Methodologies Used in Remote Sensing Data Analysis and Remote Sensors for Precision Agriculture

• Muut tekijät: Chang, Jiyul (Toimittaja), Fuentes, Sigfredo (Toimittaja)
• Aineistotyyppi: Elektroninen Kirjan osa
• Kieli: englanti
• Julkaistu: Basel MDPI - Multidisciplinary Digital Publishing Institute 2023
• Aiheet: Medicine; vineyard; pesticide application; variable rate application; unmanned aerial vehicle; satellite; nanosatellite; monsoon crops; leaf area index; leaf chlorophyll concentration; crop water content; multispectral; hyperspectral; deep learning; forage dry matter yield; high-throughput phenotyping; Brazilian pasture; nitrogen indicator; nitrogen nutrition diagnosis; optical sensor; spectral index; UAV; wheat lodging; lightweight; digital surface model (DSM); winter wheat; fractional order differential; continuous wavelet transform; optimal subset regression; support vector machine; wheat powdery mildew; machine learning; information fusion; remote sensing monitoring; hyperspectral imaging; dimensionality reduction; LDA; PLS; PCA; RandomForest; ReliefF; XGB; Meloidogyne; Solanum tuberosum; soil salinity sensitive parameter; random forest; optimal retrieval model; remote sensing; high throughput phenotyping; UAV/drone; biomass estimation; oats; wheat; yield prediction; random forests; satellite imagery; Normalized Difference Vegetation Index (NDVI); n/a
• Linkit: DOAB: download the publication; DOAB: description of the publication