4.7 Article

Banana Mapping in Heterogenous Smallholder Farming Systems Using High-Resolution Remote Sensing Imagery and Machine Learning Models with Implications for Banana Bunchy Top Disease Surveillance

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Remote Sensing

Mapping of cropland, cropping patterns and crop types by combining optical remote sensing images with decision tree classifier and random forest

Aqil Tariq et al.

Summary: This study successfully identified and monitored croplands, crop types, and cropping patterns in Gujranwala District, Pakistan, using machine learning and remote sensing techniques with Sentinel-2 and Landsat-8 imagery. The results demonstrated a high level of accuracy when validated at the county level, indicating the potential benefits of this methodology for monitoring and evaluating food security in Pakistan.

GEO-SPATIAL INFORMATION SCIENCE (2023)

Article Environmental Sciences

Crop type identification and spatial mapping using Sentinel-2 satellite data with focus on field-level information

Murali Krishna Gumma et al.

Summary: Accurate monitoring of croplands in drylands is crucial for decision-making and national food production statistics. This study uses remote sensing tools and a spectral matching technique to assess winter crop areas and patterns. The results show that this method can effectively distinguish different crops with high accuracy.

GEOCARTO INTERNATIONAL (2022)

Editorial Material Plant Sciences

First Report of Banana Bunchy Top Virus in Banana and Plantain (Musa spp.) in Tanzania

Mpoki M. Shimwela et al.

PLANT DISEASE (2022)

Article Environmental Sciences

Using Deep Learning and Very-High-Resolution Imagery to Map Smallholder Field Boundaries

Weiye Mei et al.

Summary: This study used satellite imagery and a mask region-based convolutional neural network to map smallholder field boundaries in Northeast India. The results showed that this approach had moderate accuracy and could be generalized to other sites.

REMOTE SENSING (2022)

Article Remote Sensing

Comparison of UAV and SAR performance for Crop type classification using machine learning algorithms: a case study of humid forest ecology experimental research site of West Africa

Ojo Patrick Duke et al.

Summary: Food insecurity is a major challenge in African countries, and accurate agricultural production information is crucial for feeding the growing population. This study utilized high-resolution multispectral UAV data and synthetic aperture radar (SAR) to assess the impact of different data combinations and spatial resolutions on classification accuracy and land cover classification. The findings demonstrate that adding canopy height model (CHM) improves model accuracy, while an increasing spatial resolution leads to a decrease in classification accuracy.

INTERNATIONAL JOURNAL OF REMOTE SENSING (2022)

Article Environmental Sciences

Estimation of soybean grain yield from multispectral high-resolution UAV data with machine learning models in West Africa

Tunrayo R. Alabi et al.

Summary: The study aims to estimate soybean yield rapidly and in high throughput using machine learning models and unmanned aerial vehicles (UAVs). Multispectral images were acquired using a camera aboard a UAV, and crop yield was predicted using machine learning models. The study found that predictions based on texture information slightly outperformed those based on vegetation indices, and the Cubist and RF models performed well in predicting soybean yield.

REMOTE SENSING APPLICATIONS-SOCIETY AND ENVIRONMENT (2022)

Article Computer Science, Artificial Intelligence

Gray level co-occurrence matrix (GLCM) texture based crop classification using low altitude remote sensing platforms

Naveed Iqbal et al.

Summary: The research uses optical images collected by drones for crop classification at different phenological stages. Results show that ML algorithms perform better on GLCM features compared to gray scale images.

PEERJ COMPUTER SCIENCE (2021)

Article Environmental Sciences

Detection of Banana Plants Using Multi-Temporal Multispectral UAV Imagery

Aaron Aeberli et al.

Summary: Unmanned aerial vehicles (UAVs) are increasingly used in agricultural and horticultural crop production for planning and management decisions. UAV-based sensing technologies provide high spatial and temporal resolution data, allowing monitoring of individual plants over time and offering essential information about health, yield, and growth.

REMOTE SENSING (2021)

Article Remote Sensing

An Integrated Spectral-Structural Workflow for Invasive Vegetation Mapping in an Arid Region Using Drones

Arnold Chi Kedia et al.

Summary: Mapping invasive vegetation species in arid regions is crucial for water resource management and understanding ecosystem threats. Utilizing a spectral-structural workflow improved classification accuracy compared to a spectral-only method.

DRONES (2021)

Article Environmental Sciences

Crop Monitoring Using Satellite/UAV Data Fusion and Machine Learning

Maitiniyazi Maimaitijiang et al.

REMOTE SENSING (2020)

Article Green & Sustainable Science & Technology

Mapping Maize Fields by Using Multi-Temporal Sentinel-1A and Sentinel-2A Images in Makarfi, Northern Nigeria, Africa

Ghali Abdullahi Abubakar et al.

SUSTAINABILITY (2020)

Article Agriculture, Multidisciplinary

Extracting apple tree crown information from remote imagery using deep learning

Jintao Wu et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2020)

Review Environmental Sciences

Geographic Object-Based Image Analysis: A Primer and Future Directions

Maja Kucharczyk et al.

REMOTE SENSING (2020)

Article Geography, Physical

Detection of banana plants and their major diseases through aerial images and machine learning methods: A case study in DR Congo and Republic of Benin

Michael Gomez Selvaraj et al.

ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING (2020)

Review Computer Science, Artificial Intelligence

A comparison of random forest variable selection methods for classification prediction modeling

Jaime Lynn Speiser et al.

EXPERT SYSTEMS WITH APPLICATIONS (2019)

Review Geography, Physical

Segmentation for Object-Based Image Analysis (OBIA): A review of algorithms and challenges from remote sensing perspective

Mohammad D. Hossain et al.

ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING (2019)

Article Environmental Sciences

Land Cover Classification from fused DSM and UAV Images Using Convolutional Neural Networks

Husam A. H. Al-Najjar et al.

REMOTE SENSING (2019)

Article Engineering, Electrical & Electronic

Crop Type Identification and Mapping Using Machine Learning Algorithms and Sentinel-2 Time Series Data

Siwen Feng et al.

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (2019)

Article Computer Science, Information Systems

Comparison of variable selection methods for clinical predictive modeling

L. Nelson Sanchez-Pinto et al.

INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS (2018)

Review Remote Sensing

Implementation of machine-learning classification in remote sensing: an applied review

Aaron E. Maxwell et al.

INTERNATIONAL JOURNAL OF REMOTE SENSING (2018)

Article Environmental Sciences

Crop classification from Sentinel-2-derived vegetation indices using ensemble learning

Rei Sonobe et al.

JOURNAL OF APPLIED REMOTE SENSING (2018)

Article Environmental Sciences

Crop Classification in a Heterogeneous Arable Landscape Using Uncalibrated UAV Data

Jonas E. Bohler et al.

REMOTE SENSING (2018)

Article Meteorology & Atmospheric Sciences

WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas

Stephen E. Fick et al.

INTERNATIONAL JOURNAL OF CLIMATOLOGY (2017)

Article Environmental Sciences

Cloud Cover Assessment for Operational Crop Monitoring Systems in Tropical Areas

Isaque Daniel Rocha Eberhardt et al.

REMOTE SENSING (2016)

Article Geography, Physical

An assessment of the effectiveness of a random forest classifier for land-cover classification

V. F. Rodriguez-Galiano et al.

ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING (2012)

Article Environmental Sciences

A comparative analysis of high spatial resolution IKONOS and WorldView-2 imagery for mapping urban tree species

Ruiliang Pu et al.

REMOTE SENSING OF ENVIRONMENT (2012)

Article Computer Science, Interdisciplinary Applications

Feature Selection with the Boruta Package

Miron B. Kursa et al.

JOURNAL OF STATISTICAL SOFTWARE (2010)

Article Ecology

Remote sensing for grassland management in the arid Southwest

Robert C. Marsett et al.

RANGELAND ECOLOGY & MANAGEMENT (2006)

Article Environmental Sciences

Efficiency of crop identification based on optical and SAR image time series

X Blaes et al.

REMOTE SENSING OF ENVIRONMENT (2005)

Article Geosciences, Multidisciplinary

Remote estimation of canopy chlorophyll content in crops -: art. no. L08403

AA Gitelson et al.

GEOPHYSICAL RESEARCH LETTERS (2005)

Article Remote Sensing

Support vector machines for classification in remote sensing

M Pal et al.

INTERNATIONAL JOURNAL OF REMOTE SENSING (2005)

Article Computer Science, Artificial Intelligence

Random forests

L Breiman

MACHINE LEARNING (2001)

Article Environmental Sciences

Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance

CST Daughtry et al.

REMOTE SENSING OF ENVIRONMENT (2000)