study presents a novel Sea Lion Political Optimizer_Deep Maxout Network (SLnPO_DMN) for detecting MTB, leveraging a robust processing pipeline to achieve exceptional accuracy. The proposed pipeline involves a series of meticulous steps, including data pre-processing by Min–Max normalization to scale the data to a common range. To enhance model generalizability, data augmentation is carried out by Bootstrap, generating additional training samples by resampling the original data with replacement. The augmented data is then fed into a DMN, optimized by the SLnPO, a hybrid algorithm combining the strengths of sea lion optimization (SLnO) and political optimizer (PO). The SLnPO_DMN model is trained to identify and learn intricate patterns within the data, ultimately attaining optimal performance and demonstrating exceptional capability in detecting Mycobacterium tuberculosis with high accuracy and reliability. The SLnPO_DMN is evaluated using a comprehensive set of metrics, including accuracy, sensitivity, and specificity. The results demonstrate the effectiveness of the SLnPO_DMN model achieving an accuracy (99.12%), sensitivity of (98.74%), and specificity of (98%). © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Deep maxout network Min–Max normalization Mycobacterium tuberculosis Political optimizer Sea lion optimization

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Real-Time Freeway Traffic Anomalous Event Detection System via Radar Detector Sensors 25

Real-Time Freeway Traffic Anomalous Event Detection System v...

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Proceedings of the ACM/IEEE 16th International Conference on Cyber-Physical systems (with CPS-IoT Week 2025)

作者： Austin Coursey Junyi Ji Zhiyao Zhang William Barbour Marcos Quinones-Grueiro Tyler Derr Gautam Biswas Daniel B. Work Institute for Software Integrated Systems Vanderbilt University Nashville TN USA and Department of Computer Science Vanderbilt University Nashville TN USA Institute for Software Integrated Systems Vanderbilt University Nashville TN USA and Department of Civil and Environmental Engineering Vanderbilt University Nashville TN USA Institute for Software Integrated Systems Vanderbilt University Nashville TN USA Department of Computer Science Vanderbilt University Nashville TN USA Institute for Software Integrated Systems Vanderbilt University Nashville TN USA Department of Computer Science Vanderbilt University Nashville TN USA and Department of Civil and Environmental Engineering Vanderbilt University Nashville TN USA

ISBN: (纸本)9798400714986

Undetected anomalous incidents on the freeway, such as crashes, can cause increased congestion and secondary crashes. To address this problem, we recently released a real freeway traffic anomalous event detection dataset. However, our evaluation was limited to a static week of traffic data. This demo presents a live anomaly detection dashboard, processing sensor data with a 1-minute delay. It has interactive components to adjust the anomaly detection performance, providing a realistic evaluation of graph anomaly detection methods for long-term analysis and method improvements.

关键词： anomaly detection

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