Intelligent technologies and their transformative role in modern agriculture: A comparative approach DOI Creative Commons
Karishma Behera, Anita Babbar,

R. G. Vyshnavi

et al.

Environment Conservation Journal, Journal Year: 2024, Volume and Issue: 25(3), P. 870 - 880

Published: April 22, 2024

The escalating global demand for food, propelled by a burgeoning population and the unpredictable shifts in climatic conditions, presents challenge that traditional plant breeding alone struggles to address. In response this pressing need, infusion of intelligent technologies emerges as pivotal solution, poised not only boost production but also meet demand. This transformative approach encompasses spectrum cutting-edge tools, including Remote Sensing GIS, Aeroponics, Drone Technology, Biotechnology, Artificial Intelligence, Machine Learning, and, ultimately, Robotics. synergistic integration these will enhance agricultural monitoring facilitating precise crop surveillance, early detection mitigation diseases pests, optimization water resources, accurate mapping land use types, comprehensive environmental monitoring, real-time weather climate tracking, efficient nutrient management, irrigation spraying practices, reliable yield prediction, advanced forecasting, genetic analysis, informed decision-making processes. amalgamation with modern methodologies signifies significant advancement towards achieving more sustainable practices. convergence addresses immediate need increased food sets stage resilient future-ready landscape. era integration, we witness harmonious coexistence tradition innovation, paving way abundant secure future.

Language: Английский

Remote sensing revolutionizing agriculture: Toward a new frontier DOI
Xiaoding Wang, Haitao Zeng, Xu Yang

et al.

Future Generation Computer Systems, Journal Year: 2025, Volume and Issue: 166, P. 107691 - 107691

Published: Jan. 6, 2025

Language: Английский

Citations

3
Developing new sugarcane varieties suitable for mechanized production in China: principles, strategies and prospects DOI Creative Commons
Youxiong Que, Qibin Wu, Hua Zhang

et al.

Frontiers in Plant Science, Journal Year: 2024, Volume and Issue: 14

Published: Jan. 8, 2024

The sugar industry, which relates to people's livelihood, is strategic and fundamental in the development of agricultural economy. In China, derived from sugarcane accounts for approximately 85% total production. Mechanization "flower" industry. As saying goes "when there are blooming flowers, will be sweet honey." However, due limitations land resources, technology, equipment, organization, management, mechanization throughout production process has not yet brought about economic benefits that a mechanized system should provide reached an ideal yield through integration machinery agronomic practice. This paper briefly describes how initiate Chinese promote sound, healthy, rapid ultimately achieve transformation breeding China modernization industry three perspectives, namely, requirements varieties, strategies selecting new varieties suitable production, screening diversification variety distribution or arrangement China. We also highlight current challenges surrounding this topic look forward its bright prospects.

Language: Английский

Citations

11
Fed-MPS: Federated learning with local differential privacy using model parameter selection for resource-constrained CPS DOI
Shui Jiang, Xiaoding Wang, Youxiong Que

et al.

Journal of Systems Architecture, Journal Year: 2024, Volume and Issue: 150, P. 103108 - 103108

Published: March 15, 2024

Language: Английский

Citations

5
Development of Machine Learning Methods for Accurate Prediction of Plant Disease Resistance DOI Creative Commons
Qi Liu, Shimin Zuo, Shasha Peng

et al.

Engineering, Journal Year: 2024, Volume and Issue: 40, P. 100 - 110

Published: April 26, 2024

Language: Английский

Citations

3
AI and the next medical revolution: deep learning's uncharted healthcare promise DOI

L B Krithika,

S. Vishnu,

Evans Kotei

et al.

Engineering Research Express, Journal Year: 2024, Volume and Issue: 6(2), P. 022202 - 022202

Published: June 1, 2024

Abstract Deep learning has shown tremendous potential for transforming healthcare by enabling more accurate diagnoses, improved treatment planning and better patient outcome predictions. In this comprehensive survey, we provide a detailed overview of the state-of-the-art deep techniques their applications across ecosystem. We first introduce fundamentals discuss its key advantages compared to traditional machine approaches. then present an in-depth review major in medical imaging, electronic health record analysis, genomics, robotics other domains. For each application, summarize advancements, outline technical details methods, challenges limitations highlight promising directions future work. examine cross-cutting deploying clinical settings, including interpretability, bias data scarcity. conclude proposing roadmap accelerate translation adoption high-impact learning. Overall, survey provides reference researchers practitioners working at intersection healthcare.

Language: Английский

Citations

3
Harnessing AI-Powered Genomic Research for Sustainable Crop Improvement DOI Creative Commons
Elżbieta Wójcik‐Gront, Bartłomiej Zieniuk, Magdalena Pawełkowicz

et al.

Agriculture, Journal Year: 2024, Volume and Issue: 14(12), P. 2299 - 2299

Published: Dec. 14, 2024

Artificial intelligence (AI) can revolutionize agriculture by enhancing genomic research and promoting sustainable crop improvement. AI systems integrate machine learning (ML) deep (DL) with big data to identify complex patterns relationships analyzing vast genomic, phenotypic, environmental datasets. This capability accelerates breeding cycles, improves predictive accuracy, supports the development of climate-resilient, high-yielding varieties. Applications such as precision agriculture, automated phenotyping, analytics, early pest disease detection demonstrate AI’s ability optimize agricultural practices while sustainability. Despite these advancements, challenges remain, including fragmented sources, variability in phenotyping protocols, ownership concerns. Addressing issues through standardized integration frameworks, advanced analytical tools, ethical will be critical for realizing full potential. review provides a comprehensive overview AI-powered research, highlights role training robust models, explores technological considerations practices.

Language: Английский

Citations

3
Integrative Approaches to Soybean Resilience, Productivity, and Utility: A Review of Genomics, Computational Modeling, and Economic Viability DOI Creative Commons

Yu-Hong Gai,

Shu-Hao Liu,

Zhidan Zhang

et al.

Plants, Journal Year: 2025, Volume and Issue: 14(5), P. 671 - 671

Published: Feb. 21, 2025

Soybean is a vital crop globally and key source of food, feed, biofuel. With advancements in high-throughput technologies, soybeans have become target for genetic improvement. This comprehensive review explores advances multi-omics, artificial intelligence, economic sustainability to enhance soybean resilience productivity. Genomics revolution, including marker-assisted selection (MAS), genomic (GS), genome-wide association studies (GWAS), QTL mapping, GBS, CRISPR-Cas9, metagenomics, metabolomics boosted the growth development by creating stress-resilient varieties. The intelligence (AI) machine learning approaches are improving trait discovery associated with nutritional quality, stresses, adaptation soybeans. Additionally, AI-driven technologies like IoT-based disease detection deep revolutionizing monitoring, early identification, yield prediction, prevention, precision farming. viability environmental soybean-derived biofuels critically evaluated, focusing on trade-offs policy implications. Finally, potential impact climate change productivity explored through predictive modeling adaptive strategies. Thus, this study highlights transformative multidisciplinary advancing global utility.

Language: Английский

Citations

0
Blockchain‐Empowered H‐CPS Architecture for Smart Agriculture DOI Creative Commons
Xiaoding Wang, Qibin Wu, Haitao Zeng

et al.

Advanced Science, Journal Year: 2025, Volume and Issue: unknown

Published: April 25, 2025

Abstract This study integrates blockchain technology into smart agriculture to enhance its productivity and sustainability. By combining with remote sensing, artificial intelligence (AI), the Internet of Things (IoT), a Human‐Cyber‐Physical System (H‐CPS) architecture tailored for agricultural applications is proposed. It supports real‐time crop management, data‐driven decision‐making, transparent trading products. A semantic‐based framework introduced address challenges in data management AI model integration, optimizing production, improving traceability, reducing costs, enhancing financial security. directly addresses real‐world challenges, such as optimized irrigation, improved breeding efficiency, enhanced supply chain transparency. These innovations provide practical solutions modern agriculture, contributing sustainable development global food Further research collaboration are encouraged unlock full potential transforming practices.

Language: Английский

Citations

0
CPVF: vectorization of agricultural cultivation field parcels via a boundary–parcel multi-task learning network in ultra-high-resolution remote sensing images DOI
Xiuyu Liu,

Jinshui Zhang,

Yaming Duan

et al.

ISPRS Journal of Photogrammetry and Remote Sensing, Journal Year: 2025, Volume and Issue: 226, P. 267 - 299

Published: May 26, 2025

Language: Английский

Citations

0
Harnessing Artificial Intelligence and Machine Learning for Identifying Quantitative Trait Loci (QTL) Associated with Seed Quality Traits in Crops DOI Creative Commons
My Abdelmajid Kassem

Plants, Journal Year: 2025, Volume and Issue: 14(11), P. 1727 - 1727

Published: June 5, 2025

Seed quality traits, such as seed size, oil and protein content, mineral accumulation, morphological characteristics, are crucial for enhancing crop productivity, nutritional value, marketability. Traditional quantitative trait loci (QTL) mapping methods, linkage analysis genome-wide association studies (GWAS), have played fundamental role in identifying associated with these complex traits. However, approaches often struggle high-dimensional genomic data, polygenic inheritance, genotype-by-environment (GXE) interactions. Recent advances artificial intelligence (AI) machine learning (ML) provide powerful alternatives that enable more accurate prediction, robust marker-trait associations, efficient feature selection. This review presents an integrated overview of AI/ML applications QTL highlighting key methodologies LASSO regression, Random Forest, Gradient Boosting, ElasticNet, deep techniques including convolutional neural networks (CNNs) graph (GNNs). A case study on soybean nutrients accumulation illustrates the effectiveness ML models significant SNPs chromosomes 8, 9, 14. ElasticNet consistently achieved superior predictive accuracy compared to tree-based models. Beyond soybean, methods enhanced detection wheat, lettuce, rice, cotton, supporting dissection across diverse species. I also explored AI-driven integration multi-omics data—genomics, transcriptomics, metabolomics, phenomics—to improve resolution mapping. While challenges remain terms model interpretability, biological validation, computational scalability, ongoing developments explainable AI, multi-view learning, high-throughput phenotyping offer promising avenues. underscores transformative potential AI accelerating genomic-assisted breeding developing high-quality, climate-resilient varieties.

Language: Английский

Citations

0