Revolutionizing the circular economy through new technologies: A new era of sustainable progress

Environmental Technology & Innovation, Год журнала: 2023, Номер 33, С. 103509 - 103509

Опубликована: Дек. 29, 2023

Nowadays the pace of production and consumption is reaching environmentally unsustainable levels. In this regard, great technological advances developed in recent years are postulated as a source opportunities to boost circular economy sustainable development. This wide range possibilities offered by new technologies create more reality has aroused curiosity interest academic world, especially years. The main objective research reveal challenges that arise when incorporating objectives economy. Regarding methodology, study been partially supported using bibliometric techniques. results highlight transformative role technologies, blockchain artificial intelligence, advancing economy, with particular emphasis on community technology integration, ethical considerations, synergies, business models, burgeoning bioeconomy. We conclude promise enhanced resource efficiency, optimized supply chains, innovative improved product lifecycle management, offering profound economic environmental benefits while fostering collaborative innovation. However, these also represent address, such integrating advanced methods, ensuring chain transparency, overcoming skill gap, avoiding data centralization, adapting regulatory frameworks foster equitable growth. These some most important areas for further research, those related development employees' capabilities adaptation frameworks, they understudied gaps.

Язык: Английский

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Adoption of Unmanned Aerial Vehicle (UAV) imagery in agricultural management: A systematic literature review

Ecological Informatics, Год журнала: 2023, Номер 78, С. 102305 - 102305

Опубликована: Сен. 14, 2023

Язык: Английский

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Agriculture 4.0 and beyond: Evaluating cyber threat intelligence sources and techniques in smart farming ecosystems

Computers & Security, Год журнала: 2024, Номер 140, С. 103754 - 103754

Опубликована: Фев. 12, 2024

The digitisation of agriculture, integral to Agriculture 4.0, has brought significant benefits while simultaneously escalating cybersecurity risks. With the rapid adoption smart farming technologies and infrastructure, agricultural sector become an attractive target for cyberattacks. This paper presents a systematic literature review that assesses applicability existing cyber threat intelligence (CTI) techniques within infrastructures (SFIs). We develop comprehensive taxonomy CTI sources, specifically tailored SFI context, addressing unique challenges in this domain. A crucial finding our is identified need virtual Chief Information Security Officer (vCISO) agriculture. While concept vCISO not yet established sector, study highlights its potential significance. implementation could play pivotal role enhancing measures by offering strategic guidance, developing robust security protocols, facilitating real-time analysis response strategies. approach critical safeguarding food supply chain against evolving landscape threats. Our research underscores importance integrating framework into practices as vital step towards strengthening cybersecurity. essential protecting agriculture era digital transformation, ensuring resilience sustainability emerging

Язык: Английский

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Rapid estimation of soil Mn content by machine learning and soil spectra in large-scale

Ecological Informatics, Год журнала: 2024, Номер 81, С. 102615 - 102615

Опубликована: Апрель 28, 2024

Язык: Английский

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A hybrid time series and physics-informed machine learning framework to predict soil water content

Engineering Applications of Artificial Intelligence, Год журнала: 2025, Номер 144, С. 110105 - 110105

Опубликована: Янв. 25, 2025

Язык: Английский

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A comprehensive review of artificial intelligence and machine learning applications in energy consumption and production

Journal of Technology Innovations and Energy, Год журнала: 2023, Номер 2(4), С. 1 - 26

Опубликована: Окт. 19, 2023

The energy industry worldwide is today confronted with several challenges, including heightened levels of consumption and inefficiency, volatile patterns in demand supply, a dearth crucial data necessary for effective management. Developing countries face significant challenges due to the widespread occurrence unauthorized connections electricity grid, resulting substantial amounts unmeasured unpaid consumption. Nevertheless, implementation artificial intelligence (AI) machine learning (ML) technologies has potential improve management, efficiency, sustainability. Therefore, this study aims evaluate influence AI ML on progress industry. present employed systematic literature review methodology examine arising from frequent power outages limited accessibility various developing nations. results indicate that possess domains, predictive maintenance turbines, optimization consumption, management grids, prediction prices, assessment efficiency residential buildings. This concluded discussion measures enable nations harness advantages sector.

Язык: Английский

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Innovative deep learning approach for cross-crop plant disease detection: A generalized method for identifying unhealthy leaves

Information Processing in Agriculture, Год журнала: 2024, Номер unknown

Опубликована: Март 1, 2024

One of the most serious threats to global food security is plant diseases compromising agricultural productivity and threatening livelihoods millions. These can decimate crops, disrupt supply chains, escalate risk shortages, underscoring urgency implementing robust strategies safeguard world's sources. Deep learning methods have revolutionized field disease detection, offering advanced accurate solutions for early identification management. However, a recurring problem in deep models their susceptibility lack robustness generalization when facing novel crop types that were not included training dataset. In this paper, we address issue by proposing learning-based system capable recognizing diseased healthy leaves across different even if was trained on them. The key idea focus small leaf regions rather than overall appearance leaf, along with determining disease's prevalence rate entire leaf. For efficient classification leverage excellence Inception model recognition, employ architecture, which suitable processing without performance. To confirm effectiveness our method, tested it using widely acclaimed PlantVillage dataset, recognized as utilized dataset its comprehensive diverse coverage. Our method achieved an accuracy 94.04%. Furthermore, new datasets, 97.13%. This innovative approach only enhances detection but also addresses critical challenge crops diseases. addition, outperformed existing ability identify any type, showcasing potential broad applicability contribution initiatives.

Язык: Английский

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A Novel Deep Learning Architecture for Agriculture Land Cover and Land Use Classification from Remote Sensing Images Based on Network-Level Fusion of Self-Attention Architecture

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Год журнала: 2024, Номер 17, С. 6338 - 6353

Опубликована: Янв. 1, 2024

AI-driven precision agriculture applications can benefit from the large data source that remote sensing provides, as it gather agricultural monitoring at various scales throughout year. Numerous advantages for sustainable applications, including yield prediction, crop monitoring, and climate change adaptation, be obtained artificial intelligence. In this work, we proposed a fully automated Optimized Self-Attention Fused Convolutional Neural Network (CNN) architecture land use cover classification using (RS) data. A new contrast enhancement equation has been utilized in augmentation. After that, fused CNN was proposed. The initially consists of two custom models named IBNR-65 Densenet-64. Both have designed based on inverted bottleneck residual mechanism Dense Blocks. both were depth-wise concatenation append layer deep features extraction. trained model performed neural network (NN) classifiers. results NN classifiers are insufficient; therefore, implemented Bayesian Optimization fine-tuned hyperparameters NN. addition, Quantum Hippopotamus Algorithm best feature selection. selected finally classified improved accuracy 98.20, 89.50, 91.70%, highest rate is 98.23, recall f1-score 98.21 respectively, SIRI-WHU, EuroSAT, NWPU datasets. Moreover, detailed ablation study conducted, performance compared with SOTA. shows accuracy, sensitivity, computational time performance.

Язык: Английский

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Classification of inland lake water quality levels based on Sentinel-2 images using convolutional neural networks and spatiotemporal variation and driving factors of algal bloom

Ecological Informatics, Год журнала: 2024, Номер 80, С. 102549 - 102549

Опубликована: Фев. 29, 2024

Water quality monitoring in inland lakes is crucial to ensuring the health and stability of aquatic ecosystems. For regional water environment agencies researchers, remote sensing offers a cost-effective alternative traditional in-situ sampling methods. In this study, we designed convolutional neural network (CNN) based on AlexNet represent relationship between Sentinel-2 images situ levels Lake Dianchi from November 2020 April 2023. The model incorporated an algal bloom extraction algorithm utilized correlation analysis, redundancy analysis (RDA), random forest (RF) method establish connections two environmental factors: meteorology, area (AAB). findings revealed improvement Dianchi's quality, with Levels A (good quality) B (mildly polluted averaging 1.24% 84.28%, respectively. Starting October 2022, stabilized at Level B, 98.17%. Seasonal variations demonstrated best spring worst summer (Level C, severely accounting for 5.19% 21.68%, respectively). Algal presence was minimally observed, average AAB value 1.75%, peaking autumn (4.05%) hitting low winter (0.38%). significant identified AAB, notable spatial trend decreasing C north south, featuring lower Southern Waihai compared Central Waihai. Statistical pinpointed total phosphorus (TP) as dominant factor influencing while meteorological factors such wind speed (WS), relative humidity (RH), precipitation (PP) playing secondary roles. Despite fluctuations TP concentration, recent stabilization 0.05 mg/L suggests positive trajectory future management Dianchi.

Язык: Английский

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Application of deep learning for high-throughput phenotyping of seed: a review

Artificial Intelligence Review, Год журнала: 2025, Номер 58(3)

Опубликована: Янв. 6, 2025

Abstract Seed quality is of great importance for agricultural cultivation. High-throughput phenotyping techniques can collect magnificent seed information in a rapid and non-destructive manner. Emerging deep learning technology brings new opportunities effectively processing massive diverse data from seeds evaluating their quality. This article comprehensively reviews the principle several high-throughput non-destructively collection information. In addition, recent research studies on application learning-based approaches inspection are reviewed summarized, including variety classification grading, damage detection, components prediction, cleanliness, vitality assessment, etc. review illustrates that combination be promising tool various phenotype seeds, which used effective evaluation industrial practical applications, such as breeding, management, selection food source.

Язык: Английский

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