Research on the Application of Remote Sensing Mapping and Deep Learning in Geological Survey DOI

Lizhong Zhao

Proceedings of the 2021 5th International Conference on Electronic Information Technology and Computer Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 310 - 314

Published: Oct. 18, 2024

Language: Английский

Climate change air quality monitoring using sentimental 2 dataset DOI
Mughair Aslam Bhatti, Hao Tang, Uzair Aslam Bhatti

et al.

Elsevier eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 221 - 236

Published: Jan. 1, 2025

Citations

0
Application of geographic information system and remote sensing technology in ecosystem services and biodiversity conservation DOI
Maqsood Ahmed Khaskheli, Mir Muhammad Nizamani,

Umed Ali Laghari

et al.

Elsevier eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 97 - 122

Published: Jan. 1, 2025

Language: Английский

Citations

0
Enhancing winter wheat growth indicator prediction with multi-task learning and multi-source data DOI
Heesang Song,

Tingxuan Zhuang,

Xiu-Jie Li

et al.

European Journal of Agronomy, Journal Year: 2025, Volume and Issue: 168, P. 127629 - 127629

Published: April 8, 2025

Language: Английский

Citations

0
Computer Vision and Transfer Learning for Grading of Egyptian Cotton Fibres DOI Creative Commons

Ahmed Rady,

Oliver J. Fisher,

Aly A. A. El-Banna

et al.

AgriEngineering, Journal Year: 2025, Volume and Issue: 7(5), P. 127 - 127

Published: April 22, 2025

Egyptian cotton fibres have worldwide recognition due to their distinct quality and luxurious textile products known by the “Egyptian Cotton“ label. However, fibre trading in Egypt still depends on human grading of quality, which is resource-intensive faces challenges terms subjectivity expertise requirements. This study investigates colour vision transfer learning classify grade five long (Giza 86, Giza 90, 94) extra-long 87 96) staple cultivars. Five Convolutional Neural networks (CNNs)—AlexNet, GoogleNet, SqueezeNet, VGG16, VGG19—were fine-tuned, optimised, tested independent datasets. The highest classifications were 75.7%, 85.0%, 80.0%, 77.1%, 90.0% for 87, 94, 96, respectively, with F1-Scores ranging from 51.9–100%, 66.7–100%, 42.9–100%, 40.0–100%, 80.0–100%. Among CNNs, AlexNet, VGG19 outperformed others. Fused CNN models further improved classification accuracy up 7.2% all cultivars except 87. These results demonstrate feasibility developing a fast, low-cost, low-skilled system that overcomes inconsistencies limitations manual early stages Egypt.

Language: Английский

Citations

0
Improved Identification of Forest Types in the Loess Plateau Using Multi-Source Remote Sensing Data, Transfer Learning, and Neural Residual Networks DOI Creative Commons
Mei Zhang, Daihao Yin, Zhen Li

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(12), P. 2096 - 2096

Published: June 10, 2024

This study aims to establish a deep learning-based classification framework efficiently and rapidly distinguish between coniferous broadleaf forests across the Loess Plateau. By integrating residual neural network (ResNet) architecture with transfer learning techniques multispectral data from unmanned aerial vehicles (UAVs) Landsat remote sensing data, effectiveness of was validated through well-designed experiments. The began by selecting optimal spectral band combinations, using random forest algorithm. Pre-trained models were then constructed, model performance optimized different training strategies, considering factors such as image size, sample quantity, depth. results indicated substantial improvements in model’s accuracy efficiency for reasonable dimensions sizes, especially an size 3 × pixels 2000 samples. In addition, application fine-tuning strategies greatly enhanced adaptability universality scenarios. fine-tuned achieved remarkable forest-type tasks, increasing 85% 93% Zhengning, 89% 96% Yongshou, 86% 94% Baishui, well exceeding 90% all counties. These not only confirm proposed framework, but also emphasize roles depth improving generalization ability model. conclusion, this research has developed technological effective landscape recognition, combination UAVs satellites. proved be more identifying types than alone, demonstrating capability gained UAV technology. provides valuable scientific guidance tools policymakers practitioners management sustainable development.

Language: Английский

Citations

3
A Comprehensive review on technological breakthroughs in precision agriculture: IoT and emerging data analytics DOI
Anil Kumar Saini, Anshul Kumar Yadav,

Dhiraj Sangwan

et al.

European Journal of Agronomy, Journal Year: 2024, Volume and Issue: 163, P. 127440 - 127440

Published: Nov. 29, 2024

Language: Английский

Citations

3
A laser ultrasonic intelligent inspection method for metal surface defects based on digital twin model DOI

Yunhao Zhang,

Hong Zhou,

Rao Yao

et al.

Measurement, Journal Year: 2024, Volume and Issue: 237, P. 115219 - 115219

Published: July 1, 2024

Language: Английский

Citations

2
Significant impact of the covid-19 pandemic on methane emissions evaluated by comprehensive statistical analysis of satellite data DOI Creative Commons
Beni Adi Trisna,

Seungnam Park,

Jeongsoon Lee

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Sept. 28, 2024

Language: Английский

Citations

2
Spectral data driven machine learning classification models for real time leaf spot disease detection in brinjal crops DOI

Rohit Anand,

Roaf Ahmad Parray, Indra Mani

et al.

European Journal of Agronomy, Journal Year: 2024, Volume and Issue: 161, P. 127384 - 127384

Published: Oct. 10, 2024

Language: Английский

Citations

2
Sustainable Agriculture-Based Climate Change Training Models using Remote Hyperspectral Image with Machine Learning Model DOI

M. Durairaj,

Kasapaka Rubenraju,

B. Krishna

et al.

Remote Sensing in Earth Systems Sciences, Journal Year: 2024, Volume and Issue: 7(4), P. 261 - 270

Published: Aug. 23, 2024

Language: Английский

Citations

1