Article
Biotechnology & Applied Microbiology
Malini Roy Choudhury, Jack Christopher, Sumanta Das, Armando Apan, Neal W. Menzies, Scott Chapman, Vincent Mellor, Yash P. Dang
Summary: Plants grown on sodic soils may suffer from macronutrient deficiencies, which can affect their health and growth. This study proposes a novel approach using hyperspectral sensing to determine macronutrient and chlorophyll variations/deficiencies of different wheat genotypes grown under sodic soil conditions. The results demonstrate that hyperspectral sensing can efficiently detect plant macronutrient and chlorophyll concentrations.
ENVIRONMENTAL TECHNOLOGY & INNOVATION
(2022)
Article
Engineering, Environmental
Yuhui Quan, Weijun Zhou, Tian Wu, Minfeng Chen, Xiang Han, Qinghua Tian, Junling Xu, Jizhang Chen
Summary: In this study, a food-grade sorbitol-modified cellulose hydrogel electrolyte with concentrated ZnCl2 was developed, which exhibited excellent mechanical properties, strong adhesion, high transparency, good moldability, rich porosity, ultralow freezing point, and large ionic conductivity. The modified hydrogel electrolyte enabled high zinc stripping/plating reversibility even under extreme temperatures, resulting in superior electrochemical performances in the assembled flexible Zn-PANI battery.
CHEMICAL ENGINEERING JOURNAL
(2022)
Article
Environmental Sciences
Huiqin Ma, Wenjiang Huang, Yingying Dong, Linyi Liu, Anting Guo
Summary: This study explored the potential of using hyperspectral imagery obtained from an unmanned aerial vehicle to detect wheat FHB by combining different spectral features. The field-scale wheat FHB detection model based on a combination of SBs, VIs, and WFs achieved the highest accuracy among the tested models. Our results suggest that UAV-based hyperspectral imaging technology is promising for the field-scale detection of wheat FHB.
Article
Plant Sciences
Yao Cai, Yuxuan Miao, Hao Wu, Dan Wang
Summary: The study found a close relationship between chlorophyll content of winter wheat and spectral reflectance under elevated CO2 conditions, with better estimation accuracy using sensitive spectral bands and difference vegetation index (DVI). Hyperspectral measurement can effectively estimate chlorophyll content and serve as a useful tool for monitoring plant physiology and growth under both ambient and elevated CO2 conditions.
FRONTIERS IN PLANT SCIENCE
(2021)
Article
Environmental Sciences
Haikuan Feng, Huilin Tao, Yiguang Fan, Yang Liu, Zhenhai Li, Guijun Yang, Chunjiang Zhao
Summary: This study used different vegetation indices and red-edge parameters derived from near-surface and UAV hyperspectral data to estimate the yield of winter wheat at different growth stages using PLSR and ANN regression methods. The results showed that using a combination of vegetation indices and red-edge parameters improved the estimation accuracy of yield, with the PLSR method outperforming the ANN method. Additionally, the near-surface hyperspectral sensors achieved a higher accuracy in yield prediction compared to the UAV hyperspectral remote sensing data.
Article
Environmental Sciences
Qi Wang, Xiaokai Chen, Huayi Meng, Huiling Miao, Shiyu Jiang, Qingrui Chang
Summary: This study utilized machine learning methods to estimate SPAD values of winter wheat, combining vegetation indices and red-edge parameters. Different growth stages were found to have a significant impact on SPAD value estimation, with machine learning methods showing better stability in model estimation.
Article
Computer Science, Artificial Intelligence
Aravind Krishnaswamy Rangarajan, Rebecca Louise Whetton, Abdul Mounem Mouazen
Summary: This study explores the use of deep learning models for automatically extracting features of fusarium head blight (FHB) in wheat. Images generated from hyperspectral data were classified using a convolutional neural network, resulting in high accuracy and F1 scores when using specific image conversion schemes and appropriate pre-trained models.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Agronomy
Yingnan Wei, Han Ru, Xiaolan Leng, Zhijian He, Olusola O. Ayantobo, Tehseen Javed, Ning Yao
Summary: Crop models play an important role in understanding and regulating agroecosystems. However, the CERES-Wheat model has limitations under water stress conditions. This study aimed to improve the model's performance by modifying the crop coefficient based on experimental data from arid and semi-arid regions. The results showed that using the PT equation for calculating reference crop evapotranspiration and crop coefficient had the best performance in simulating biomass and grain yield under water stress conditions.
Article
Agronomy
J. L. Pancorbo, C. Camino, M. Alonso-Ayuso, M. D. Raya-Sereno, I Gonzalez-Fernandez, J. L. Gabriel, P. J. Zarco-Tejada, M. Quemada
Summary: Remote sensing technology is a valuable tool for reducing environmental impact in agriculture by detecting crop nitrogen and water status. This study evaluated the potential of visible and infrared hyperspectral and thermal imaging sensors to assess nitrogen and water status with reduced confounding effects. The combination of spectral and thermal information improved the adjustment of N fertilization and irrigation to crop requirements.
EUROPEAN JOURNAL OF AGRONOMY
(2021)
Article
Green & Sustainable Science & Technology
Xiaoxuan Wang, Guosheng Cai, Xiaoping Lu, Zenan Yang, Xiangjun Zhang, Qinggang Zhang
Summary: Leaf area index (LAI) is a crucial parameter for determining the growth status of winter wheat and its impact on ecological and physical processes. This study proposes a new method using red-edge spectral vegetation index to invert the spectral saturation of winter wheat LAI. The multivariable red-edge spectral vegetation index model effectively delays spectral saturation and improves inversion precision.
Article
Environmental Sciences
Imran Haider Khan, Haiyan Liu, Wei Li, Aizhong Cao, Xue Wang, Hongyan Liu, Tao Cheng, Yongchao Tian, Yan Zhu, Weixing Cao, Xia Yao
Summary: This study introduced an early detection model for crop diseases using hyperspectral images and machine learning, which enhanced the accuracy of early identification of infected leaves by combining VIs and NDTIs features, as well as developed a partial least-squares regression model for estimating disease severity. The results showed promising ability for early disease detection and quantification in crops, with high overall accuracy and coefficient values.
Article
Environmental Sciences
Shahbaz Ahmad, Arvind Chandra Pandey, Amit Kumar, Nikhil Lele
Summary: This study evaluated the biophysical and biochemical spectral responses for carbon stock mapping using AVIRIS-NG data in Sholayar reserve forest, Kerala. The results showed that the red-edge position and narrowband indices could be used to predict and measure vegetation biomass and structure using AVIRIS-NG data.
GEOCARTO INTERNATIONAL
(2022)
Article
Remote Sensing
Israr Majeed, Naveen K. Purushothaman, Poulamee Chakraborty, Niranjan Panigrahi, Hitesh B. Vasava, Bhabani S. Das
Summary: Detailed ground cover information and efficient modelling approaches are needed for estimating soil properties from hyperspectral remote sensing (HRS) data. In this study, soil and crop residue samples were collected from 101 locations in the Western Catchment of Chilika lagoon, India, and nonlinear unmixing and two chemometric models were examined. The results showed that high spatial resolution soil and crop residue parameters can be accurately assessed for large areas with multiple land use and soil cover conditions using these models.
INTERNATIONAL JOURNAL OF REMOTE SENSING
(2023)
Article
Chemistry, Analytical
Yupeng Kang, Qingyan Meng, Miao Liu, Youfeng Zou, Xuemiao Wang
Summary: This study analyzed the red edge features and their impact on crop classification, finding that the red edge 710 band can effectively improve classification accuracy.
Article
Agriculture, Multidisciplinary
Pengfei Wen, Zujiao Shi, Ao Li, Fang Ning, Yuanhong Zhang, Rui Wang, Jun Li
Summary: The study proposed an optimized red-edge absorption area (OREA) index to improve the prediction accuracy of vertically integrated leaf N content within maize canopies. Vertical distributions of N were found regardless of maize growth stages, with higher leaf N density in upper and middle layers. The OREA index showed the highest prediction accuracy compared to other published VIs, providing more effective estimation of leaf N content in different layers.
PRECISION AGRICULTURE
(2021)
