S2-IFNet: A spatial-semantic information fusion network integrated with boundary feature enhancement for forest land extraction from Sentinel-2 data.

Detecting sun glint in UAV RGB images at different times using a deep learning algorithm.

An improved deep learning approach for detection of maize tassels using UAV-based RGB images.

Comparison of different machine learning algorithms for predicting maize grain yield using UAV-based hyperspectral images.

Integrated UAV-Based Multi-Source Data for Predicting Maize Grain Yield Using Machine Learning Approaches.

Accuracy Evaluation of the Landsat 9 Land Surface Temperature Product.

UAV Multispectral Image-Based Urban River Water Quality Monitoring Using Stacked Ensemble Machine Learning Algorithms - A Case Study of the Zhanghe River, China.

Machine Learning-Based Approaches for Predicting SPAD Values of Maize Using Multi-Spectral Images.

Comparison of Multi-Methods for Identifying Maize Phenology Using PhenoCams.

Validation of the ECOSTRESS Land Surface Temperature Product Using Ground Measurements.

Identifying crop phenology using maize height constructed from multi-sources images.

Integrating Spectral and Textural Information for Monitoring the Growth of Pear Trees Using Optical Images from the UAV Platform.

A Quantitative Study on English Polyfunctional Words.

Integrating spectral and textural information for identifying the tasseling date of summer maize using UAV based RGB images.

Scaling Effects on Chlorophyll Content Estimations with RGB Camera Mounted on a UAV Platform Using Machine-Learning Methods.

Modified Red Blue Vegetation Index for Chlorophyll Estimation and Yield Prediction of Maize from Visible Images Captured by UAV.