Related references
Note: Only part of the references are listed.
Review
Plant Sciences
Andrew J. Simkin et al.
Summary: Photosynthetic pigments, including chlorophyll, carotenoids, and phycobilins, are essential for efficient light absorption and adaptation to different environments in photosynthetic organisms. They play crucial roles in light harvesting, photoprotection, and deep water colonization.
PHOTOSYNTHESIS RESEARCH
(2022)
Article
Plant Sciences
Etsushi Kumagai et al.
Summary: Hyperspectral reflectance can accurately measure the maximum rate of photosynthesis and maximum electron transport in soybean leaves, providing insights into the biochemical acclimation process to high temperatures.
PLANT CELL AND ENVIRONMENT
(2022)
Article
Environmental Sciences
Xuewei Zhang et al.
Summary: This study investigates the effects of combining spectral and texture features extracted from unmanned aerial systems (UAS) multispectral imagery on maize leaf area index (LAI) estimation. The results show that combining spectral and texture features improves the accuracy of maize LAI estimation.
Article
Agronomy
Abeya Temesgen Tefera et al.
Summary: The study examined the effectiveness of different vegetation indices for breeding line selection and found a strong correlation between NDVI readings from aerial and ground-based sensors. NDVI was related to pea genotype rankings and early growth biomass production, with a positive correlation to seed yield in water limiting environments. High vigour scores were associated with increased seed yield in drier environments, but lower yields in better conditions.
FIELD CROPS RESEARCH
(2022)
Article
Plant Sciences
Anatoly Gitelson et al.
Summary: Non-invasive comparative analysis was conducted on the spectral composition of energy absorbed by crop species at leaf and plant levels, revealing similar spectral absorption patterns across different crops and confirming the role of green and far-red light in photosynthesis. The use of leaf and plant absorption coefficients shows the potential for quantifying photosynthesis in different spectral ranges.
PHOTOSYNTHESIS RESEARCH
(2022)
Article
Biochemical Research Methods
Jinnuo Zhang et al.
Summary: This study utilized visible/near-infrared hyperspectral images and deep learning techniques to assess disease severity and extract spectral features, providing valuable information for rapid screening of disease resistant cultivars in rice bacterial blight research.
Editorial Material
Plant Sciences
Viet D. Nguyen et al.
TRENDS IN PLANT SCIENCE
(2022)
Article
Horticulture
Chi Cuong Doan et al.
Summary: This study established mathematical models through multiple linear regression analysis to describe the relationship between tomato cluster's growth indices and environmental factors, finding that cumulative solar radiation and vapor pressure deficit had a significant impact on plant and fruit numbers, respectively. The results demonstrated the potential of using cumulative environmental factors to predict the growth indices of tomato clusters.
SCIENTIA HORTICULTURAE
(2022)
Article
Environmental Sciences
Yahui Guo et al.
Summary: This study used multi-spectral images and machine learning methods to retrieve the growth condition and nutritional status of maize, establishing models to relate spectral and textural indices with SPAD values, and improving the estimation accuracy.
Article
Environmental Sciences
Andreia Valentina Miclea et al.
Summary: Obtaining accurate classification results for hyperspectral images requires high-quality data and carefully selected samples and descriptors for training and testing. This study proposes a machine learning framework for hyperspectral image classification, which includes denoising and enhancement techniques, as well as a parallel approach for feature extraction. The proposed approach combines spectral and spatial features using a Support Vector Machine classifier. Experimental results on three public datasets demonstrate the effectiveness of the proposed approach, especially in terms of avoiding biased classification results caused by overlapping between training and testing datasets.
Review
Plant Sciences
Shan Kothari et al.
Summary: Spectroscopy has been widely used in plant ecology to infer plant traits and processes at individual or community levels. However, understanding the phenotypic information contained in spectra can be challenging due to the complexity of structural and chemical factors. Different approaches, such as radiative transfer and empirical models, have been used to estimate plant traits from spectra, each with their strengths and limitations. In addition, treating spectral data as analogous to trait space can provide valuable insights into the processes structuring plant communities, although it may be more difficult to interpret specific biological processes.
JOURNAL OF ECOLOGY
(2022)
Article
Mathematics
Luis Alberto Cantera-Cantera et al.
Summary: This article introduces the classical curve-fitting problem and the related methods of least squares, total least squares, and orthogonal distances. The research shows that TLS and OD methods yield the same estimates when fitting a first-degree polynomial without an independent coefficient.
Article
Environmental Sciences
Eileen Perry et al.
Summary: Remote sensing from optical radiometers in space provides a nondestructive approach for estimating above ground biomass (AGB). However, challenges such as cloud cover, soil background differences, and crop phenology need to be addressed. In this study, a framework based on Sentinel-2 imagery is presented for estimating AGB using adjusted summed NDVI measurements. The results show high accuracy and reliability, with R-2 values ranging from 0.79 to 0.98. Additionally, the study demonstrates the use of active optical and additional satellite imagery to fill gaps caused by clouds in the Sentinel-2 imagery.
Article
Agronomy
Zhenfeng Yang et al.
Summary: Photosynthesis is a critical indicator for predicting crop yield and quality, and accurately monitoring its dynamics is of great importance in field management. This study developed a Bayesian neural network model to predict photosynthetic performance parameters in grape leaves by quantifying spectral response indices of photosynthetic pigments and water status. The results showed that the developed model had better predictive performance compared to other models, and it could simplify the complex photosynthetic reaction process and provide a rapid and accurate method for monitoring photosynthetic performance.
EUROPEAN JOURNAL OF AGRONOMY
(2022)
Review
Agriculture, Multidisciplinary
Lei Feng et al.
Summary: This review focuses on the applications of UAVs in high-throughput phenotyping of different plant traits using different phenotyping sensors. The review briefly introduces UAV platforms and phenotyping sensors, summarizes applications of UAVs for obtaining and analyzing plant phenotype traits, compares different phenotyping sensors, and discusses challenges and future prospects of using UAVs for remote sensing in phenotyping studies.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2021)
Article
Plant Sciences
Stien Mertens et al.
Summary: Hyperspectral imaging is a useful tool for non-destructive phenotyping of plant physiological traits, especially for studying responses to drought, development, and day-night cycles. With better understanding of spectral measurements, it has been demonstrated to accurately predict physiological changes in plants under different conditions using advanced processing techniques such as partial least squares regression.
FRONTIERS IN PLANT SCIENCE
(2021)
Article
Environmental Sciences
Dongdong Ma et al.
Summary: This study examined diurnal variations in remote sensing data in crop phenotyping, and improved prediction accuracy of plant features through modeling.
Article
Environmental Sciences
Dongdong Ma et al.
Summary: This study proposed a modeling method to understand and model the environmental influences on hyperspectral imaging data by constructing a fixed hyperspectral imaging gantry at Purdue University's research farm. The results showed that an artificial neural network (ANN) model accurately predicted the environmental effects in remote sensing results and effectively eliminated the environment-induced variation in the phenotyping features. The variance in NDVI was reduced by 79% and similar performance was confirmed with the relative water content (RWC) predictions.
Article
Remote Sensing
Milton Valencia-Ortiz et al.
Summary: The study evaluated the variation in standard vegetation indices and their relationship with ground-reference data across two different latitudes using UAV-based multispectral images. The simple ratio index (SR) showed less variability across solar zenith angles in both latitude zones, while other vegetation indices were affected by SZA. The correlation between vegetation indices and ground-reference data remained stable across different solar zenith angles in both latitude zones.
Article
Plant Sciences
Jingfeng Xiao et al.
Summary: New satellite observations have the potential to study how plant functioning and ecosystem processes vary over the course of the diurnal cycle. These observations help characterize and understand variations in ecosystem productivity, water use efficiency, and other processes in response to temperature and water stresses, as well as management practices.
Article
Agriculture, Multidisciplinary
Angel Maresma et al.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2020)
Article
Environmental Sciences
Xuanlong Ma et al.
Review
Agriculture, Multidisciplinary
Chuanqi Xie et al.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2020)
Article
Plant Sciences
Feilong Wang et al.
FRONTIERS IN PLANT SCIENCE
(2019)
Article
Agriculture, Multidisciplinary
Tetsuro Ishida et al.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2018)
Article
Environmental Sciences
Behnood Rasti et al.
Article
Ecology
David Garcia-Callejas et al.
ECOLOGICAL MODELLING
(2016)
Article
Agronomy
Luciane F. Oliveira et al.
Article
Geochemistry & Geophysics
Xuefeng Liu et al.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2012)
Article
Agricultural Engineering
D. Moshou et al.
BIOSYSTEMS ENGINEERING
(2011)
Article
Environmental Sciences
Stephane Jacquemoud et al.
REMOTE SENSING OF ENVIRONMENT
(2009)