Sustainable Agriculture and Advances of Remote Sensing (Volume 1)

© 2022 by the authors; CC BY-NC-ND license

geographic information system (GIS); pocket beaches; coastal management; Interreg; climate change; remote sensing; drone; Sicily; Malta; Gozo; Comino; systematic literature review; anomaly intrusion detection; deep learning; IoT; resource constraint; IDS; evapotranspiration; penman-monteith equation; artificial neural network; canopy conductance; Ziz basin; water quality; satellite image analysis; modeling approach; nitrate; dissolved oxygen; chlorophyll a; climate change; time series analysis; environmental monitoring; water extraction; modified normalized difference water index (MNDWI); remote sensing; machine learning algorithm; hyperspectral; proximal sensing; panicle initiation; normalized difference vegetation index (NDVI); green ring; internode-elongation; Sentinel 1 and 2; Copernicus Sentinels; crop classification; food security; agricultural monitoring; remote sensing; data analysis; SAR; random forest; 3D bale wrapping method; equal bale dimensions; mathematical model; minimal film consumption; optimal bale dimensions; round bales; Sentinel-2; SVM; RF; Boufakrane River watershed; irrigation requirements; water resources; sustainable land use; agriculture; invasive plants; precision agriculture; remote sensing; rice farming; site-specific weed management; nitrogen prediction; 1D convolution neural networks; cucumber; crop yield improvement; mango leaf; CCA; vein pattern; leaf disease; cubic SVM; chlorophyll-a concentration; artificial neural network; transfer learning; overfitting; data augmentation; deep learning; guava disease; plant disease detection; crops diseases; data augmentation; deep learning; entropy; features fusion; machine learning; machine learning; object-based classification; density estimation; histogram; land use; crop fields; soil tillage; data fusion; multispectral; SAR; IoT; sensor; probe; temperature profile; forest roads; simulation; autonomous robots; remote sensing; smart agriculture; climate change; environmental protection; drone; photogrammetry; path planning; internet of things; environmental monitoring; modeling; simulation; precision agriculture; convolutional neural networks; machine vision; computer vision; modular robot; precision agriculture; selective spraying; vision-based crop and weed detection; convolutional neural networks; Faster R-CNN; YOLOv5; band selection; CNN; NDVI; hyperspectral imaging; crops; agriculture; urban flood; Sentinel-1a; Synthetic Aperture Radar (SAR); 3D Convolutional Neural Network; multi-temporal data; remote sensing; land use classification; GIS; Coatzacoalcos; algorithms; clustering; modeling; pest control; precision agriculture; site-specific; virtual pests; rice plant; weed; hyperspectral imagery; remote sensing; sustainable agriculture; food security; green technologies; Internet of Things; natural resources; sustainable environment; IoT ecosystem; hyperspectral remoting sensing; crop mapping; image classification; deep transfer learning; hyperparameter optimization; autonomous robots; remote sensing; smart agriculture; climate change; environmental protection; drone; metaheuristic; Internet of Things; soil attribute; GIS; ordinary Kriging; rational sampling numbers; spatial heterogeneity; sampling; soil pH; spatial variation; ordinary kriging; Land Use/Land Cover; LISS-III; Landsat; Vision Transformer; Bidirectional long-short term memory; Google Earth Engine; Explainable Artificial Intelligence