Artificial Intelligence in Food Safety: A Decade Review and Bibliometric Analysis

Foods, Journal Year: 2023, Volume and Issue: 12(6), P. 1242 - 1242

Published: March 14, 2023

Artificial Intelligence (AI) technologies have been powerful solutions used to improve food yield, quality, and nutrition, increase safety traceability while decreasing resource consumption, eliminate waste. Compared with several qualitative reviews on AI in safety, we conducted an in-depth quantitative systematic review based the Core Collection database of WoS (Web Science). To discover historical trajectory identify future trends, analysed literature concerning from 2012 2022 by CiteSpace. In this review, bibliometric methods describe development including performance analysis, science mapping, network analysis Among 1855 selected articles, China United States contributed most literature, Chinese Academy Sciences released largest number relevant articles. all journals field, PLoS ONE Computers Electronics Agriculture ranked first second terms annual publications co-citation frequency. The present character, hot spots, research trends were determined. Furthermore, our analyses, provide researchers, practitioners, policymakers big picture across whole process, precision agriculture through 28 enlightening

Language: Английский

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Integrating artificial intelligence into the modernization of traditional Chinese medicine industry: a review

Frontiers in Pharmacology, Journal Year: 2024, Volume and Issue: 15

Published: Feb. 23, 2024

Traditional Chinese medicine (TCM) is the practical experience and summary of nation for thousands years. It shows great potential in treating various chronic diseases, complex diseases major infectious has gradually attracted attention people all over world. However, due to complexity prescription action mechanism TCM, development TCM industry still a relatively conservative stage. With rise artificial intelligence technology fields, many scholars began apply traditional made remarkable progress. This paper comprehensively summarizes important role from aspects, including new drug discovery, data mining, quality standardization medicine. The limitations these applications are also emphasized, lack pharmacological research, database problems challenges brought by human-computer interaction. Nevertheless, opportunities innovations modernization Integrating into comprehensive application expected overcome faced further promote whole industry.

Language: Английский

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Integrating blockchain and deep learning for intelligent greenhouse control and traceability

Alexandria Engineering Journal, Journal Year: 2023, Volume and Issue: 79, P. 259 - 273

Published: Aug. 12, 2023

This research presents a solution that combines deep learning-based image processing, blockchain technology, and the Internet of Things (IoT) to achieve smarter control traceability in greenhouse operations within agricultural sector. By integrating these technologies, aim is overcome challenges posed by climate change, plant growth, limited land, water scarcity, while enhancing crop yields ensuring efficient secure operations. The proposed system automates capture, measurement, storage, monitoring environmental parameters greenhouses, utilizing highly accurate processing techniques with 98% success rate. integration technology establishes an immutable transparent record transactions data points, thereby improving across supply chain. comprehensive approach fosters accountability, transparency, trust, empowering consumers make well-informed decisions regarding products they purchase. Ultimately, this contributes advancing sustainable practices.

Language: Английский

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Agroeconomic Indexes and Big Data: Digital Marketing Analytics Implications for Enhanced Decision Making with Artificial Intelligence-Based Modeling

Information, Journal Year: 2024, Volume and Issue: 15(2), P. 67 - 67

Published: Jan. 23, 2024

Agriculture firms face an array of struggles, most which are financial; thus, the role decision making is discerned as highly important. The agroeconomic indexes (AEIs) Employment Rate (AER), Chemical Product Price Index (CPPI), Farm (FPPI), and Machinery Equipment (MEPI) were selected basis this study. This research aims to examine connection between digital marketing analytics while providing valuable insights into their decision-making process, with utilization AI (artificial intelligence) models. Thus, a dataset website was collected from five well-established agriculture firms, apart values referred indexes. By performing regression correlation analyses, index relationships firms’ extracted used for deployment fuzzy cognitive mapping (FCM) hybrid modeling (HM) processes, assisted by using artificial neural network (ANN) Through above there strong AER, CPPI, FPPR, MEPI metrics branded traffic, social search traffic sources, paid organic costs firms. It highlighted that better understand sector’s employment rate volatility farming, chemicals, machine equipment prices future investment strategies should try increase in preferred applications.

Language: Английский

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A systematic review of current AI techniques used in the context of the SDGs

International Journal of Environmental Research, Journal Year: 2024, Volume and Issue: 19(1)

Published: Oct. 24, 2024

Abstract This study aims to explore the application of artificial intelligence (AI) in resolution sustainability challenges, with a specific focus on environmental studies. Given rapidly evolving nature this field, there is an urgent need for more frequent and dynamic reviews keep pace innovative applications AI. Through systematic analysis 191 research articles, we classified AI techniques applied field sustainability. Our review found that 65% studies supervised learning methods, 18% employed unsupervised learning, 17% utilized reinforcement approaches. The highlights neural networks (ANN), are most commonly contexts, accounting 23% reviewed methods. comprehensive overview identifies key trends proposes new avenues address complex issue achieving Sustainable Development Goals (SDGs). Graphic abstract

Language: Английский

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Leaf Disease Detection Using Fuzzy Edge Detection and Clustering

Lecture notes in networks and systems, Journal Year: 2025, Volume and Issue: unknown, P. 17 - 25

Published: Jan. 1, 2025

Language: Английский

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Adaptive hybrid segmentation combined with meta heuristic optimization in transfer learning for plant leaf disease classification

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 21, 2025

Plant diseases can damage specific parts of leaves for better readability during the farming process. Plants refer to various types crops, including fruits and vegetables. During production phase healthy plant often begin by infecting leaves. Leaves, being exposed, are more vulnerable disease than other parts. When affected disease, crop yield decreases, leading economic loss. Hence, early identification model is required deployed in an automated computerized way. The analysis have shown that multiple approaches were executed detect still it suffers from pitfalls like inadequate feature extraction, handcrafted features, computation burden, complexities so on. To improve process, efficient method developed detecting different learning method. Firstly, leaf data gathered UCI, Kaggle web sources benchmarks. unwanted noise input images pre-processed using median filter. Subsequently, or abnormal region was segmented adaptive hybrid K-means with fuzzy C-means clustering (AHKM-FCM); parameter tuning also done improved random variable-based water strider algorithm (IRV-WSA). Finally, subjected into Transfer Learning Network processed Efficient-net, ResNet Densenet, which fine weight accomplished IRV-WSA. analyzed computed across divergent measurements. Classification output results proposed IRV-WSA-ETLNet include 94.853% accuracy, 94.750% sensitivity, 94.888% specificity, 96.068% F1 Score. Additionally, this system uses less computing time 24.378 ms. Compared previous methods, findings demonstrate classification rates help farmer increase production.

Language: Английский

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Detection of serum interleukin-18 level and neutrophil/lymphocyte ratio in patients with antineutrophil cytoplasmic antibody-associated vasculitis and its clinical significance

Open Life Sciences, Journal Year: 2024, Volume and Issue: 19(1)

Published: Jan. 1, 2024

Anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) is a group of autoimmune diseases. This study aimed to investigate the clinical significance changes in interleukin-18 (IL-18) and neutrophil/lymphocyte ratio (NLR) pathogenesis AAV impact NLR on prognosis patients. The data 52 patients (AAV group) who met conditions hospitalization, 30 with mild mesangial proliferative glomerulonephritis (disease controls), healthy volunteers (normal controls) Nephrology Department Liuzhou People's Hospital from May 2020 August 2022 were selected. A total divided into active phase (>15 points) remission (≤15 based Birmingham activity score (BVAS). Serum IL-18 level was detected by enzyme-linked immunosorbent assay three groups. Pearson product moment correlation analysis performed between serum levels laboratory indicators, receiver operating characteristic (ROC) curve IL-18, C-reactive protein (CRP), erythrocyte sedimentation rate (ESR) levels, creatinine, parathyroid hormone, β2-microglobulin (β2-MG), ESR, CRP, stage significantly higher than those AAV. Moreover, that disease control (

Language: Английский

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Deep learning for plant stress detection: A comprehensive review of technologies, challenges, and future directions

Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 229, P. 109734 - 109734

Published: Dec. 13, 2024

Language: Английский

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ResNeXt-SVM: a novel strawberry appearance quality identification method based on ResNeXt network and support vector machine

Journal of Food Measurement & Characterization, Journal Year: 2023, Volume and Issue: 17(5), P. 4345 - 4356

Published: May 10, 2023

Language: Английский

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