Results 101 to 110 of about 12,889,375 (285)

Assessing plant water status: Part 1 – Classical methods

Journal of the Science of Food and Agriculture, EarlyView.
Abstract As a result of the changing climate, water scarcity poses a significant threat to crop and pasture production. Although soil water content can indicate drought, its measurements often provide limited spatial resolution and are weakly correlated with plant water status, producing misleading drought assessments.
Naila Farooq, Tasawer Abbas, Bo Zhang, Bastian Leander Franzisky, Jochen Taiber, Martin Koch, Christoph‐Martin Geilfus, Ian C Dodd   +7 more
wiley   +1 more source

Assessing plant water status: Part 2 – Non‐destructive and remote sensing approaches

Journal of the Science of Food and Agriculture, EarlyView.
Abstract Precise, real time and non‐destructive assessment of plant water status is important for advancing plant physiological understanding, optimizing water usage, improving crop resilience and supporting precision agriculture in the face of increasingly variable climatic conditions.
Naila Farooq, Bo Zhang, Bastian Leander Franzisky, Tasawer Abbas, Jochen Taiber, Martin Koch, Ian C Dodd, Christoph‐Martin Geilfus   +7 more
wiley   +1 more source

Hyperspectral image unmixing using a multiresolution sticky HDP [PDF]

, 2012
This paper is concerned with joint Bayesian endmember extraction and linear unmixing of hyperspectral images using a spatial prior on the abundance vectors.We propose a generative model for hyperspectral images in which the abundances are sampled from a ...
Hero, Alfred O., Dobigeon, Nicolas, Hero, Alfred, Mittelman, Roni   +3 more
core   +1 more source

Image‐based and biochemical multimodal phenotyping for explainable classification of chia (Salvia hispanica L.) genotypes

Journal of the Science of Food and Agriculture, EarlyView.
Abstract BACKGROUND This study developed an explainable machine learning framework integrating morphological, color, and biochemical characteristics for classifying chia (Salvia hispanica L.) genotypes. A dataset was assembled from 1200 seed images spanning four genotypes, from which 17 morphological and color features were extracted.
Sevim Akcura, Irem Poyraz, Beyza Ciftci, Mahmut Kaplan   +3 more
wiley   +1 more source

Land use/land cover (LULC) classification using hyperspectral images: a review

Geo-spatial Information Science
In the rapidly evolving realm of remote sensing technology, the classification of Hyperspectral Images (HSIs) is a pivotal yet formidable task. Hindered by inherent limitations in hyperspectral imaging, enhancing the accuracy and efficiency of HSI ...
Chen Lou, Mohammed A. A. Al-qaness, Dalal AL-Alimi, Abdelghani Dahou, Mohamed Abd Elaziz, Laith Abualigah, Ahmed A. Ewees   +6 more
doaj   +1 more source

Artificial Intelligence in Dermatology: Current Applications and Future Directions

JEADV Clinical Practice, EarlyView.
This scoping review of 56 studies maps AI applications in dermatology. Image‐based classification for skin cancer detection dominates (48%), followed by clinical decision support (21%), teledermatology triage (11%), and predictive analytics (11%). While deep learning algorithms demonstrate diagnostic performance comparable to clinicians in controlled ...
Sofía Pérez‐Lalinde, Francisco Flores   +1 more
wiley   +1 more source

Hyperspectral Image Resolution Enhancement Based on Spectral Unmixing and Information Fusion [PDF]

, 2011
Hyperspectral imaging sensors exibit high spectral resolution, but normally low spatial resolution. This leads to spectral signatures of pixels originating from different object types. Such pixels are called mixed pixels.
Avbelj, Janja, Reinartz, Peter, Müller, Rupert, Cerra, Daniele, Bieniarz, Jakub   +4 more
core  

Optimizing Soil Organic Matter Estimation Through Multi‐Factor Zoning and Tree‐Based Automated Learning

Land Degradation &Development, EarlyView.
ABSTRACT Soil organic matter (SOM) is a key indicator of land degradation and soil functioning. It plays an essential role in nutrient cycling, soil structure, and long‐term agroecosystem resilience. The variations of surface cover, soil moisture and soil texture heterogeneity will affect the accuracy of SOM remote sensing mapping. This study developed
Xuzhou Qu, Meiyan Shu, Huiming Song, Xiaohe Gu, Jingping Zhou, Qian Sun, Baoyuan Zhang, Menglei Dai, Chunjiang Zhao   +8 more
wiley   +1 more source

Nonlinear unmixing of hyperspectral images: Models and algorithms [PDF]

, 2013
When considering the problem of unmixing hyperspectral images, most of the literature in the geoscience and image processing areas relies on the widely used linear mixing model (LMM).
McLaughlin, Stephen; id_orcid, Hero, Alfred O, Jean-yves Tourneret, Cédric Richard, Hero, Alfred O., Dobigeon, Nicolas, McLaughlin, Steve, Bermudez, José Carlos M., Alfred O. Hero, Stephen Mclaughlin, Bermudez, José Carlos Moreira, Nicolas Dobigeon, Richard, Cédric, Tourneret, Jean-Yves, José Carlos M. Bermudez, Bermudez, José Carlos M   +14 more
core   +1 more source

A novel approach to estimate non‐algal particle absorption for improved retrieval of pigment concentrations in coastal waters

Limnology and Oceanography: Methods, EarlyView.
Abstract Phytoplankton play a central role in ocean biogeochemistry and understanding their composition and variability is critical for monitoring marine ecosystem dynamics. Continuous spectrophotometric measurements (using a Seabird Sci. AC‐S) along ship tracks enable high‐resolution measurements of particle spectral absorption (apλ$$ {a}_p\left ...
Margherita Costanzo, Vittorio E. Brando, Emmanuel Boss, Alison Chase, David Doxaran   +4 more
wiley   +1 more source