Study on the Method of Vineyard Information Extraction Based on Spectral and Texture Features of GF-6 Satellite Imagery DOI Creative Commons

Xuemei Han,

Huichun Ye, Yue Zhang

et al.

Agronomy, Journal Year: 2024, Volume and Issue: 14(11), P. 2542 - 2542

Published: Oct. 28, 2024

Accurately identifying the distribution of vineyard cultivation is great significance for development grape industry and optimization planting structures. Traditional remote sensing techniques identification primarily depend on machine learning algorithms based spectral features. However, reflectance similarities between grapevines other orchard vegetation lead to persistent misclassification omission errors across various algorithms. As a perennial vine plant, grapes are cultivated using trellis systems, displaying regular row spacing distinctive strip-like texture patterns in high-resolution satellite imagery. This study selected main oasis area Turpan City Xinjiang, China, as research area. First, this extracted both features GF-6 imagery, subsequently employing Boruta algorithm discern relative these Then, constructed information extraction models by integrating features, including Naive Bayes (NB), Support Vector Machines (SVMs), Random Forests (RFs). The efficacy extracting was evaluated compared. results indicate that three five under 7 × window have significant sensitivity recognition. These include Normalized Difference Vegetation Index (NDVI), Enhanced (EVI), Water (NDWI), while contrast statistics near-infrared band (B4_CO) variance statistic, heterogeneity correlation statistic derived from NDVI images (NDVI_VA, NDVI_CO, NDVI_DI, NDVI_COR). RF significantly outperforms NB SVM information, boasting an impressive accuracy 93.89% Kappa coefficient 0.89. marks 12.25% increase 0.11 increment over model, well 8.02% enhancement 0.06 rise compared model. Moreover, which amalgamates exhibits notable 13.59% versus spectral-only model 14.92% improvement texture-only underscores harnessing textural attributes imagery precise data, offering valuable theoretical methodological insights future retrieval efforts.

Language: Английский

A Review on Advancing Agricultural Efficiency through Geographic Information Systems, Remote Sensing, and Automated Systems DOI Creative Commons
Mrutyunjay Padhiary, Payaswini Saikia, Pankaj Roy

et al.

Cureus Journal of Engineering., Journal Year: 2025, Volume and Issue: unknown

Published: March 6, 2025

Language: Английский

Citations

2
Strategies for Spatial Data Management in Cloud Environments DOI

B. N. Das,

Munir Ahmad, Maida Maqsood

et al.

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 181 - 204

Published: Jan. 17, 2025

Cloud platforms can enhance spatial data management with specialized services like databases, geocoding, and geospatial analytics. Databases such as Amazon Redshift PostGIS, Microsoft Azure's Cosmos DB, Google Spanner offer efficient storage, retrieval, analysis. Geocoding convert addresses into geographic coordinates, including Google's API, OpenStreetMap Nominatim, Mapbox's API. Geospatial analytics tools from Amazon, Azure, Earth Engine provide actionable insights data. Optimization techniques indexing, partitioning, caching, parallel processing (MapReduce Apache Spark) access processing. Security measures include control, encryption, anonymization to protect sensitive information. Disaster recovery backup strategies ensure resilience business continuity. Utilizing these cloud transform management, unlocking its potential for analysis, visualization, decision-making.

Language: Английский

Citations

0
Technological Innovations Aimed at Reducing the Environmental Impact of Pesticides and Increasing the Resilience of Agriculture to Climate Change DOI

Barbara Sawicka,

Piotr Barbaś, Piotr Pszczółkowski

et al.

Published: Jan. 1, 2025

Language: Английский

Citations

0
Integrating GIS and Remote Sensing for Soil Attributes Mapping in Degraded Pastures of the Brazilian Cerrado DOI Creative Commons
Rômullo Oliveira Louzada, Ivan Bergier, Édson Luís Bolfe

et al.

Soil Advances, Journal Year: 2025, Volume and Issue: unknown, P. 100044 - 100044

Published: March 1, 2025

Language: Английский

Citations

0
Enhancing Precision Farming Innovations for Global Food Security Through Agricultural Extension Services DOI
Isah Shehu Nabara, Norsida Man

Advances in environmental engineering and green technologies book series, Journal Year: 2025, Volume and Issue: unknown, P. 119 - 142

Published: April 4, 2025

Precision farming depends on agricultural extension because it provides farmers with the knowledge, skills, and support they need to adopt successfully use precision agriculture technologies. By offering guidance practical aspects of agriculture, services assist in overcoming technical challenges optimizing their technology. Additionally, facilitate farmers' access technologies, such as software, tools, which could otherwise be unaffordable individual farmers. Agricultural can help overcome barriers adopting boost productivity efficiency, sustainable development by carrying out these duties. technology is essential for assuring effective ethical food production this era global security. As develops, its incorporation into methods holds potential transform sector satisfying expanding needs a dynamic community..

Language: Английский

Citations

0
GIS, Remote Sensing, and Forecasting Systems for Precision Agriculture Development DOI
Vincenzo Barrile, Emanuela Genovese

Lecture notes in computer science, Journal Year: 2024, Volume and Issue: unknown, P. 302 - 318

Published: Jan. 1, 2024

Language: Английский

Citations

2
Terrain Analysis of Elements Using LISS-IV Satellite Image in Bhainsa Region, Northwestern Part of Nirmal District, Telangana State, India DOI Open Access

T. Priyanka,

B. Veeraiah,

Linga Swamy Jogu

et al.

Asian Journal of Geographical Research, Journal Year: 2024, Volume and Issue: 7(2), P. 107 - 122

Published: June 20, 2024

Terrain is considered one of the most important natural geographic features and a vital factor in physical processes. This study focuses attention on terrain analysis area. The effect this surface characteristics were analyzed, was achieved by generating extracting data high-resolution 5.8m satellite image (IRS P6-LISS IV) area respectively. Remote sensing information system (GIS) are used defined as nature, like drainage, digital elevation model (DEM), land use/ cover, lithology, geomorphology features, soil around Bhainsa region, northwestern part Nirmal district. drainage pattern dendritic to sub-dendritic topography region undulating with gentle slope towards southeast. morphological composition forms, result which form or component region. diverse use categories such forest, agriculture, water bodies, cover divided into agriculture land, barren built up, mining industrial, scrub bodies. major litho-units occupied granitic deccan traps basalt. soils covered black clayey, reddish brown, gravelly clay red soils. IRS IV, 2016 made optimum utilization for interpretation analysis. parameters further input analyze locality.

Language: Английский

Citations

1
Delineating Homogeneous Management Zones for Nutrient Management in Rice Cultivated Area of Eastern India DOI
Rahul Tripathi, Bismay Ranjan Tripathy,

A. Gouda

et al.

Journal of soil science and plant nutrition, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 6, 2024

Language: Английский

Citations

1
GEOTrat Points: Free resource in QGIS software for mapping the performance of agricultural experiments DOI

Laura Xavier,

G. B. Martins,

Guilherme de Oliveira

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 7, 2024

Abstract Agricultural experimentation requires careful selection of the experimental design and model for analyzing treatment data. However, even with rigorous control, discrepancies between treatments are so subtle that traditional statistical models fail to highlight statistically significant differences occur in field practice. The incorporation geotechnologies offers ability map agricultural variability, but a gap still exists availability tools designed evaluate effectiveness experiments. To overcome this limitation promote wider application Geographic Information Systems (GIS) agriculture, scope study focuses on development resource QGIS software, aimed at evaluating experiments using randomized block up five treatments. developed incorporates spatial interpolation techniques geostatistical kriging, generation, statistics. used yield samples from six different crops identify quantitative two-treatment terms gain. results consisted two surfaces representing area treated each (T1 T2), as well surface reflecting gain reference relation control treatment, accompanied by relevant descriptive statistics measures surface. simulated cartographic representations treatments, maps illustrating gain, revealed both numerical distinctions an accuracy 95.40%. tool, called GEOTrat - Points, flexibility various designs, encompassing quantities samples, providing analysis. This tool is experimentation, helping select appropriate management practices most effective

Language: Английский

Citations

0
Study on the Method of Vineyard Information Extraction Based on Spectral and Texture Features of GF-6 Satellite Imagery DOI Creative Commons

Xuemei Han,

Huichun Ye, Yue Zhang

et al.

Agronomy, Journal Year: 2024, Volume and Issue: 14(11), P. 2542 - 2542

Published: Oct. 28, 2024

Accurately identifying the distribution of vineyard cultivation is great significance for development grape industry and optimization planting structures. Traditional remote sensing techniques identification primarily depend on machine learning algorithms based spectral features. However, reflectance similarities between grapevines other orchard vegetation lead to persistent misclassification omission errors across various algorithms. As a perennial vine plant, grapes are cultivated using trellis systems, displaying regular row spacing distinctive strip-like texture patterns in high-resolution satellite imagery. This study selected main oasis area Turpan City Xinjiang, China, as research area. First, this extracted both features GF-6 imagery, subsequently employing Boruta algorithm discern relative these Then, constructed information extraction models by integrating features, including Naive Bayes (NB), Support Vector Machines (SVMs), Random Forests (RFs). The efficacy extracting was evaluated compared. results indicate that three five under 7 × window have significant sensitivity recognition. These include Normalized Difference Vegetation Index (NDVI), Enhanced (EVI), Water (NDWI), while contrast statistics near-infrared band (B4_CO) variance statistic, heterogeneity correlation statistic derived from NDVI images (NDVI_VA, NDVI_CO, NDVI_DI, NDVI_COR). RF significantly outperforms NB SVM information, boasting an impressive accuracy 93.89% Kappa coefficient 0.89. marks 12.25% increase 0.11 increment over model, well 8.02% enhancement 0.06 rise compared model. Moreover, which amalgamates exhibits notable 13.59% versus spectral-only model 14.92% improvement texture-only underscores harnessing textural attributes imagery precise data, offering valuable theoretical methodological insights future retrieval efforts.

Language: Английский

Citations

0