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UAS-Based Scouting for Potato Leafhopper Infestation in Common Bean: Assessing the Influence of Flight Height, Canopy Delineation, and Spectral Index Selection
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Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/bimj.200810425openalex-snapshot
Simultaneous Inference in General Parametric Models
inferredcites →https://doi.org/10.1007/s00374-003-0682-6openalex-snapshot
Benefits of inoculation of the common bean ( Phaseolus vulgaris ) crop with efficient and competitive Rhizobium tropici strains
inferredcites →https://doi.org/10.1007/s11676-020-01155-1openalex-snapshot
A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing
inferredcites →https://doi.org/10.1016/0034-4257(88)90106-xopenalex-snapshot
A soil-adjusted vegetation index (SAVI)
inferredcites →https://doi.org/10.1016/j.aiia.2025.02.007openalex-snapshot
Using UAV-based multispectral images and CGS-YOLO algorithm to distinguish maize seeding from weed
inferredcites →https://doi.org/10.1016/j.atech.2024.100513openalex-snapshot
Garlic yield monitoring using vegetation indices and texture features derived from UAV multispectral imagery
inferredcites →https://doi.org/10.1016/j.fraope.2026.100516openalex-snapshot
Addressing mixed pixel challenges in crop mapping: An integrated remote sensing and machine learning framework
inferredcites →https://doi.org/10.1016/j.isprsjprs.2017.10.011openalex-snapshot
Unmanned Aerial System (UAS)-based phenotyping of soybean using multi-sensor data fusion and extreme learning machine
inferredcites →https://doi.org/10.1016/j.jag.2015.03.017openalex-snapshot
Estimating chlorophyll with thermal and broadband multispectral high resolution imagery from an unmanned aerial system using relevance vector machines for precision agriculture
inferredcites →https://doi.org/10.1016/s0034-4257(02)00096-2openalex-snapshot
Overview of the radiometric and biophysical performance of the MODIS vegetation indices
inferredcites →https://doi.org/10.1016/s0034-4257(96)00072-7openalex-snapshot
Use of a green channel in remote sensing of global vegetation from EOS-MODIS
inferredcites →https://doi.org/10.1093/jee/tov250openalex-snapshot
Soybean Aphid (Hemiptera: Aphididae) Affects Soybean Spectral Reflectance
inferredcites →https://doi.org/10.1093/jee/toz306openalex-snapshot
Detection of Stress Induced by Soybean Aphid (Hemiptera: Aphididae) Using Multispectral Imagery from Unmanned Aerial Vehicles
inferredcites →https://doi.org/10.1109/jsen.2013.2279720openalex-snapshot
A Novel UAV-Based Ultra-Light Weight Spectrometer for Field Spectroscopy
inferredcites →https://doi.org/10.1109/metroagrifor63043.2024.10948870openalex-snapshot
On the Use of Remote Sensing Vegetation Indices for Agricultural Zones' Delineation
inferredcites →https://doi.org/10.1109/mgrs.2025.3603640openalex-snapshot
Hyperspectral Remote Sensing for Monitoring Crop Disease: Applications, challenges, and perspectives
inferredcites →https://doi.org/10.1109/tgrs.2025.3555235openalex-snapshot
Performance of Human Annotators in Object Detection and Segmentation of Remotely Sensed Data
inferredcites →https://doi.org/10.1146/annurev.ento.49.061802.123310openalex-snapshot
MECHANISMS OF HOPPERBURN: An Overview of Insect Taxonomy, Behavior, and Physiology
inferredcites →https://doi.org/10.1603/ipm13014openalex-snapshot
Potato Leafhopper (Hemiptera: Cicadellidae) Ecology and Integrated Pest Management Focused on Alfalfa
inferredcites →https://doi.org/10.2478/agriceng-2023-0004openalex-snapshot
Comparison of NDVI, NDRE, MSAVI and NDSI Indices for Early Diagnosis of Crop Problems
inferredcites →https://doi.org/10.2478/contagri-2024-0019openalex-snapshot
Assessing the Impact of UAV Flight Altitudes on the Accuracy of Multispectral Indices
inferredcites →https://doi.org/10.32614/rj-2017-066openalex-snapshot
glmmTMB Balances Speed and Flexibility Among Packages for Zero-inflated Generalized Linear Mixed Modeling
inferredcites →https://doi.org/10.33545/2618060x.2024.v7.i8sk.1399openalex-snapshot
Use of drones in precision pest management
inferredcites →https://doi.org/10.3389/fpls.2022.1077403openalex-snapshot
Monitoring tar spot disease in corn at different canopy and temporal levels using aerial multispectral imaging and machine learning
inferredcites →https://doi.org/10.3390/agronomy14092061openalex-snapshot
Using Multispectral Data from UAS in Machine Learning to Detect Infestation by Xylotrechus chinensis (Chevrolat) (Coleoptera: Cerambycidae) in Mulberries
inferredcites →https://doi.org/10.3390/rs16173296openalex-snapshot
Sugarcane Mosaic Virus Detection in Maize Using UAS Multispectral Imagery
inferredcites →https://doi.org/10.3390/rs18040659openalex-snapshot
A Systematic Review of Available Multispectral UAV Image Datasets for Precision Agriculture Applications
inferredcites →https://doi.org/10.3390/rs70404026openalex-snapshot
Evaluating Multispectral Images and Vegetation Indices for Precision Farming Applications from UAV Images
inferredcites →https://doi.org/10.4141/cjps10002openalex-snapshot
Review: Breeding common bean for resistance to insect pests and nematodes
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