In the USA, 100 000 people go missing every year. Difficulty in the rapid identification of sites of human decomposition complicates the recovery of bodies, especially in forests. We propose that spectral responses in tree and shrub canopies could act as guides to find cadavers using remote sensing platforms for societal benefit.
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http://dx.doi.org/10.1016/j.tplants.2020.07.013 | DOI Listing |
Plants (Basel)
January 2025
Department of Plant and Soil Sciences, University of Pretoria, Hatfield, Pretoria P.O. Box X20, South Africa.
The global rise in temperatures due to climate change has made it difficult even for specialised desert-adapted plant species to survive on sandy desert soils. Two of Namibia's iconic desert-adapted plant species, and the quiver tree , have recently been shown to be under threat because of climate change. In the current study, three ecologically important Namibian milk bushes were evaluated for their climate change response.
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January 2025
United States Department of Agriculture-Agriculture Research Service, Grassland Soil and Water Research Laboratory, Temple, TX 76502, USA.
Efficient and reliable corn ( L.) yield prediction is important for varietal selection by plant breeders and management decision-making by growers. Unlike prior studies that focus mainly on county-level or controlled laboratory-scale areas, this study targets a production-scale area, better representing real-world agricultural conditions and offering more practical relevance for farmers.
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January 2025
Institut de Recherche en Informatique de Toulouse, IRIT UMR5505 CNRS, 31400 Toulouse, France.
This review explores the applications of Convolutional Neural Networks (CNNs) in smart agriculture, highlighting recent advancements across various applications including weed detection, disease detection, crop classification, water management, and yield prediction. Based on a comprehensive analysis of more than 115 recent studies, coupled with a bibliometric study of the broader literature, this paper contextualizes the use of CNNs within Agriculture 5.0, where technological integration optimizes agricultural efficiency.
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January 2025
Department of Environmental Remote Sensing and Geoinformatics, Trier University, Universitätsring 15, 54296 Trier, Germany.
Assessing vines' vigour is essential for vineyard management and automatization of viticulture machines, including shaking adjustments of berry harvesters during grape harvest or leaf pruning applications. To address these problems, based on a standardized growth class assessment, labeled ground truth data of precisely located grapevines were predicted with specifically selected Machine Learning (ML) classifiers (Random Forest Classifier (RFC), Support Vector Machines (SVM)), utilizing multispectral UAV (Unmanned Aerial Vehicle) sensor data. The input features for ML model training comprise spectral, structural, and texture feature types generated from multispectral orthomosaics (spectral features), Digital Terrain and Surface Models (DTM/DSM- structural features), and Gray-Level Co-occurrence Matrix (GLCM) calculations (texture features).
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January 2025
Forest Biometrics and Remote Sensing Laboratory (Silva Lab), School of Forest, Fisheries, and Geomatics Sciences, University of Florida, P.O. Box 110410, Gainesville, FL 32611, USA.
Developing the capacity to monitor species diversity worldwide is of great importance in halting biodiversity loss. To this end, remote sensing plays a unique role. In this study, we evaluate the potential of Global Ecosystem Dynamics Investigation (GEDI) data, combined with conventional satellite optical imagery and climate reanalysis data, to predict in situ alpha diversity (Species richness, Simpson index, and Shannon index) among tree species.
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