Sustainable Agriculture and Advances of Remote Sensing (Volume 2)

• Other Authors: Paraforos, Dimitrios (Editor), Muzirafuti, Anselme (Editor), Randazzo, Giovanni (Editor), Lanza, Stefania (Editor)
• Format: Electronic Book Chapter
• Language: English
• Published: MDPI - Multidisciplinary Digital Publishing Institute 2022
• Subjects: Research & information: general; Geography; 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; time series analysis; environmental monitoring; water extraction; modified normalized difference water index (MNDWI); 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; 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; 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; transfer learning; overfitting; data augmentation; guava disease; plant disease detection; crops diseases; entropy; features fusion; machine learning; object-based classification; density estimation; histogram; land use; crop fields; soil tillage; data fusion; multispectral; sensor; probe; temperature profile; forest roads; simulation; autonomous robots; smart agriculture; environmental protection; photogrammetry; path planning; internet of things; modeling; convolutional neural networks; machine vision; computer vision; modular robot; selective spraying; vision-based crop and weed detection; Faster R-CNN; YOLOv5; band selection; CNN; NDVI; hyperspectral imaging; crops; urban flood; Sentinel-1a; Synthetic Aperture Radar (SAR); 3D Convolutional Neural Network; multi-temporal data; land use classification; GIS; Coatzacoalcos; algorithms; clustering; pest control; site-specific; virtual pests; rice plant; weed; hyperspectral imagery; sustainable agriculture; green technologies; Internet of Things; natural resources; sustainable environment; IoT ecosystem; hyperspectral remoting sensing; crop mapping; image classification; deep transfer learning; hyperparameter optimization; metaheuristic; soil attribute; 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
• Online Access: DOAB: download the publication; DOAB: description of the publication

Agriculture, as the main source of alimentation and the most important economic activity globally, is being affected by the impacts of climate change. To maintain and increase our global food system production, to reduce biodiversity loss and preserve our natural ecosystem, new practices and technologies are required. This book focuses on the latest advances in remote sensing technology and agricultural engineering leading to the sustainable agriculture practices. Earth observation data, in situ and proxy-remote sensing data are the main source of information for monitoring and analyzing agriculture activities. Particular attention is given to earth observation satellites and the Internet of Things for data collection, to multispectral and hyperspectral data analysis using machine learning and deep learning, to WebGIS and the Internet of Things for sharing and publication of the results, among others.